Pattern recognition Books

Book SynopsisFoundations.- to Music Transcription.- An Introduction to Statistical Signal Processing and Spectrum Estimation.- Sparse Adaptive Representations for Musical Signals.- Rhythm and Timbre Analysis.- Beat Tracking and Musical Metre Analysis.- Unpitched Percussion Transcription.- Automatic Classification of Pitched Musical Instrument Sounds.- Multiple Fundamental Frequency Analysis.- Multiple Fundamental Frequency Estimation Based on Generative Models.- Auditory Model-Based Methods for Multiple Fundamental Frequency Estimation.- Unsupervised Learning Methods for Source Separation in Monaural Music Signals.- Entire Systems, Acoustic and Musicological Modelling.- Auditory Scene Analysis in Music Signals.- Music Scene Description.- Singing Transcription.Table of ContentsFoundations.- to Music Transcription.- An Introduction to Statistical Signal Processing and Spectrum Estimation.- Sparse Adaptive Representations for Musical Signals.- Rhythm and Timbre Analysis.- Beat Tracking and Musical Metre Analysis.- Unpitched Percussion Transcription.- Automatic Classification of Pitched Musical Instrument Sounds.- Multiple Fundamental Frequency Analysis.- Multiple Fundamental Frequency Estimation Based on Generative Models.- Auditory Model-Based Methods for Multiple Fundamental Frequency Estimation.- Unsupervised Learning Methods for Source Separation in Monaural Music Signals.- Entire Systems, Acoustic and Musicological Modelling.- Auditory Scene Analysis in Music Signals.- Music Scene Description.- Singing Transcription.

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Book SynopsisTagline: Teaching your computer to seeTable of Contents1. Getting Started with Processing and OpenCV2. Image Sources and Representations3. Pixel-Based Manipulation4. Geometry and Transformation5. Identification of Structure6. Understanding Motion7. Feature Detection and Matching8. Application Deployment and Conclusion

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Book SynopsisTurn futuristic ideas about computer vision and machine learning into demonstrations that are both functional and entertainingKey Features Build OpenCV 4 apps with Python 2 and 3 on desktops and Raspberry Pi, Java on Android, and C# in Unity Detect, classify, recognize, and measure real-world objects in real-time Work with images from diverse sources, including the web, research datasets, and various cameras Book DescriptionOpenCV 4 is a collection of image processing functions and computer vision algorithms. It is open source, supports many programming languages and platforms, and is fast enough for many real-time applications. With this handy library, you’ll be able to build a variety of impressive gadgets.OpenCV 4 for Secret Agents features a broad selection of projects based on computer vision, machine learning, and several application frameworks. To enable you to build apps for diverse desktop systems and Raspberry Pi, the book supports multiple Python versions, from 2.7 to 3.7. For Android app development, the book also supports Java in Android Studio, and C# in the Unity game engine. Taking inspiration from the world of James Bond, this book will add a touch of adventure and computer vision to your daily routine. You’ll be able to protect your home and car with intelligent camera systems that analyze obstacles, people, and even cats. In addition to this, you’ll also learn how to train a search engine to praise or criticize the images that it finds, and build a mobile app that speaks to you and responds to your body language.By the end of this book, you will be equipped with the knowledge you need to advance your skills as an app developer and a computer vision specialist.What you will learn Detect motion and recognize gestures to control a smartphone game Detect car headlights and estimate their distance Detect and recognize human and cat faces to trigger an alarm Amplify motion in a real-time video to show heartbeats and breaths Make a physics simulation that detects shapes in a real-world drawing Build OpenCV 4 projects in Python 3 for desktops and Raspberry Pi Develop OpenCV 4 Android applications in Android Studio and Unity Who this book is forIf you are an experienced software developer who is new to computer vision or machine learning, and wants to study these topics through creative projects, then this book is for you. The book will also help existing OpenCV users who want upgrade their projects to OpenCV 4 and new versions of other libraries, languages, tools, and operating systems. General familiarity with object-oriented programming, application development, and usage of operating systems (OS), developer tools, and the command line is required.Table of ContentsTable of Contents Preparing for the Mission Searching for Luxury Accommodations Worldwide Training a Smart Alarm to Recognize the Villain and His Cat Controlling a Phone App with Your Suave Gestures Equipping Your Car with a Rearview Camera and Hazard Detection Creating a Physics Simulation Based on a Pen and Paper Sketch Seeing a Heartbeat with a Motion-Amplifying Camera Stopping Time and Seeing like a Bee

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Book SynopsisMedical imaging is an important topic and plays a key role in robust diagnosis and patient care. It has experienced an explosive growth over the last few years due to imaging modalities such as X-rays, computed tomography (CT), magnetic resonance (MR) imaging, and ultrasound. This book focuses primarily on model-based segmentation techniques, which are applied to cardiac, brain, breast and microscopic cancer cell imaging. It includes contributions from authors working in industry and academia, and presents new material.Table of Contents1. Principles of Image Generation.- 1.1 Introduction.- 1.2 Ultrasound Image Generation.- 1.2.1 The Principle of Pulse-Echo Ultrasound Imaging.- 1.2.2 B-Scan Quality and the Ultimate Limits.- 1.2.3 Propagation-Related Artifacts and Resolution Limits.- 1.2.3 Attenuation-Related Artifacts.- 1.3 X-Ray Cardiac Image Generation.- 1.3.1 LV Data Acquisition System Using X-Rays.- 1.3.2 Drawbacks of Cardiac Catheterization.- 1.4 Magnetic Resonance Image Generation.- 1.4.1 Physical Principles of Nuclear Magnetic Resonance.- 1.4.2 Basics of Magnetic Resonance Imaging.- 1.4.3 Gradient-Echo (GRE).- 1.4.4 The Latest Techniques for MR Image Generation.- 1.4.5 3-D Turbo FLASH (MP-RAGE) Technique.- 1.4.6 Non-Rectilinear k-Space Trajectory: Spiral.- 1.4.7 Fat Suppression.- 1.4.8 High Speed MRI: Perfusion-Weighted.- 1.4.9 Time of Flight (TOF) MR Angiography.- 1.4.10 Fast Spectroscopic Imaging.- 1.4.11 Recent MR Imaging Techniques.- 1.5 Computer Tomography Image Generation.- 1.5.1 Fourier Reconstruction Method.- 1.6 Positron-Emission Tomography Image Generation.- 1.6.1 Underlying Principles of.- 1.6.2 Usage of PET in Diagnosis.- 1.6.3 Fourier Slice Theorem.- 1.6.4 The Reconstruction Algorithm in PET.- 1.6.5 Image Reconstruction Using Filtered Back-Projection.- 1.7 Comparison of Imaging Modalities: A Summary.- 1.7.1 Acknowledgements.- 2. Segmentation in Echocardiographic Images.- 2.1 Introduction.- 2.2 Heart Physiology and Anatomy.- 2.2.1 Cardiac Function.- 2.2.2 Standard LV Views in 2-DEs.- 2.2.3 LV Function Assessment Using 2-DEs.- 2.3 Review of LV Boundary Extraction Techniques Applied to Echocardiographic Data.- 2.3.1 Acoustic Quantification Techniques.- 2.3.2 Image-Based Techniques.- 2.3.3 2-DE Image Processing Techniques.- 2.4Automatic Fuzzy Reasoning-Based Left Ventricular Center Point Extraction.- 2.4.1 LVCP Extraction System Overview.- 2.4.2 Stage 1: Pre-Processing.- 2.4.3 Stage 2: LVCP Features Fuzzification.- 2.4.4 Template Matching.- 2.4.5 Experimental Results.- 2.4.6 Conclusion.- 2.5 A New Edge Detection in the Wavelet Transform Domain.- 2.5.1 Multiscale Edge Detection and the Wavelet Transform.- 2.5.2 Edge Detection Based on the Global Maximum of Wavelet Transform (GMWT).- 2.5.3 GMWT Performance Analysis and Comparison.- 2.6 LV Segmentation System.- 2.6.1 Overall Reference.- 2.6.2 3D Non-Uniform Radial Intensity Sampling.- 2.6.3 LV Boundary Edge Detection on 3D Radial Intensity Matrix.- 2.6.4 Post-Processing of the Edges and Closed LVE Approximation.- 2.6.5 Automatic LV Volume Assessment.- 2.7 Conclusions.- 2.8 Acknowledgments.- 3. Cardiac Boundary Segmentation.- 3.1 Introduction.- 3.2 Cardiac Anatomy and Data Acquisitions for MR, CT, Ul-trasound and X-Rays.- 3.2.1 Cardiac Anatomy.- 3.2.2 Cardiac MR, CT, Ultrasound and X-Ray Acquisitions.- 3.3 Low- and Medium-Level LV Segmentation Techniques.- 3.3.1 Smoothing Image Data.- 3.3.2 Manual and Semi-Automatic LV Thresholding.- 3.3.3 LV Dynamic Thresholding.- 3.3.4 Edge-Based Techniques.- 3.3.5 Mathematical Morphology-Based Techniques.- 3.3.6 Drawbacks of Low-Level LV Segmentation Techniques.- 3.4 Model-Based Pattern Recognition Methods for LV Modeling.- 3.4.1 LV Active Contour Models in the Spatial and Temporal Domains.- 3.4.2 Model-Based Pattern Recognition Learning Methods.- 3.4.3 Polyline Distance Measure and Performance Terms.- 3.4.4 Data Analysis Using IdCM, InCM and the Greedy Method.- 3.5 Left Ventricle Apex Modeling: A Model-Based Approach.- 3.5.1 Longitudinal Axis and Apex Modeling.- 3.5.2 Ruled Surface Model.- 3.5.3 Ruled Surface sr and its Coefficients.- 3.5.4 Estimation of Robust Coefficients and Coordinates of the Ruled Surface.- 3.5.5 Experiment Design.- 3.5.6 Analytical Error Measure, AQin for Inlier Data.- 3.5.7 Experiments, Results and Discussions.- 3.5.8 Conclusions on LV Apex Modeling.- 3.6 Integration of Low-Level Features in LV Model-Based Cardiac Imaging: Fusion of Two Computer Vision Systems.- 3.7 General Purpose LV Validation Technique.- 3.8 LV Convex Hulling: Quadratic Training-Based Point Modeling.- 3.8.1 Quadratic Vs. Linear Optimization for Convex Hulling.- 3.9 LV Eigen Shape Modeling.- 3.9.1 Procrustes Superposition.- 3.9.2 Dimensionality Reduction Using Constraints for Joint.- 3.10 LV Neural Network Models.- 3.11 Comparative Study and Summary of the Characteristics of Model-Based Techniques.- 3.11.1 Characteristics of Model-Based LV Imaging.- 3.12 LV Quantification: Wall Motion and Tracking.- 3.12.1 LV Wall Motion Measurements.- 3.12.2 LV Volume Measurements.- 3.12.3 LV Wall Motion Tracking.- 3.13 Conclusions.- 3.13.1 Cardiac Hardware.- 3.13.2 Cardiac Software.- 3.13.3 Summary.- 3.13.4 Acknowledgments.- 4. Brain Segmentation Techniques.- 4.1 Introduction.- 4.1.1 Human Brain Anatomy and the MRI System.- 4.1.2 Applications of Brain Segmentation.- 4.2 Brain Scanning and its Clinical Significance.- 4.3 Region-Based 2-D and 3-D Cortical Segmentation Techniques.- 4.3.1 Atlas-Based and Threshold-Based Techniques.- 4.3.2 Cortical Segmentation Using Probability-Based Techniques.- 4.3.3 Clustering-Based Cortical Segmentation Techniques.- 4.3.4 Mathematical Morphology-Based Cortical Segmentation Techniques.- 4.3.5 Prior Knowledge-Based Techniques.- 4.3.6 Texture-Based Techniques.- 4.3.7 Neural Network-Based Techniques.- 4.3.8 Regional Hyperstack: Fusion of Edge-Diffusion with Region-Linking.- 4.3.9 Fusion of Probability-Based with Edge Detectors, Connectivity and Region-Growing.- 4.3.10 Summary of Region-Based Techniques: Pros and Cons.- 4.4 Boundary/Surface-Based 2-D and 3-D Cortical Segmentation Techniques: Edge, Reconstruction, Parametric and Geometric Snakes/Surfaces.- 4.4.1 Edge-Based Cortical-Boundary Estimation Techniques.- 4.4.2 3-D Cortical Reconstruction From 2-D Serial Cross-Sections (Bourke/Victoria).- 4.4.3 2-D and 3-D Parametric Deformable Models for Cortical Boundary Estimation: Snakes, Fitting, Constrained, Ribbon, T-Surface, Connectedness.- 4.4.4 2-D and 3-D Geometric Deformable Models.- 4.4.5 A Note on Isosurface Extraction (Lorensen/GE).- 4.4.6 Summary of Boundary/Surface-Based Techniques: Pros and Cons.- 4.5 Fusion of Boundary/Surface with Region-Based 2-D and 3-D Cortical Segmentation Techniques.- 4.5.1 2-D/3-D Regional Parametric Boundary: Fusion of Boundary with Classification (Kapur/MIT).- 4.5.2 Regional Parametric Surfaces: Fusion of Surface with Clustering (Xu/JHU).- 4.5.3 2-D Regional Geometric Boundary: Fusion of Boundary with Clustering for Cortical Boundary Estimation (Suri/Marconi).- 4.5.34 3-D Regional Geometric Surfaces: Fusion of Geometric Surface with Probability-Based Voxel Classification (Zeng/Yale).- 4.5.5 2-D/3-D Regional Geometric Surface: Fusion of Geometric Boundary/Surface with Global Shape Information (Leventon/MIT).- 4.5.6 2-D/3-D Regional Geometric Surface: Fusion of Boundary/Surface with Bayesian-Based Pixel Classification (Barillot/IRISA).- 4.5.7 Similarities/Differences Between Different Cortical Segmentation Techniques.- 4.6 3-D Visualization Using Volume Rendering and Texture Mapping.- 4.6.1 Volume Rendering Algorithm for Brain Segmentation.- 4.6.2 Texture Mapping Algorithm for Segmented Brain Visualization.- 4.7 A Note on fMRI: Algorithmic Approach for Establishing the Relationship Between Cognitive Functions and Brain Cortical Anatomy.- 4.7.1 Superiority of fMRI over PET/SPECT Imaging.- 4.7.2 Applications of fMRI.- 4.7.3 Algorithm for Superimposition of Functional and Anatomical Cortex.- 4.7.4 A Short Note on fMRI Time Course Data Analysis.- 4.7.5 Measure of Cortex Geometry.- 4.8 Discussions: Advantages, Validation and New Challenges i 2-D.- 4.8.1 Advantages of Regional Geometric Boundary/Surfaces.- 4.8.2 Validation of 2-D and 3-D Cortical Segmentation Algorithms.- 4.8.3 Challenges in 2-D and 3-D Cortical Segmentation Algorithms.- 4.8.4 Challenges in fMRI.- 4.9 Conclusions and the Future.- 4.9.1 Acknowledgements.- 5. Segmentation for Multiple Sclerosis Lesion.- 5.1 Introduction.- 5.2 Segmentation Techniques.- 5.2.1 Multi-Spectral Techniques.- 5.2.2 Feature Space Classification.- 5.2.3 Supervised Segmentation.- 5.2.4 Unsupervised Segmentation.- 5.2.5 Automatic Segmentation.- 5.3 AFFIRMATIVE Images.- 5.4 Image Pre-Processing.- 5.4.1 RF Inhomogeneity Correction.- 5.4.2 Image Stripping.- 5.4.3 Three Dimensional MR Image Registration.- 5.4.4 Segmentation.- 5.4.5 Flow Correction.- 5.4.6 Evaluation and Validation.- 5.5 Quantification of Enhancing Multiple Sclerosis Lesions.- 5.6 Quadruple Contrast Imaging.- 5.7 Discussion.- 5.7.1 Acknowledgements.- 6. Finite Mixture Models.- 6.1 Introduction.- 6.2 Pixel Labeling Using the Classical Mixture Model.- 6.3 Pixel Labeling Using the Spatially Variant Mixture Model.- 6.4 Comparison of CMM and SVMM for Pixel Labeling.- 6.5 Bayesian Pixel Labeling Using the SVMM.- 6.6 Segmentation Results.- 6.6.1 Computer Simulations.- 6.6.2 Application to Magnetic Resonance Images.- 6.7 Practical Aspects.- 6.8 Summary.- 6.9 Acknowledgements.- 7. MR Spectroscopy.- 7.1 Introduction.- 7.2 A Short History of Neurospectroscopic Imaging and Segmentation in Alzheimer’s Disease and Multiple Sclerosis.- 7.2.1 Alzheimer’s Disease.- 7.2.2 Multiple Sclerosis.- 7.3 Data Acquisition and Image Segmentation.- 7.3.1 Image Pre-Processing for Segmentation.- 7.3.2 Image Post-Processing for Segmentation.- 7.4 Proton Magnetic Resonance Spectroscopic Imaging and Segmentation in Multiple Sclerosis.- 7.4.1 Automatic MRSI Segmentation and Image Processing Algorithm.- 7.4.2 Relative Metabolite Concentrations and Contribution of Gray Matter and White Matter in the Normal Human Brain.- 7.4.3 MRSI and Gadolinium-Enhanced (Gd).- 7.4.4 Lesion Load and Metabolite Concentrations by Segmentation and MRSI.- 7.4.5 MR Spectroscopic Imaging and Localization for Segmentation.- 7.4.6 Lesion Segmentation and Quantification.- 7.4.7 Magnetic Resonance Spectroscopic Imaging and Segmentation Data Processing.- 7.4.8 Statistical Analysis.- 7.5 Proton Magnetic Resonance Spectroscopic Imaging and Segmentation of Alzheimer’s Disease.- 7.5.1 MRSI Data Acquisition Methods.- 7.5.2 H-1 MR Spectra Analysis.- 7.6 Applications of Magnetic Resonance Spectroscopic Imaging and Segmentation.- 7.6.1 Multiple Sclerosis Lesion Metabolite Characteristics and Serial Changes.- 7.6.2 zheimer’s Disease Plaque Metabolite Characteristics.- 7.7 Discussion.- 7.8 Conclusion.- 7.8.1 Acknowledgements.- 8. Fast WM/GM Boundary Estimation.- 8.1 Introduction.- 8.2 Derivation of the Regional Geometric Active Contour Model from the Classical Parametric Deformable Model.- 8.3 Numerical Implementation of the Three Speed Functions in the Level Set Framework for Geometric Snake Propagation.- 8.3.1 Regional Speed Term Expressed in Terms of the Level Set Function (ø).- 8.3.2 Gradient Speed Term Expressed in Terms of the Level Set Function (ø).- 8.3.3 Curvature Speed Term Expressed in Terms of the Level Set Function (ø).- 8.4 Fast Brain Segmentation System Based on Regional Level Sets.- 8.4.1 Overall System and Its Components.- 8.4.2 Fuzzy Membership Computation/Pixel Classification.- 8.4.3 Eikonal Equation and its Mathematical Solution.- 8.4.4 Fast Marching Method for Solving the Eikonal Equation.- 8.4.5 A Note on the Heap Sorting Algorithm.- 8.4.6 Segmentation Engine: Running the Level Set Method in the Narrow Band.- 8.5 MR Segmentation Results on Synthetic and Real Data.- 8.5.1 Input Data Set and Input Level Set Parameters.- 8.5.2 Results: Synthetic and Real.- 8.5.3 Numerical Stability, Signed Distance Transformation Computation, Sensitivity of Parameters and Speed Issues.- 8.6 Advantages of the Regional Level Set Technique.- 8.7 Discussions: Comparison with Previous Techniques.- 8.8 Conclusions and Further Directions.- 8.8.1 Acknowledgements.- 9. Digital Mammography Segmentation.- 9.1 Introduction.- 9.2 Image Segmentation in Mammography.- 9.3 Anatomy of the Breast.- 9.4 Image Acquisition and Formats.- 9.4.1 Digitization of X-Ray Mammograms.- 9.4.2 Image Formats.- 9.4.3 Image Quantization and Tree-Pyramids.- 9.5 Mammogram Enhancement Methods.- 9.6 Quantifying Mammogram Enhancement.- 9.7 Segmentation of Breast Profile.- 9.8 Segmentation of Microcalcifications.- 9.9 Segmentation of Masses.- 9.9.1 Global Methods.- 9.9.2 Edge-Based Methods.- 9.9.3 Region-Based Segmentation.- 9.9.4 ROI Detection Techniques Using a Single Breast.- 9.9.5 ROI Detection Techniques Using Breast Symmetry.- 9.9.6 Detection of Spicules.- 9.9.7 Breast Alignment for Segmentation.- 9.10 Measures of Segmentation and Abnormality Detection.- 9.11 Feature Extraction From Segmented Regions.- 9.11.1 Morphological Features.- 9.11.2 Texture Features.- 9.11.3 Other Features.- 9.12 Public Domain Databases in Mammography.- 9.12.1 The Digital Database for Screening Mammography (DDSM).- 9.12.2 LLNL/UCSF Database.- 9.12.3 Washington University Digital Mammography Database.- 9.12.4 The Mammographic Image Analysis Society (MIAS) Database.- 9.13 Classification and Measures of Performance.- 9.13.1 Classification Techniques.- 9.13.2 The Receiver Operating Characteristic Curve.- 9.14 Conclusions.- 9.15 Acknowledgements.- 10. Cell Image Segmentation for Diagnostic Pathology.- 10.1 Introduction.- 10.2 Segmentation.- 10.2.1 Feature Space Analysis.- 10.2.2 Mean Shift Procedure.- 10.2.3 Cell Segmentation.- 10.2.4 Segmentation Examples.- 10.3 Decision Support System for Pathology.- 10.3.1 Problem Domain.- 10.3.2 System Overview.- 10.3.3 Current Database.- 10.3.4 Analysis of Visual Attributes.- 10.3.5 Overall Dissimilarity Metric.- 10.3.6 Performance Evaluation and Comparisons.- 10.4 Conclusion.- 11. The Future in Segmentation.- 11.1 Future Research in Medical Image Segmentation.- 11.1.1 The Future of MR Image Generation and Physical Principles.- 11.1.2 The Future of Cardiac Imaging.- 11.2.3 The Future of Neurological Segmentation.- 11.2.4 The Future in Digital Mammography.- 11.2.5 The Future of Pathology Image Segmentation.

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Book SynopsisThe book collects the contributions to the NATO Advanced Study Institute on "Speech Recognition and Understanding: Recent Advances, Trends and Applications", held in Cetraro, Italy, during the first two weeks of July 1990. This Institute focused on three topics that are considered of particular interest and rich of i'p.novation by researchers in the fields of speech recognition and understanding: Advances in Hidden Markov modeling, connectionist approaches to speech and language modeling, and linguistic processing including language and dialogue modeling. The purpose of any ASI is that of encouraging scientific communications between researchers of NATO countries through advanced tutorials and presentations: excellent tutorials were offered by invited speakers that present in this book 15 papers which sum­ marize or detail the topics covered in their lectures. The lectures were complemented by discussions, panel sections and by the presentation of related works carried on by some of the attending researchers: these presentations have been collected in 42 short contributions to the Proceedings. This volume, that the reader can find useful for an overview, although incomplete, of the state of the art in speech understanding, is divided into 6 Parts.Table of Contents1 Recent Results on Hidden Markov Models.- Invited papers.- Hidden Markov Models for Speech Recognition — Strengths and Limitations.- Hidden Markov Models and Speaker Adaptation.- A 20,000 word Automatic Speech Recognizer. Adaptation to French of the US TANGORA System.- Automatic Adjustments of the Markov Models Topology for Speech Recognition Applications over the Telephone.- Phonetic Structure Inference of Phonemic HMM.- Phonetic Units and Phonotactical Structure Inference by Ergodic Hidden Markov Models.- Clustering of Gaussian Densities in Hidden Markov Models.- Developments in High-Performance Connected Digit Recognition.- Robust Speaker-Independent Hidden Markov Model Based Word Spotter.- Robust Speech Recognition in Noisy and Reverberant Environments.- An ISDN Speech Server based on Speaker Independent Continuous Hidden Markov Models.- RAMSES: A Spanish Demisyllable Based Continuous Speech Recognition System.- Speaker Independent, 1000 Words Speech Recognition in Spanish.- Continuously Variable Transition Probability HMM for Speech Recognition.- 2 Continuous Speech Recognition Systems.- Invited papers.- Context-Dependent Phonetic Hidden Markov Models for Speaker-Independent Continuous Speech Recognition (Abstract).- Speaker-Independent Continuous Speech Recognition Using Continuous Density Hidden Markov Models.- Contributed papers.- A Fast Lexical Selection Strategy for Large Vocabulary Continuous Speech Recognition.- Performance of a Speaker-Independent Continuous Speech Recognizer.- Automatic Transformation of Speech Databases for Continuous Speech Recognition.- Iterative Optimization of the Data Driven Analysis in Continuous Speech.- Syllable-based Stochastic Models for Continuous Speech Recognition.- Word Hypothesization in Continuous Speech Recognition.- Phone Recognition Using High Order Phonotactic Constraints.- An Efficient Structure for Continuous Speech Recognition.- Search Organization for Large Vocabulary Continuous Speech Recognition.- 3 Connectionist Models of Speech.- Invited papers.- Neural Networks or Hidden Markov Models for Automatic Speech Recognition: Is there a Choice?.- Neural Networks for Continuous Speech Recognition.- Connectionist Large Vocabulary Speech Recognition.- The Cortical Column as a Model for Speech Recognition: Principles and First Experiments.- Contributed papers.- Radial Basis Functions for Speech Recognition.- Phonetic Features Extraction Using Time-Delay Neural Networks.- Improved Broad Phonetic Classification and Segmentation with an Auditory Model.- Automatic Learning of a Production Rule System for Acoustic-Phonetic Decoding.- 4 Stochastic Models for Language and Dialogue.- Invited papers.- Stochastic Grammars and Pattern Recognition.- Basic Methods of Probabilistic Context Free Grammars.- A Probabilistic Approach to Person-Robot Dialogue.- Contributed papers.- Experimenting Text Creation by Natural-Language, Large-Vocabulary Speech Recognition.- DUALGRAM: An Efficient Method for Representing Limited-Domain Language Models.- Strategies for Speech Recognition and Understanding Using Layered Protocols.- 5 Understanding and Dialogue Systems.- Invited papers.- TINA: A Probabilistic Syntactic Parser for Speech Understanding Systems.- The Voyager Speech Understanding System: A Progress Report.- The Interaction of Word Recognition and Linguistic Processing in Speech Understanding.- Linguistic Processing in a Speech Understanding System.- Contributed papers.- Linguistic Tools for Speech Recognition and Understanding.- Evidential Reasoning and the Combination of Knowledge and Statistical Techniques in Syllable Based Speech Recognition.- 6 Speech Analysis, Coding and Segmentation.- Contributed papers.- Data Base Management for Use with Acoustic-Phonetic Speech Data Bases.- BPF Outputs Compared with Formant Frequencies and LPCs for the Recognition of Vowels.- A Codification of Error Signal by Splines Functions.- Specific Distance for Feature Selection in Speech Recognition.- Multiple Template Modeling of Sublexical Units.- Learning Structural Models of Sublexical Units.- On the Use of Negative Samples in the MGGI Methodology and its Application for Difficult Vocabulary Recognition Tasks.- A New Method for Dynamic Time Alignment of Speech Waveforms.- A New Technique for Automatic Segmentation of Continuous Speech.- Segmentation of Speech based upon a Linear Model of the Effects of Coarticulation 549 P.J. D.

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Book SynopsisSebastian T. Gollmer entwickelt neue Methoden und Algorithmen für die Erstellung statistischer Formmodelle, die Formmodellierung und die formmodellbasierte Bildsegmentierung. Der Autor diskutiert ihre Vorteile gegenüber den jeweils etablierten Verfahren aus der Literatur und evaluiert den generellen Einfluss dieser drei Aspekte auf die erzielbare Segmentierungsgenauigkeit. Letzteres erfolgt sowohl unter Verwendung neu entwickelter und etablierter Evaluierungsverfahren als auch im Rahmen realer Anwendungen. Von besonderer praktischer Relevanz zeigen sich dabei die exzellenten, mit einem neuen vollautomatischen Algorithmus erzielten Ergebnisse für die Unterkiefersegmentierung.Table of ContentsStatistische Formmodelle.- Evaluierung der Korrespondenzgüte.- Untersuchung der Normalverteilungsannahme.- Kernbasierte Formmodellierung.- Relaxiertes aktives Formmodell.- Unterkiefer- und Abdomensegmentierung.

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Book SynopsisThis book introduces readers to the basic concepts, classical approaches, and the newest design, development, and applications of biometrics. It also provides a glimpse of future designs and research directions in biometrics. In addition, it discusses some latest concerns and issues in this area. Suitable for a wide range of readers, the book explains professional terms in plain English and is ahead of time. Some concepts and designs discussed are so new that commercial systems based on them may not arrive in the market in the next 10 to 20 years. Table of ContentsBasic Concepts, Classic Approaches and Newest Designs in Biometrics: Fingerprint Recognition. Face Recognition Iris Recognition. Speaker Recognition. Palm Print Recognition. Multimodal Biometrics.Classic Approaches and Newest Designs in Biometric Technologies and Systems: Biometrics-Based Smart ID Card. Smart Clothes for Biometrics. Challenges and Concerns in Biometrics: Spoof and Vulnerability of Biometrics. Accessibility, Usability, and Legal Challenges in Biometrics.Latest Design and the Future of Biometric Systems to Address the Challenges and Concerns in Biometrics: Cancelable Biometrics Continuous Biometric Verification. Future Trends in Biometrics.

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Book SynopsisStatistical pattern recognition relates to the use of statistical techniques for analysing data measurements in order to extract information and make justified decisions. It is a very active area of study and research, which has seen many advances in recent years. Applications such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition, all require robust and efficient pattern recognition techniques. This third edition provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields, including the areas of engineering, statistics, computer science and the social sciences. The book has been updated to cover new methods and applications, and includes a wide range of techniques such as Bayesian methods, neural networks, support vector machines, feature selection and feature reduction techniques.Technical descriptions and motivations are provided, and the techniques are illustTrade Review“In the end I must add that this book is so appealing that I often found myself lost in the reading, pausing the overview of the manuscript in order to look more into some presented subject, and not being able to continue until I had finished seeing all about it.” (Zentralblatt MATH, 1 December 2012)Table of ContentsPreface xix Notation xxiii 1 Introduction to Statistical Pattern Recognition 1 1.1 Statistical Pattern Recognition 1 1.1.1 Introduction 1 1.1.2 The Basic Model 2 1.2 Stages in a Pattern Recognition Problem 4 1.3 Issues 6 1.4 Approaches to Statistical Pattern Recognition 7 1.5 Elementary Decision Theory 8 1.5.1 Bayes’ Decision Rule for Minimum Error 8 1.5.2 Bayes’ Decision Rule for Minimum Error – Reject Option 12 1.5.3 Bayes’ Decision Rule for Minimum Risk 13 1.5.4 Bayes’ Decision Rule for Minimum Risk – Reject Option 15 1.5.5 Neyman–Pearson Decision Rule 15 1.5.6 Minimax Criterion 18 1.5.7 Discussion 19 1.6 Discriminant Functions 20 1.6.1 Introduction 20 1.6.2 Linear Discriminant Functions 21 1.6.3 Piecewise Linear Discriminant Functions 23 1.6.4 Generalised Linear Discriminant Function 24 1.6.5 Summary 26 1.7 Multiple Regression 27 1.8 Outline of Book 29 1.9 Notes and References 29 Exercises 31 2 Density Estimation – Parametric 33 2.1 Introduction 33 2.2 Estimating the Parameters of the Distributions 34 2.2.1 Estimative Approach 34 2.2.2 Predictive Approach 35 2.3 The Gaussian Classifier 35 2.3.1 Specification 35 2.3.2 Derivation of the Gaussian Classifier Plug-In Estimates 37 2.3.3 Example Application Study 39 2.4 Dealing with Singularities in the Gaussian Classifier 40 2.4.1 Introduction 40 2.4.2 Na¨ive Bayes 40 2.4.3 Projection onto a Subspace 41 2.4.4 Linear Discriminant Function 41 2.4.5 Regularised Discriminant Analysis 42 2.4.6 Example Application Study 44 2.4.7 Further Developments 45 2.4.8 Summary 46 2.5 Finite Mixture Models 46 2.5.1 Introduction 46 2.5.2 Mixture Models for Discrimination 48 2.5.3 Parameter Estimation for Normal Mixture Models 49 2.5.4 Normal Mixture Model Covariance Matrix Constraints 51 2.5.5 How Many Components? 52 2.5.6 Maximum Likelihood Estimation via EM 55 2.5.7 Example Application Study 60 2.5.8 Further Developments 62 2.5.9 Summary 63 2.6 Application Studies 63 2.7 Summary and Discussion 66 2.8 Recommendations 66 2.9 Notes and References 67 Exercises 67 3 Density Estimation – Bayesian 70 3.1 Introduction 70 3.1.1 Basics 72 3.1.2 Recursive Calculation 72 3.1.3 Proportionality 73 3.2 Analytic Solutions 73 3.2.1 Conjugate Priors 73 3.2.2 Estimating the Mean of a Normal Distribution with Known Variance 75 3.2.3 Estimating the Mean and the Covariance Matrix of a Multivariate Normal Distribution 79 3.2.4 Unknown Prior Class Probabilities 85 3.2.5 Summary 87 3.3 Bayesian Sampling Schemes 87 3.3.1 Introduction 87 3.3.2 Summarisation 87 3.3.3 Sampling Version of the Bayesian Classifier 89 3.3.4 Rejection Sampling 89 3.3.5 Ratio of Uniforms 90 3.3.6 Importance Sampling 92 3.4 Markov Chain Monte Carlo Methods 95 3.4.1 Introduction 95 3.4.2 The Gibbs Sampler 95 3.4.3 Metropolis–Hastings Algorithm 103 3.4.4 Data Augmentation 107 3.4.5 Reversible Jump Markov Chain Monte Carlo 108 3.4.6 Slice Sampling 109 3.4.7 MCMC Example – Estimation of Noisy Sinusoids 111 3.4.8 Summary 115 3.4.9 Notes and References 116 3.5 Bayesian Approaches to Discrimination 116 3.5.1 Labelled Training Data 116 3.5.2 Unlabelled Training Data 117 3.6 Sequential Monte Carlo Samplers 119 3.6.1 Introduction 119 3.6.2 Basic Methodology 121 3.6.3 Summary 125 3.7 Variational Bayes 126 3.7.1 Introduction 126 3.7.2 Description 126 3.7.3 Factorised Variational Approximation 129 3.7.4 Simple Example 131 3.7.5 Use of the Procedure for Model Selection 135 3.7.6 Further Developments and Applications 136 3.7.7 Summary 137 3.8 Approximate Bayesian Computation 137 3.8.1 Introduction 137 3.8.2 ABC Rejection Sampling 138 3.8.3 ABC MCMC Sampling 140 3.8.4 ABC Population Monte Carlo Sampling 141 3.8.5 Model Selection 142 3.8.6 Summary 143 3.9 Example Application Study 144 3.10 Application Studies 145 3.11 Summary and Discussion 146 3.12 Recommendations 147 3.13 Notes and References 147 Exercises 148 4 Density Estimation – Nonparametric 150 4.1 Introduction 150 4.1.1 Basic Properties of Density Estimators 150 4.2 k-Nearest-Neighbour Method 152 4.2.1 k-Nearest-Neighbour Classifier 152 4.2.2 Derivation 154 4.2.3 Choice of Distance Metric 157 4.2.4 Properties of the Nearest-Neighbour Rule 159 4.2.5 Linear Approximating and Eliminating Search Algorithm 159 4.2.6 Branch and Bound Search Algorithms: kd-Trees 163 4.2.7 Branch and Bound Search Algorithms: Ball-Trees 170 4.2.8 Editing Techniques 174 4.2.9 Example Application Study 177 4.2.10 Further Developments 178 4.2.11 Summary 179 4.3 Histogram Method 180 4.3.1 Data Adaptive Histograms 181 4.3.2 Independence Assumption (Naïve Bayes) 181 4.3.3 Lancaster Models 182 4.3.4 Maximum Weight Dependence Trees 183 4.3.5 Bayesian Networks 186 4.3.6 Example Application Study – Naïve Bayes Text Classification 190 4.3.7 Summary 193 4.4 Kernel Methods 194 4.4.1 Biasedness 197 4.4.2 Multivariate Extension 198 4.4.3 Choice of Smoothing Parameter 199 4.4.4 Choice of Kernel 201 4.4.5 Example Application Study 202 4.4.6 Further Developments 203 4.4.7 Summary 203 4.5 Expansion by Basis Functions 204 4.6 Copulas 207 4.6.1 Introduction 207 4.6.2 Mathematical Basis 207 4.6.3 Copula Functions 208 4.6.4 Estimating Copula Probability Density Functions 209 4.6.5 Simple Example 211 4.6.6 Summary 212 4.7 Application Studies 213 4.7.1 Comparative Studies 216 4.8 Summary and Discussion 216 4.9 Recommendations 217 4.10 Notes and References 217 Exercises 218 5 Linear Discriminant Analysis 221 5.1 Introduction 221 5.2 Two-Class Algorithms 222 5.2.1 General Ideas 222 5.2.2 Perceptron Criterion 223 5.2.3 Fisher’s Criterion 227 5.2.4 Least Mean-Squared-Error Procedures 228 5.2.5 Further Developments 235 5.2.6 Summary 235 5.3 Multiclass Algorithms 236 5.3.1 General Ideas 236 5.3.2 Error-Correction Procedure 237 5.3.3 Fisher’s Criterion – Linear Discriminant Analysis 238 5.3.4 Least Mean-Squared-Error Procedures 241 5.3.5 Regularisation 246 5.3.6 Example Application Study 246 5.3.7 Further Developments 247 5.3.8 Summary 248 5.4 Support Vector Machines 249 5.4.1 Introduction 249 5.4.2 Linearly Separable Two-Class Data 249 5.4.3 Linearly Nonseparable Two-Class Data 253 5.4.4 Multiclass SVMs 256 5.4.5 SVMs for Regression 257 5.4.6 Implementation 259 5.4.7 Example Application Study 262 5.4.8 Summary 263 5.5 Logistic Discrimination 263 5.5.1 Two-Class Case 263 5.5.2 Maximum Likelihood Estimation 264 5.5.3 Multiclass Logistic Discrimination 266 5.5.4 Example Application Study 267 5.5.5 Further Developments 267 5.5.6 Summary 268 5.6 Application Studies 268 5.7 Summary and Discussion 268 5.8 Recommendations 269 5.9 Notes and References 270 Exercises 270 6 Nonlinear Discriminant Analysis – Kernel and Projection Methods 274 6.1 Introduction 274 6.2 Radial Basis Functions 276 6.2.1 Introduction 276 6.2.2 Specifying the Model 278 6.2.3 Specifying the Functional Form 278 6.2.4 The Positions of the Centres 279 6.2.5 Smoothing Parameters 281 6.2.6 Calculation of the Weights 282 6.2.7 Model Order Selection 284 6.2.8 Simple RBF 285 6.2.9 Motivation 286 6.2.10 RBF Properties 288 6.2.11 Example Application Study 288 6.2.12 Further Developments 289 6.2.13 Summary 290 6.3 Nonlinear Support Vector Machines 291 6.3.1 Introduction 291 6.3.2 Binary Classification 291 6.3.3 Types of Kernel 292 6.3.4 Model Selection 293 6.3.5 Multiclass SVMs 294 6.3.6 Probability Estimates 294 6.3.7 Nonlinear Regression 296 6.3.8 Example Application Study 296 6.3.9 Further Developments 297 6.3.10 Summary 298 6.4 The Multilayer Perceptron 298 6.4.1 Introduction 298 6.4.2 Specifying the MLP Structure 299 6.4.3 Determining the MLP Weights 300 6.4.4 Modelling Capacity of the MLP 307 6.4.5 Logistic Classification 307 6.4.6 Example Application Study 310 6.4.7 Bayesian MLP Networks 311 6.4.8 Projection Pursuit 313 6.4.9 Summary 313 6.5 Application Studies 314 6.6 Summary and Discussion 316 6.7 Recommendations 317 6.8 Notes and References 318 Exercises 318 7 Rule and Decision Tree Induction 322 7.1 Introduction 322 7.2 Decision Trees 323 7.2.1 Introduction 323 7.2.2 Decision Tree Construction 326 7.2.3 Selection of the Splitting Rule 327 7.2.4 Terminating the Splitting Procedure 330 7.2.5 Assigning Class Labels to Terminal Nodes 332 7.2.6 Decision Tree Pruning – Worked Example 332 7.2.7 Decision Tree Construction Methods 337 7.2.8 Other Issues 339 7.2.9 Example Application Study 340 7.2.10 Further Developments 341 7.2.11 Summary 342 7.3 Rule Induction 342 7.3.1 Introduction 342 7.3.2 Generating Rules from a Decision Tree 345 7.3.3 Rule Induction Using a Sequential Covering Algorithm 345 7.3.4 Example Application Study 350 7.3.5 Further Developments 351 7.3.6 Summary 351 7.4 Multivariate Adaptive Regression Splines 351 7.4.1 Introduction 351 7.4.2 Recursive Partitioning Model 351 7.4.3 Example Application Study 355 7.4.4 Further Developments 355 7.4.5 Summary 356 7.5 Application Studies 356 7.6 Summary and Discussion 358 7.7 Recommendations 358 7.8 Notes and References 359 Exercises 359 8 Ensemble Methods 361 8.1 Introduction 361 8.2 Characterising a Classifier Combination Scheme 362 8.2.1 Feature Space 363 8.2.2 Level 366 8.2.3 Degree of Training 368 8.2.4 Form of Component Classifiers 368 8.2.5 Structure 369 8.2.6 Optimisation 369 8.3 Data Fusion 370 8.3.1 Architectures 370 8.3.2 Bayesian Approaches 371 8.3.3 Neyman–Pearson Formulation 373 8.3.4 Trainable Rules 374 8.3.5 Fixed Rules 375 8.4 Classifier Combination Methods 376 8.4.1 Product Rule 376 8.4.2 Sum Rule 377 8.4.3 Min, Max and Median Combiners 378 8.4.4 Majority Vote 379 8.4.5 Borda Count 379 8.4.6 Combiners Trained on Class Predictions 380 8.4.7 Stacked Generalisation 382 8.4.8 Mixture of Experts 382 8.4.9 Bagging 385 8.4.10 Boosting 387 8.4.11 Random Forests 389 8.4.12 Model Averaging 390 8.4.13 Summary of Methods 396 8.4.14 Example Application Study 398 8.4.15 Further Developments 399 8.5 Application Studies 399 8.6 Summary and Discussion 400 8.7 Recommendations 401 8.8 Notes and References 401 Exercises 402 9 Performance Assessment 404 9.1 Introduction 404 9.2 Performance Assessment 405 9.2.1 Performance Measures 405 9.2.2 Discriminability 406 9.2.3 Reliability 413 9.2.4 ROC Curves for Performance Assessment 415 9.2.5 Population and Sensor Drift 419 9.2.6 Example Application Study 421 9.2.7 Further Developments 422 9.2.8 Summary 423 9.3 Comparing Classifier Performance 424 9.3.1 Which Technique is Best? 424 9.3.2 Statistical Tests 425 9.3.3 Comparing Rules When Misclassification Costs are Uncertain 426 9.3.4 Example Application Study 428 9.3.5 Further Developments 429 9.3.6 Summary 429 9.4 Application Studies 429 9.5 Summary and Discussion 430 9.6 Recommendations 430 9.7 Notes and References 430 Exercises 431 10 Feature Selection and Extraction 433 10.1 Introduction 433 10.2 Feature Selection 435 10.2.1 Introduction 435 10.2.2 Characterisation of Feature Selection Approaches 439 10.2.3 Evaluation Measures 440 10.2.4 Search Algorithms for Feature Subset Selection 449 10.2.5 Complete Search – Branch and Bound 450 10.2.6 Sequential Search 454 10.2.7 Random Search 458 10.2.8 Markov Blanket 459 10.2.9 Stability of Feature Selection 460 10.2.10 Example Application Study 462 10.2.11 Further Developments 462 10.2.12 Summary 463 10.3 Linear Feature Extraction 463 10.3.1 Principal Components Analysis 464 10.3.2 Karhunen–Lo`eve Transformation 475 10.3.3 Example Application Study 481 10.3.4 Further Developments 482 10.3.5 Summary 483 10.4 Multidimensional Scaling 484 10.4.1 Classical Scaling 484 10.4.2 Metric MDS 486 10.4.3 Ordinal Scaling 487 10.4.4 Algorithms 490 10.4.5 MDS for Feature Extraction 491 10.4.6 Example Application Study 492 10.4.7 Further Developments 493 10.4.8 Summary 493 10.5 Application Studies 493 10.6 Summary and Discussion 495 10.7 Recommendations 495 10.8 Notes and References 496 Exercises 497 11 Clustering 501 11.1 Introduction 501 11.2 Hierarchical Methods 502 11.2.1 Single-Link Method 503 11.2.2 Complete-Link Method 506 11.2.3 Sum-of-Squares Method 507 11.2.4 General Agglomerative Algorithm 508 11.2.5 Properties of a Hierarchical Classification 508 11.2.6 Example Application Study 509 11.2.7 Summary 509 11.3 Quick Partitions 510 11.4 Mixture Models 511 11.4.1 Model Description 511 11.4.2 Example Application Study 512 11.5 Sum-of-Squares Methods 513 11.5.1 Clustering Criteria 514 11.5.2 Clustering Algorithms 515 11.5.3 Vector Quantisation 520 11.5.4 Example Application Study 530 11.5.5 Further Developments 530 11.5.6 Summary 531 11.6 Spectral Clustering 531 11.6.1 Elementary Graph Theory 531 11.6.2 Similarity Matrices 534 11.6.3 Application to Clustering 534 11.6.4 Spectral Clustering Algorithm 535 11.6.5 Forms of Graph Laplacian 535 11.6.6 Example Application Study 536 11.6.7 Further Developments 538 11.6.8 Summary 538 11.7 Cluster Validity 538 11.7.1 Introduction 538 11.7.2 Statistical Tests 539 11.7.3 Absence of Class Structure 540 11.7.4 Validity of Individual Clusters 541 11.7.5 Hierarchical Clustering 542 11.7.6 Validation of Individual Clusterings 542 11.7.7 Partitions 543 11.7.8 Relative Criteria 543 11.7.9 Choosing the Number of Clusters 545 11.8 Application Studies 546 11.9 Summary and Discussion 549 11.10 Recommendations 551 11.11 Notes and References 552 Exercises 553 12 Complex Networks 555 12.1 Introduction 555 12.1.1 Characteristics 557 12.1.2 Properties 557 12.1.3 Questions to Address 559 12.1.4 Descriptive Features 560 12.1.5 Outline 560 12.2 Mathematics of Networks 561 12.2.1 Graph Matrices 561 12.2.2 Connectivity 562 12.2.3 Distance Measures 562 12.2.4 Weighted Networks 563 12.2.5 Centrality Measures 563 12.2.6 Random Graphs 564 12.3 Community Detection 565 12.3.1 Clustering Methods 565 12.3.2 Girvan–Newman Algorithm 568 12.3.3 Modularity Approaches 570 12.3.4 Local Modularity 571 12.3.5 Clique Percolation 573 12.3.6 Example Application Study 574 12.3.7 Further Developments 575 12.3.8 Summary 575 12.4 Link Prediction 575 12.4.1 Approaches to Link Prediction 576 12.4.2 Example Application Study 578 12.4.3 Further Developments 578 12.5 Application Studies 579 12.6 Summary and Discussion 579 12.7 Recommendations 580 12.8 Notes and References 580 Exercises 580 13 Additional Topics 581 13.1 Model Selection 581 13.1.1 Separate Training and Test Sets 582 13.1.2 Cross-Validation 582 13.1.3 The Bayesian Viewpoint 583 13.1.4 Akaike’s Information Criterion 583 13.1.5 Minimum Description Length 584 13.2 Missing Data 585 13.3 Outlier Detection and Robust Procedures 586 13.4 Mixed Continuous and Discrete Variables 587 13.5 Structural Risk Minimisation and the Vapnik–Chervonenkis Dimension 588 13.5.1 Bounds on the Expected Risk 588 13.5.2 The VC Dimension 589 References 591 Index 637

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Book SynopsisStatistical pattern recognition relates to the use of statistical techniques for analysing data measurements in order to extract information and make justified decisions. It is a very active area of study and research, which has seen many advances in recent years.Trade Review"In the end I must add that this book is so appealing that I often found myself lost in the reading, pausing the overview of the manuscript in order to look more into some presented subject, and not being able to continue until I had finished seeing all about it.” (Zentralblatt MATH, 1 December 2012)Table of ContentsPreface xix Notation xxiii 1 Introduction to Statistical Pattern Recognition 1 1.1 Statistical Pattern Recognition 1 1.1.1 Introduction 1 1.1.2 The Basic Model 2 1.2 Stages in a Pattern Recognition Problem 4 1.3 Issues 6 1.4 Approaches to Statistical Pattern Recognition 7 1.5 Elementary Decision Theory 8 1.5.1 Bayes’ Decision Rule for Minimum Error 8 1.5.2 Bayes’ Decision Rule for Minimum Error – Reject Option 12 1.5.3 Bayes’ Decision Rule for Minimum Risk 13 1.5.4 Bayes’ Decision Rule for Minimum Risk – Reject Option 15 1.5.5 Neyman–Pearson Decision Rule 15 1.5.6 Minimax Criterion 18 1.5.7 Discussion 19 1.6 Discriminant Functions 20 1.6.1 Introduction 20 1.6.2 Linear Discriminant Functions 21 1.6.3 Piecewise Linear Discriminant Functions 23 1.6.4 Generalised Linear Discriminant Function 24 1.6.5 Summary 26 1.7 Multiple Regression 27 1.8 Outline of Book 29 1.9 Notes and References 29 Exercises 31 2 Density Estimation – Parametric 33 2.1 Introduction 33 2.2 Estimating the Parameters of the Distributions 34 2.2.1 Estimative Approach 34 2.2.2 Predictive Approach 35 2.3 The Gaussian Classifier 35 2.3.1 Specification 35 2.3.2 Derivation of the Gaussian Classifier Plug-In Estimates 37 2.3.3 Example Application Study 39 2.4 Dealing with Singularities in the Gaussian Classifier 40 2.4.1 Introduction 40 2.4.2 Na¨ive Bayes 40 2.4.3 Projection onto a Subspace 41 2.4.4 Linear Discriminant Function 41 2.4.5 Regularised Discriminant Analysis 42 2.4.6 Example Application Study 44 2.4.7 Further Developments 45 2.4.8 Summary 46 2.5 Finite Mixture Models 46 2.5.1 Introduction 46 2.5.2 Mixture Models for Discrimination 48 2.5.3 Parameter Estimation for Normal Mixture Models 49 2.5.4 Normal Mixture Model Covariance Matrix Constraints 51 2.5.5 How Many Components? 52 2.5.6 Maximum Likelihood Estimation via EM 55 2.5.7 Example Application Study 60 2.5.8 Further Developments 62 2.5.9 Summary 63 2.6 Application Studies 63 2.7 Summary and Discussion 66 2.8 Recommendations 66 2.9 Notes and References 67 Exercises 67 3 Density Estimation – Bayesian 70 3.1 Introduction 70 3.1.1 Basics 72 3.1.2 Recursive Calculation 72 3.1.3 Proportionality 73 3.2 Analytic Solutions 73 3.2.1 Conjugate Priors 73 3.2.2 Estimating the Mean of a Normal Distribution with Known Variance 75 3.2.3 Estimating the Mean and the Covariance Matrix of a Multivariate Normal Distribution 79 3.2.4 Unknown Prior Class Probabilities 85 3.2.5 Summary 87 3.3 Bayesian Sampling Schemes 87 3.3.1 Introduction 87 3.3.2 Summarisation 87 3.3.3 Sampling Version of the Bayesian Classifier 89 3.3.4 Rejection Sampling 89 3.3.5 Ratio of Uniforms 90 3.3.6 Importance Sampling 92 3.4 Markov Chain Monte Carlo Methods 95 3.4.1 Introduction 95 3.4.2 The Gibbs Sampler 95 3.4.3 Metropolis–Hastings Algorithm 103 3.4.4 Data Augmentation 107 3.4.5 Reversible Jump Markov Chain Monte Carlo 108 3.4.6 Slice Sampling 109 3.4.7 MCMC Example – Estimation of Noisy Sinusoids 111 3.4.8 Summary 115 3.4.9 Notes and References 116 3.5 Bayesian Approaches to Discrimination 116 3.5.1 Labelled Training Data 116 3.5.2 Unlabelled Training Data 117 3.6 Sequential Monte Carlo Samplers 119 3.6.1 Introduction 119 3.6.2 Basic Methodology 121 3.6.3 Summary 125 3.7 Variational Bayes 126 3.7.1 Introduction 126 3.7.2 Description 126 3.7.3 Factorised Variational Approximation 129 3.7.4 Simple Example 131 3.7.5 Use of the Procedure for Model Selection 135 3.7.6 Further Developments and Applications 136 3.7.7 Summary 137 3.8 Approximate Bayesian Computation 137 3.8.1 Introduction 137 3.8.2 ABC Rejection Sampling 138 3.8.3 ABC MCMC Sampling 140 3.8.4 ABC Population Monte Carlo Sampling 141 3.8.5 Model Selection 142 3.8.6 Summary 143 3.9 Example Application Study 144 3.10 Application Studies 145 3.11 Summary and Discussion 146 3.12 Recommendations 147 3.13 Notes and References 147 Exercises 148 4 Density Estimation – Nonparametric 150 4.1 Introduction 150 4.1.1 Basic Properties of Density Estimators 150 4.2 k-Nearest-Neighbour Method 152 4.2.1 k-Nearest-Neighbour Classifier 152 4.2.2 Derivation 154 4.2.3 Choice of Distance Metric 157 4.2.4 Properties of the Nearest-Neighbour Rule 159 4.2.5 Linear Approximating and Eliminating Search Algorithm 159 4.2.6 Branch and Bound Search Algorithms: kd-Trees 163 4.2.7 Branch and Bound Search Algorithms: Ball-Trees 170 4.2.8 Editing Techniques 174 4.2.9 Example Application Study 177 4.2.10 Further Developments 178 4.2.11 Summary 179 4.3 Histogram Method 180 4.3.1 Data Adaptive Histograms 181 4.3.2 Independence Assumption (Na¨ive Bayes) 181 4.3.3 Lancaster Models 182 4.3.4 Maximum Weight Dependence Trees 183 4.3.5 Bayesian Networks 186 4.3.6 Example Application Study – Na¨ive Bayes Text Classification 190 4.3.7 Summary 193 4.4 Kernel Methods 194 4.4.1 Biasedness 197 4.4.2 Multivariate Extension 198 4.4.3 Choice of Smoothing Parameter 199 4.4.4 Choice of Kernel 201 4.4.5 Example Application Study 202 4.4.6 Further Developments 203 4.4.7 Summary 203 4.5 Expansion by Basis Functions 204 4.6 Copulas 207 4.6.1 Introduction 207 4.6.2 Mathematical Basis 207 4.6.3 Copula Functions 208 4.6.4 Estimating Copula Probability Density Functions 209 4.6.5 Simple Example 211 4.6.6 Summary 212 4.7 Application Studies 213 4.7.1 Comparative Studies 216 4.8 Summary and Discussion 216 4.9 Recommendations 217 4.10 Notes and References 217 Exercises 218 5 Linear Discriminant Analysis 221 5.1 Introduction 221 5.2 Two-Class Algorithms 222 5.2.1 General Ideas 222 5.2.2 Perceptron Criterion 223 5.2.3 Fisher’s Criterion 227 5.2.4 Least Mean-Squared-Error Procedures 228 5.2.5 Further Developments 235 5.2.6 Summary 235 5.3 Multiclass Algorithms 236 5.3.1 General Ideas 236 5.3.2 Error-Correction Procedure 237 5.3.3 Fisher’s Criterion – Linear Discriminant Analysis 238 5.3.4 Least Mean-Squared-Error Procedures 241 5.3.5 Regularisation 246 5.3.6 Example Application Study 246 5.3.7 Further Developments 247 5.3.8 Summary 248 5.4 Support Vector Machines 249 5.4.1 Introduction 249 5.4.2 Linearly Separable Two-Class Data 249 5.4.3 Linearly Nonseparable Two-Class Data 253 5.4.4 Multiclass SVMs 256 5.4.5 SVMs for Regression 257 5.4.6 Implementation 259 5.4.7 Example Application Study 262 5.4.8 Summary 263 5.5 Logistic Discrimination 263 5.5.1 Two-Class Case 263 5.5.2 Maximum Likelihood Estimation 264 5.5.3 Multiclass Logistic Discrimination 266 5.5.4 Example Application Study 267 5.5.5 Further Developments 267 5.5.6 Summary 268 5.6 Application Studies 268 5.7 Summary and Discussion 268 5.8 Recommendations 269 5.9 Notes and References 270 Exercises 270 6 Nonlinear Discriminant Analysis – Kernel and Projection Methods 274 6.1 Introduction 274 6.2 Radial Basis Functions 276 6.2.1 Introduction 276 6.2.2 Specifying the Model 278 6.2.3 Specifying the Functional Form 278 6.2.4 The Positions of the Centres 279 6.2.5 Smoothing Parameters 281 6.2.6 Calculation of the Weights 282 6.2.7 Model Order Selection 284 6.2.8 Simple RBF 285 6.2.9 Motivation 286 6.2.10 RBF Properties 288 6.2.11 Example Application Study 288 6.2.12 Further Developments 289 6.2.13 Summary 290 6.3 Nonlinear Support Vector Machines 291 6.3.1 Introduction 291 6.3.2 Binary Classification 291 6.3.3 Types of Kernel 292 6.3.4 Model Selection 293 6.3.5 Multiclass SVMs 294 6.3.6 Probability Estimates 294 6.3.7 Nonlinear Regression 296 6.3.8 Example Application Study 296 6.3.9 Further Developments 297 6.3.10 Summary 298 6.4 The Multilayer Perceptron 298 6.4.1 Introduction 298 6.4.2 Specifying the MLP Structure 299 6.4.3 Determining the MLP Weights 300 6.4.4 Modelling Capacity of the MLP 307 6.4.5 Logistic Classification 307 6.4.6 Example Application Study 310 6.4.7 Bayesian MLP Networks 311 6.4.8 Projection Pursuit 313 6.4.9 Summary 313 6.5 Application Studies 314 6.6 Summary and Discussion 316 6.7 Recommendations 317 6.8 Notes and References 318 Exercises 318 7 Rule and Decision Tree Induction 322 7.1 Introduction 322 7.2 Decision Trees 323 7.2.1 Introduction 323 7.2.2 Decision Tree Construction 326 7.2.3 Selection of the Splitting Rule 327 7.2.4 Terminating the Splitting Procedure 330 7.2.5 Assigning Class Labels to Terminal Nodes 332 7.2.6 Decision Tree Pruning – Worked Example 332 7.2.7 Decision Tree Construction Methods 337 7.2.8 Other Issues 339 7.2.9 Example Application Study 340 7.2.10 Further Developments 341 7.2.11 Summary 342 7.3 Rule Induction 342 7.3.1 Introduction 342 7.3.2 Generating Rules from a Decision Tree 345 7.3.3 Rule Induction Using a Sequential Covering Algorithm 345 7.3.4 Example Application Study 350 7.3.5 Further Developments 351 7.3.6 Summary 351 7.4 Multivariate Adaptive Regression Splines 351 7.4.1 Introduction 351 7.4.2 Recursive Partitioning Model 351 7.4.3 Example Application Study 355 7.4.4 Further Developments 355 7.4.5 Summary 356 7.5 Application Studies 356 7.6 Summary and Discussion 358 7.7 Recommendations 358 7.8 Notes and References 359 Exercises 359 8 Ensemble Methods 361 8.1 Introduction 361 8.2 Characterising a Classifier Combination Scheme 362 8.2.1 Feature Space 363 8.2.2 Level 366 8.2.3 Degree of Training 368 8.2.4 Form of Component Classifiers 368 8.2.5 Structure 369 8.2.6 Optimisation 369 8.3 Data Fusion 370 8.3.1 Architectures 370 8.3.2 Bayesian Approaches 371 8.3.3 Neyman–Pearson Formulation 373 8.3.4 Trainable Rules 374 8.3.5 Fixed Rules 375 8.4 Classifier Combination Methods 376 8.4.1 Product Rule 376 8.4.2 Sum Rule 377 8.4.3 Min, Max and Median Combiners 378 8.4.4 Majority Vote 379 8.4.5 Borda Count 379 8.4.6 Combiners Trained on Class Predictions 380 8.4.7 Stacked Generalisation 382 8.4.8 Mixture of Experts 382 8.4.9 Bagging 385 8.4.10 Boosting 387 8.4.11 Random Forests 389 8.4.12 Model Averaging 390 8.4.13 Summary of Methods 396 8.4.14 Example Application Study 398 8.4.15 Further Developments 399 8.5 Application Studies 399 8.6 Summary and Discussion 400 8.7 Recommendations 401 8.8 Notes and References 401 Exercises 402 9 Performance Assessment 404 9.1 Introduction 404 9.2 Performance Assessment 405 9.2.1 Performance Measures 405 9.2.2 Discriminability 406 9.2.3 Reliability 413 9.2.4 ROC Curves for Performance Assessment 415 9.2.5 Population and Sensor Drift 419 9.2.6 Example Application Study 421 9.2.7 Further Developments 422 9.2.8 Summary 423 9.3 Comparing Classifier Performance 424 9.3.1 Which Technique is Best? 424 9.3.2 Statistical Tests 425 9.3.3 Comparing Rules When Misclassification Costs are Uncertain 426 9.3.4 Example Application Study 428 9.3.5 Further Developments 429 9.3.6 Summary 429 9.4 Application Studies 429 9.5 Summary and Discussion 430 9.6 Recommendations 430 9.7 Notes and References 430 Exercises 431 10 Feature Selection and Extraction 433 10.1 Introduction 433 10.2 Feature Selection 435 10.2.1 Introduction 435 10.2.2 Characterisation of Feature Selection Approaches 439 10.2.3 Evaluation Measures 440 10.2.4 Search Algorithms for Feature Subset Selection 449 10.2.5 Complete Search – Branch and Bound 450 10.2.6 Sequential Search 454 10.2.7 Random Search 458 10.2.8 Markov Blanket 459 10.2.9 Stability of Feature Selection 460 10.2.10 Example Application Study 462 10.2.11 Further Developments 462 10.2.12 Summary 463 10.3 Linear Feature Extraction 463 10.3.1 Principal Components Analysis 464 10.3.2 Karhunen–Lo`eve Transformation 475 10.3.3 Example Application Study 481 10.3.4 Further Developments 482 10.3.5 Summary 483 10.4 Multidimensional Scaling 484 10.4.1 Classical Scaling 484 10.4.2 Metric MDS 486 10.4.3 Ordinal Scaling 487 10.4.4 Algorithms 490 10.4.5 MDS for Feature Extraction 491 10.4.6 Example Application Study 492 10.4.7 Further Developments 493 10.4.8 Summary 493 10.5 Application Studies 493 10.6 Summary and Discussion 495 10.7 Recommendations 495 10.8 Notes and References 496 Exercises 497 11 Clustering 501 11.1 Introduction 501 11.2 Hierarchical Methods 502 11.2.1 Single-Link Method 503 11.2.2 Complete-Link Method 506 11.2.3 Sum-of-Squares Method 507 11.2.4 General Agglomerative Algorithm 508 11.2.5 Properties of a Hierarchical Classification 508 11.2.6 Example Application Study 509 11.2.7 Summary 509 11.3 Quick Partitions 510 11.4 Mixture Models 511 11.4.1 Model Description 511 11.4.2 Example Application Study 512 11.5 Sum-of-Squares Methods 513 11.5.1 Clustering Criteria 514 11.5.2 Clustering Algorithms 515 11.5.3 Vector Quantisation 520 11.5.4 Example Application Study 530 11.5.5 Further Developments 530 11.5.6 Summary 531 11.6 Spectral Clustering 531 11.6.1 Elementary Graph Theory 531 11.6.2 Similarity Matrices 534 11.6.3 Application to Clustering 534 11.6.4 Spectral Clustering Algorithm 535 11.6.5 Forms of Graph Laplacian 535 11.6.6 Example Application Study 536 11.6.7 Further Developments 538 11.6.8 Summary 538 11.7 Cluster Validity 538 11.7.1 Introduction 538 11.7.2 Statistical Tests 539 11.7.3 Absence of Class Structure 540 11.7.4 Validity of Individual Clusters 541 11.7.5 Hierarchical Clustering 542 11.7.6 Validation of Individual Clusterings 542 11.7.7 Partitions 543 11.7.8 Relative Criteria 543 11.7.9 Choosing the Number of Clusters 545 11.8 Application Studies 546 11.9 Summary and Discussion 549 11.10 Recommendations 551 11.11 Notes and References 552 Exercises 553 12 Complex Networks 555 12.1 Introduction 555 12.1.1 Characteristics 557 12.1.2 Properties 557 12.1.3 Questions to Address 559 12.1.4 Descriptive Features 560 12.1.5 Outline 560 12.2 Mathematics of Networks 561 12.2.1 Graph Matrices 561 12.2.2 Connectivity 562 12.2.3 Distance Measures 562 12.2.4 Weighted Networks 563 12.2.5 Centrality Measures 563 12.2.6 Random Graphs 564 12.3 Community Detection 565 12.3.1 Clustering Methods 565 12.3.2 Girvan–Newman Algorithm 568 12.3.3 Modularity Approaches 570 12.3.4 Local Modularity 571 12.3.5 Clique Percolation 573 12.3.6 Example Application Study 574 12.3.7 Further Developments 575 12.3.8 Summary 575 12.4 Link Prediction 575 12.4.1 Approaches to Link Prediction 576 12.4.2 Example Application Study 578 12.4.3 Further Developments 578 12.5 Application Studies 579 12.6 Summary and Discussion 579 12.7 Recommendations 580 12.8 Notes and References 580 Exercises 580 13 Additional Topics 581 13.1 Model Selection 581 13.1.1 Separate Training and Test Sets 582 13.1.2 Cross-Validation 582 13.1.3 The Bayesian Viewpoint 583 13.1.4 Akaike’s Information Criterion 583 13.1.5 Minimum Description Length 584 13.2 Missing Data 585 13.3 Outlier Detection and Robust Procedures 586 13.4 Mixed Continuous and Discrete Variables 587 13.5 Structural Risk Minimisation and the Vapnik–Chervonenkis Dimension 588 13.5.1 Bounds on the Expected Risk 588 13.5.2 The VC Dimension 589 References 591 Index 637

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Book SynopsisPATTERN CLASSIFICATION a unified view of statistical and neural approaches The product of years of research and practical experience in pattern classification, this book offers a theory-based engineering perspective on neural networks and statistical pattern classification. Pattern Classification sheds new light on the relationship between seemingly unrelated approaches to pattern recognition, including statistical methods, polynomial regression, multilayer perceptron, and radial basis functions. Important topics such as feature selection, reject criteria, classifier performance measurement, and classifier combinations are fully covered, as well as material on techniques that, until now, would have required an extensive literature search to locate. A full program of illustrations, graphs, and examples helps make the operations and general properties of different classification approaches intuitively understandable. Offering a lucid presentation of complex appTable of ContentsStatistical Decision Theory. Need for Approximations: Fundamental Approaches. Classification Based on Statistical Models Determined by First-and-Second Order Statistical Moments. Classification Based on Mean-Square Functional Approximations. Polynomial Regression. Multilayer Perceptron Regression. Radial Basis Functions. Measurements, Features, and Feature Section. Reject Criteria and Classifier Performance. Combining Classifiers. Conclusion. STATMOD Program: Description of ftp Package. References. Index.

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Book SynopsisThis book addresses the most efficient methods of pattern analysis using wavelet decomposition. Readers will learn to analyze data in order to emphasize the differences between closely related patterns and then categorize them in a way that is useful to system users.Trade Review"...provides a valuable summary of data reduction." (Technometrics, May 2002) "...effectively describes and summarizes an emerging new field, namely, scientific data modeling and analysis." (Mathematical Reviews, 2003h)Table of ContentsPreface. Acknowledgments. INTRODUCTION. Pattern Analysis as Data Reduction. Vector Spaces and Linear Transformations. OPTIMAL ORTHOGONAL PATTERN REPRESENTATIONS. The Karhunen-Loève Expansion. Additional Theory, Algorithms and Applications. TIME, FREQUENCY AND SCALE ANALYSIS. Fourier Analysis. Wavelet Expansions. ADAPTIVE NONLINEAR MAPPINGS. Radial Basis Functions. Neural Networks. Nonlinear Reduction Architectures. Appendix A Mathemetical Preliminaries. References. Index.

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Book SynopsisSpeech coding has evolved into a highly matured branch of signal processing, with deployment of a plethora of products such as cellular phones, answering machines, communication devices, and more recently, voice over internet protocol (VoIP).Trade Review“…well equipped with exercises and with procedures which are helpful in implementing the coders…” (Zentralblatt Math, Vol.1041, No.16, 2004)Table of ContentsPreface xiii Acronyms xix Notation xxiii 1 Introduction 1 1.1 Overview of Speech Coding 2 1.2 Classification of Speech Coders 8 1.3 Speech Production and Modeling 11 1.4 Some Properties of the Human Auditory System 18 1.5 Speech Coding Standards 22 1.6 About Algorithms 26 1.7 Summary and References 31 2 Signal Processing Techniques 33 2.1 Pitch Period Estimation 33 2.2 All-Pole and All-Zero Filters 45 2.3 Convolution 52 2.4 Summary and References 57 Exercises 57 3 Stochastic Processes and Models 61 3.1 Power Spectral Density 62 3.2 Periodogram 67 3.3 Autoregressive Model 69 3.4 Autocorrelation Estimation 73 3.5 Other Signal Models 85 3.6 Summary and References 86 Exercises 87 4 Linear Prediction 91 4.1 The Problem of Linear Prediction 92 4.2 Linear Prediction Analysis of Nonstationary Signals 96 4.3 Examples of Linear Prediction Analysis of Speech 101 4.4 The Levinson–Durbin Algorithm 107 4.5 The Leroux–Gueguen Algorithm 114 4.6 Long-Term Linear Prediction 120 4.7 Synthesis Filters 127 4.8 Practical Implementation 131 4.9 Moving Average Prediction 137 4.10 Summary and References 138 Exercises 139 5 Scalar Quantization 143 5.1 Introduction 143 5.2 Uniform Quantizer 147 5.3 Optimal Quantizer 149 5.4 Quantizer Design Algorithms 151 5.5 Algorithmic Implementation 155 5.6 Summary and References 158 Exercises 158 6 Pulse Code Modulation and Its Variants 161 6.1 Uniform Quantization 161 6.2 Nonuniform Quantization 166 6.3 Differential Pulse Code Modulation 172 6.4 Adaptive Schemes 175 6.5 Summary and References 180 Exercises 181 7 Vector Quantization 184 7.1 Introduction 185 7.2 Optimal Quantizer 188 7.3 Quantizer Design Algorithms 189 7.4 Multistage VQ 194 7.5 Predictive VQ 216 7.6 Other Structured Schemes 219 7.7 Summary and References 221 Exercises 222 8 Scalar Quantization of Linear Prediction Coefficient 227 8.1 Spectral Distortion 227 8.2 Quantization Based on Reflection Coefficient and Log Area Ratio 232 8.3 Line Spectral Frequency 239 8.4 Quantization Based on Line Spectral Frequency 252 8.5 Interpolation of LPC 256 8.6 Summary and References 258 Exercises 260 9 Linear Prediction Coding 263 9.1 Speech Production Model 264 9.2 Structure of the Algorithm 268 9.3 Voicing Detector 271 9.4 The FS1015 LPC Coder 275 9.5 Limitations of the LPC Model 277 9.6 Summary and References 280 Exercises 281 10 Regular-pulse Excitation Coders 285 10.1 Multipulse Excitation Model 286 10.2 Regular-Pulse-Excited–Long-Term Prediction 289 10.3 Summary and References 295 Exercises 296 11 Code-excited Linear Prediction 299 11.1 The CELP Speech Production Model 300 11.2 The Principle of Analysis-by-Synthesis 301 11.3 Encoding and Decoding 302 11.4 Excitation Codebook Search 308 11.5 Postfilter 317 11.6 Summary and References 325 Exercises 326 12 The Federal Standard Version of CELP 330 12.1 Improving the Long-Term Predictor 331 12.2 The Concept of the Adaptive Codebook 333 12.3 Incorporation of the Adaptive Codebook to the CELP Framework 336 12.4 Stochastic Codebook Structure 338 12.5 Adaptive Codebook Search 341 12.6 Stochastic Codebook Search 344 12.7 Encoder and Decoder 346 12.8 Summary and References 349 Exercises 350 13 Vector Sum Excited Linear Prediction 353 13.1 The Core Encoding Structure 354 13.2 Search Strategies for Excitation Codebooks 356 13.3 Excitation Codebook Searches 357 13.4 Gain Related Procedures 362 13.5 Encoder and Decoder 366 13.6 Summary and References 368 Exercises 369 14 Low-delay CELP 372 14.1 Strategies to Achieve Low Delay 373 14.2 Basic Operational Principles 375 14.3 Linear Prediction Analysis 377 14.4 Excitation Codebook Search 380 14.5 Backward Gain Adaptation 385 14.6 Encoder and Decoder 389 14.7 Codebook Training 391 14.8 Summary and References 393 Exercises 394 15 Vector Quantization of Linear Prediction Coefficient 396 15.1 Correlation Among the LSFs 396 15.2 Split VQ 399 15.3 Multistage VQ 403 15.4 Predictive VQ 407 15.5 Summary and References 418 Exercises 419 16 Algebraic CELP 423 16.1 Algebraic Codebook Structure 424 16.2 Adaptive Codebook 425 16.3 Encoding and Decoding 433 16.4 Algebraic Codebook Search 437 16.5 Gain Quantization Using Conjugate VQ 443 16.6 Other ACELP Standards 446 16.7 Summary and References 451 Exercises 451 17 Mixed Excitation Linear Prediction 454 17.1 The MELP Speech Production Model 455 17.2 Fourier Magnitudes 456 17.3 Shaping Filters 464 17.4 Pitch Period and Voicing Strength Estimation 466 17.5 Encoder Operations 474 17.6 Decoder Operations 477 17.7 Summary and References 481 Exercises 482 18 Source-controlled Variable Bit-rate CELP 486 18.1 Adaptive Rate Decision 487 18.2 LP Analysis and LSF-Related Operations 494 18.3 Decoding and Encoding 496 18.4 Summary and References 498 Exercises 499 19 Speech Quality Assessment 501 19.1 The Scope of Quality and Measuring Conditions 501 19.2 Objective Quality Measurements for Waveform Coders 502 19.3 Subjective Quality Measures 504 19.4 Improvements on Objective Quality Measures 505 Appendix A Minimum-phase Property of the Forward Prediction-error Filter 507 Appendix B Some Properties of Line Spectral Frequency 514 Appendix C Research Directions in Speech Coding 518 Appendix D Linear Combiner for Pattern Classification 522 Appendix E CELP: Optimal Long-term Predictor to Minimize the Weighted Difference 531 Appendix F Review of Linear Algebra: Orthogonality, Basis, Linear Independence, and the Gram–schmidt Algorithm 537 Bibliography 542 Index 553

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Book Synopsis

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Book SynopsisExplainability methods provide an essential toolkit for better understanding model behavior, and this practical guide brings together best-in-class techniques for model explainability.

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Book SynopsisA comprehensive overview of high-performance pattern recognition techniques and approaches to Computational Molecular Biology This book surveys the developments of techniques and approaches on pattern recognition related to Computational Molecular Biology. Providing a broad coverage of the field, the authors cover fundamental and technical information on these techniques and approaches, as well as discussing their related problems. The text consists of twenty nine chapters, organized into seven parts: Pattern Recognition in Sequences, Pattern Recognition in Secondary Structures, Pattern Recognition in Tertiary Structures, Pattern Recognition in Quaternary Structures, Pattern Recognition in Microarrays, Pattern Recognition in Phylogenetic Trees, and Pattern Recognition in Biological Networks. Surveys the development of techniques and approaches on pattern recognition in biomolecular data Discusses pattern recognitTable of ContentsLIST OF CONTRIBUTORS xxi PREFACE xxvii I PATTERN RECOGNITION IN SEQUENCES 1 1 COMBINATORIAL HAPLOTYPING PROBLEMS 3Giuseppe Lancia 1.1 Introduction / 3 1.2 Single Individual Haplotyping / 5 1.2.1 The Minimum Error Correction Model / 8 1.2.2 Probabilistic Approaches and Alternative Models / 10 1.3 Population Haplotyping / 12 1.3.1 Clark’s Rule / 14 1.3.2 Pure Parsimony / 15 1.3.3 Perfect Phylogeny / 19 1.3.4 Disease Association / 21 1.3.5 Other Models / 22 References / 23 2 ALGORITHMIC PERSPECTIVES OF THE STRING BARCODING PROBLEMS 28Sima Behpour and Bhaskar DasGupta 2.1 Introduction / 28 2.2 Summary of Algorithmic Complexity Results for Barcoding Problems / 32 2.2.1 Average Length of Optimal Barcodes / 33 2.3 Entropy-Based Information Content Technique for Designing Approximation Algorithms for String Barcoding Problems / 34 2.4 Techniques for Proving Inapproximability Results for String Barcoding Problems / 36 2.4.1 Reductions from Set Covering Problem / 36 2.4.2 Reduction from Graph-Coloring Problem / 38 2.5 Heuristic Algorithms for String Barcoding Problems / 39 2.5.1 Entropy-Based Method with a Different Measure for Information Content / 39 2.5.2 Balanced Partitioning Approach / 40 2.6 Conclusion / 40 Acknowledgments / 41 References / 41 3 ALIGNMENT-FREE MEASURES FOR WHOLE-GENOME COMPARISON 43Matteo Comin and Davide Verzotto 3.1 Introduction / 43 3.2 Whole-Genome Sequence Analysis / 44 3.2.1 Background on Whole-Genome Comparison / 44 3.2.2 Alignment-Free Methods / 45 3.2.3 Average Common Subword / 46 3.2.4 Kullback–Leibler Information Divergence / 47 3.3 Underlying Approach / 47 3.3.1 Irredundant Common Subwords / 48 3.3.2 Underlying Subwords / 49 3.3.3 Efficient Computation of Underlying Subwords / 50 3.3.4 Extension to Inversions and Complements / 53 3.3.5 A Distance-Like Measure Based on Underlying Subwords / 53 3.4 Experimental Results / 54 3.4.1 Genome Data sets and Reference Taxonomies / 54 3.4.2 Whole-Genome Phylogeny Reconstruction / 56 3.5 Conclusion / 61 Author’s Contributions / 62 Acknowledgments / 62 References / 62 4 A MAXIMUM LIKELIHOOD FRAMEWORK FOR MULTIPLE SEQUENCE LOCAL ALIGNMENT 65Chengpeng Bi 4.1 Introduction / 65 4.2 Multiple Sequence Local Alignment / 67 4.2.1 Overall Objective Function / 67 4.2.2 Maximum Likelihood Model / 68 4.3 Motif Finding Algorithms / 70 4.3.1 DEM Motif Algorithm / 70 4.3.2 WEM Motif Finding Algorithm / 70 4.3.3 Metropolis Motif Finding Algorithm / 72 4.3.4 Gibbs Motif Finding Algorithm / 73 4.3.5 Pseudo-Gibbs Motif Finding Algorithm / 74 4.4 Time Complexity / 75 4.5 Case Studies / 75 4.5.1 Performance Evaluation / 76 4.5.2 CRP Binding Sites / 76 4.5.3 Multiple Motifs in Helix–Turn–Helix Protein Structure / 78 4.6 Conclusion / 80 References / 81 5 GLOBAL SEQUENCE ALIGNMENT WITH A BOUNDED NUMBER OF GAPS 83Carl Barton, Tomáš Flouri, Costas S. Iliopoulos, and Solon P. Pissis 5.1 Introduction / 83 5.2 Definitions and Notation / 85 5.3 Problem Definition / 87 5.4 Algorithms / 88 5.5 Conclusion / 94 References / 95 II PATTERN RECOGNITION IN SECONDARY STRUCTURES 97 6 A SHORT REVIEW ON PROTEIN SECONDARY STRUCTURE PREDICTION METHODS 99Renxiang Yan, Jiangning Song, Weiwen Cai, and Ziding Zhang 6.1 Introduction / 99 6.2 Representative Protein Secondary Structure Prediction Methods / 102 6.2.1 Chou–Fasman / 103 6.2.2 GOR / 104 6.2.3 PHD / 104 6.2.4 PSIPRED / 104 6.2.5 SPINE-X / 105 6.2.6 PSSpred / 105 6.2.7 Meta Methods / 105 6.3 Evaluation of Protein Secondary Structure Prediction Methods / 106 6.3.1 Measures / 106 6.3.2 Benchmark / 106 6.3.3 Performances / 107 6.4 Conclusion / 110 Acknowledgments / 110 References / 111 7 A GENERIC APPROACH TO BIOLOGICAL SEQUENCE SEGMENTATION PROBLEMS: APPLICATION TO PROTEIN SECONDARY STRUCTURE PREDICTION 114Yann Guermeur and Fabien Lauer 7.1 Introduction / 114 7.2 Biological Sequence Segmentation / 115 7.3 MSVMpred / 117 7.3.1 Base Classifiers / 117 7.3.2 Ensemble Methods / 118 7.3.3 Convex Combination / 119 7.4 Postprocessing with A Generative Model / 119 7.5 Dedication to Protein Secondary Structure Prediction / 120 7.5.1 Biological Problem / 121 7.5.2 MSVMpred2 / 121 7.5.3 Hidden Semi-Markov Model / 122 7.5.4 Experimental Results / 122 7.6 Conclusions and Ongoing Research / 125 Acknowledgments / 126 References / 126 8 STRUCTURAL MOTIF IDENTIFICATION AND RETRIEVAL: A GEOMETRICAL APPROACH 129Virginio Cantoni, Marco Ferretti, Mirto Musci, and Nahumi Nugrahaningsih 8.1 Introduction / 129 8.2 A Few Basic Concepts / 130 8.2.1 Hierarchy of Protein Structures / 130 8.2.2 Secondary Structure Elements / 131 8.2.3 Structural Motifs / 132 8.2.4 Available Sources for Protein Data / 134 8.3 State of the Art / 135 8.3.1 Protein Structure Motif Search / 135 8.3.2 Promotif / 136 8.3.3 Secondary-Structure Matching / 137 8.3.4 Multiple Structural Alignment by Secondary Structures / 138 8.4 A Novel Geometrical Approach to Motif Retrieval / 138 8.4.1 Secondary Structures Cooccurrences / 138 8.4.2 Cross Motif Search / 143 8.4.3 Complete Cross Motif Search / 146 8.5 Implementation Notes / 149 8.5.1 Optimizations / 149 8.5.2 Parallel Approaches / 150 8.6 Conclusions and Future Work / 151 Acknowledgment / 152 References / 152 9 GENOME-WIDE SEARCH FOR PSEUDOKNOTTED NONCODING RNAs: A COMPARATIVE STUDY 155Meghana Vasavada, Kevin Byron, Yang Song, and Jason T.L. Wang 9.1 Introduction / 155 9.2 Background / 156 9.2.1 Noncoding RNAs and Their Secondary Structures / 156 9.2.2 Pseudoknotted ncRNA Search Tools / 157 9.3 Methodology / 157 9.4 Results and Interpretation / 161 9.5 Conclusion / 162 References / 163 III PATTERN RECOGNITION IN TERTIARY STRUCTURES 165 10 MOTIF DISCOVERY IN PROTEIN 3D-STRUCTURES USING GRAPH MINING TECHNIQUES 167Wajdi Dhifli and Engelbert Mephu Nguifo 10.1 Introduction / 167 10.2 From Protein 3D-Structures to Protein Graphs / 169 10.2.1 Parsing Protein 3D-Structures into Graphs / 169 10.3 Graph Mining / 172 10.4 Subgraph Mining / 173 10.5 Frequent Subgraph Discovery / 173 10.5.1 Problem Definition / 174 10.5.2 Candidates Generation / 176 10.5.3 Frequent Subgraph Discovery Approaches / 177 10.5.4 Variants of Frequent Subgraph Mining: Closed and Maximal Subgraphs / 178 10.6 Feature Selection / 179 10.6.1 Relevance of a Feature / 179 10.7 Feature Selection for Subgraphs / 180 10.7.1 Problem Statement / 180 10.7.2 Mining Top-k Subgraphs / 180 10.7.3 Clustering-Based Subgraph Selection / 181 10.7.4 Sampling-Based Approaches / 181 10.7.5 Approximate Subgraph Mining / 181 10.7.6 Discriminative Subgraph Selection / 182 10.7.7 Other Significant Subgraph Selection Approaches / 182 10.8 Discussion / 183 10.9 Conclusion / 185 Acknowledgments / 185 References / 186 11 FUZZY AND UNCERTAIN LEARNING TECHNIQUES FOR THE ANALYSIS AND PREDICTION OF PROTEIN TERTIARY STRUCTURES 190Chinua Umoja, Xiaxia Yu, and Robert Harrison 11.1 Introduction / 190 11.2 Genetic Algorithms / 192 11.2.1 GA Model Selection in Protein Structure Prediction / 196 11.2.2 Common Methodology / 198 11.3 Supervised Machine Learning Algorithm / 201 11.3.1 Artificial Neural Networks / 201 11.3.2 ANNs in Protein Structure Prediction / 202 11.3.3 Support Vector Machines / 203 11.4 Fuzzy Application / 204 11.4.1 Fuzzy Logic / 204 11.4.2 Fuzzy SVMs / 204 11.4.3 Adaptive-Network-Based Fuzzy Inference Systems / 205 11.4.4 Fuzzy Decision Trees / 206 11.5 Conclusion / 207 References / 208 12 PROTEIN INTER-DOMAIN LINKER PREDICTION 212Maad Shatnawi, Paul D. Yoo, and Sami Muhaidat 12.1 Introduction / 212 12.2 Protein Structure Overview / 213 12.3 Technical Challenges and Open Issues / 214 12.4 Prediction Assessment / 215 12.5 Current Approaches / 216 12.5.1 DomCut / 216 12.5.2 Scooby-Domain / 217 12.5.3 FIEFDom / 218 12.5.4 Chatterjee et al. (2009) / 219 12.5.5 Drop / 219 12.6 Domain Boundary Prediction Using Enhanced General Regression Network / 220 12.6.1 Multi-Domain Benchmark Data Set / 220 12.6.2 Compact Domain Profile / 221 12.6.3 The Enhanced Semi-Parametric Model / 222 12.6.4 Training, Testing, and Validation / 225 12.6.5 Experimental Results / 226 12.7 Inter-Domain Linkers Prediction Using Compositional Index and Simulated Annealing / 227 12.7.1 Compositional Index / 228 12.7.2 Detecting the Optimal Set of Threshold Values Using Simulated Annealing / 229 12.7.3 Experimental Results / 230 12.8 Conclusion / 232 References / 233 13 PREDICTION OF PROLINE CIS–TRANS ISOMERIZATION 236Paul D. Yoo, Maad Shatnawi, Sami Muhaidat, Kamal Taha, and Albert Y. Zomaya 13.1 Introduction / 236 13.2 Methods / 238 13.2.1 Evolutionary Data Set Construction / 238 13.2.2 Protein Secondary Structure Information / 239 13.2.3 Method I: Intelligent Voting / 239 13.2.4 Method II: Randomized Meta-Learning / 241 13.2.5 Model Validation and Testing / 242 13.2.6 Parameter Tuning / 242 13.3 Model Evaluation and Analysis / 243 13.4 Conclusion / 245 References / 245 IV PATTERN RECOGNITION IN QUATERNARY STRUCTURES 249 14 PREDICTION OF PROTEIN QUATERNARY STRUCTURES 251Akbar Vaseghi, Maryam Faridounnia, Soheila Shokrollahzade, Samad Jahandideh, and Kuo-Chen Chou 14.1 Introduction / 251 14.2 Protein Structure Prediction / 255 14.2.1 Secondary Structure Prediction / 255 14.2.2 Modeling of Tertiary Structure / 256 14.3 Template-Based Predictions / 257 14.3.1 Homology Modeling / 257 14.3.2 Threading Methods / 257 14.3.3 Ab initio Modeling / 257 14.4 Critical Assessment of Protein Structure Prediction / 258 14.5 Quaternary Structure Prediction / 258 14.6 Conclusion / 261 Acknowledgments / 261 References / 261 15 COMPARISON OF PROTEIN QUATERNARY STRUCTURES BY GRAPH APPROACHES 266Sheng-Lung Peng and Yu-Wei Tsay 15.1 Introduction / 266 15.2 Similarity in the Graph Model / 268 15.2.1 Graph Model for Proteins / 270 15.3 Measuring Structural Similarity VIA MCES / 272 15.3.1 Problem Formulation / 273 15.3.2 Constructing P-Graphs / 274 15.3.3 Constructing Line Graphs / 276 15.3.4 Constructing Modular Graphs / 276 15.3.5 Maximum Clique Detection / 277 15.3.6 Experimental Results / 277 15.4 Protein Comparison VIA Graph Spectra / 279 15.4.1 Graph Spectra / 279 15.4.2 Matrix Selection / 281 15.4.3 Graph Cospectrality and Similarity / 283 15.4.4 Cospectral Comparison / 283 15.4.5 Experimental Results / 284 15.5 Conclusion / 287 References / 287 16 STRUCTURAL DOMAINS IN PREDICTION OF BIOLOGICAL PROTEIN–PROTEIN INTERACTIONS 291Mina Maleki, Michael Hall, and Luis Rueda 16.1 Introduction / 291 16.2 Structural Domains / 293 16.3 The Prediction Framework / 293 16.4 Feature Extraction and Prediction Properties / 294 16.4.1 Physicochemical Properties / 296 16.4.2 Domain-Based Properties / 298 16.5 Feature Selection / 299 16.5.1 Filter Methods / 299 16.5.2 Wrapper Methods / 301 16.6 Classification / 301 16.6.1 Linear Dimensionality Reduction / 301 16.6.2 Support Vector Machines / 303 16.6.3 k-Nearest Neighbor / 303 16.6.4 Naive Bayes / 304 16.7 Evaluation and Analysis / 304 16.8 Results and Discussion / 304 16.8.1 Analysis of the Prediction Properties / 304 16.8.2 Analysis of Structural DDIs / 307 16.9 Conclusion / 309 References / 310 V PATTERN RECOGNITION IN MICROARRAYS 315 17 CONTENT-BASED RETRIEVAL OF MICROARRAY EXPERIMENTS 317Hasan O¢gul 17.1 Introduction / 317 17.2 Information Retrieval: Terminology and Background / 318 17.3 Content-Based Retrieval / 320 17.4 Microarray Data and Databases / 322 17.5 Methods for Retrieving Microarray Experiments / 324 17.6 Similarity Metrics / 327 17.7 Evaluating Retrieval Performance / 329 17.8 Software Tools / 330 17.9 Conclusion and Future Directions / 331 Acknowledgment / 332 References / 332 18 EXTRACTION OF DIFFERENTIALLY EXPRESSED GENES IN MICROARRAY DATA 335Tiratha Raj Singh, Brigitte Vannier, and Ahmed Moussa 18.1 Introduction / 335 18.2 From Microarray Image to Signal / 336 18.2.1 Signal from Oligo DNA Array Image / 336 18.2.2 Signal from Two-Color cDNA Array / 337 18.3 Microarray Signal Analysis / 337 18.3.1 Absolute Analysis and Replicates in Microarrays / 338 18.3.2 Microarray Normalization / 339 18.4 Algorithms for De Gene Selection / 339 18.4.1 Within–Between DE Gene (WB-DEG) Selection Algorithm / 340 18.4.2 Comparison of the WB-DEGs with Two Classical DE Gene Selection Methods on Latin Square Data / 341 18.5 Gene Ontology Enrichment and Gene Set Enrichment Analysis / 343 18.6 Conclusion / 345 References / 345 19 CLUSTERING AND CLASSIFICATION TECHNIQUES FOR GENE EXPRESSION PROFILE PATTERN ANALYSIS 347Emanuel Weitschek, Giulia Fiscon, Valentina Fustaino, Giovanni Felici, and Paola Bertolazzi 19.1 Introduction / 347 19.2 Transcriptome Analysis / 348 19.3 Microarrays / 349 19.3.1 Applications / 349 19.3.2 Microarray Technology / 350 19.3.3 Microarray Workflow / 350 19.4 RNA-Seq / 351 19.5 Benefits and Drawbacks of RNA-Seq and Microarray Technologies / 353 19.6 Gene Expression Profile Analysis / 356 19.6.1 Data Definition / 356 19.6.2 Data Analysis / 357 19.6.3 Normalization and Background Correction / 357 19.6.4 Genes Clustering / 359 19.6.5 Experiment Classification / 361 19.6.6 Software Tools for Gene Expression Profile Analysis / 362 19.7 Real Case Studies / 364 19.8 Conclusions / 367 References / 368 20 MINING INFORMATIVE PATTERNS IN MICROARRAY DATA 371Li Teng 20.1 Introduction / 371 20.2 Patterns with Similarity / 373 20.2.1 Similarity Measurement / 374 20.2.2 Clustering / 376 20.2.3 Biclustering / 379 20.2.4 Types of Biclusters / 380 20.2.5 Measurement of the Homogeneity / 383 20.2.6 Biclustering Algorithms with Different Searching Schemes / 387 20.3 Conclusion / 391 References / 391 21 ARROW PLOT AND CORRESPONDENCE ANALYSIS MAPS FOR VISUALIZING THE EFFECTS OF BACKGROUND CORRECTION AND NORMALIZATION METHODS ON MICROARRAY DATA 394Carina Silva, Adelaide Freitas, Sara Roque, and Lisete Sousa 21.1 Overview / 394 21.1.1 Background Correction Methods / 395 21.1.2 Normalization Methods / 396 21.1.3 Literature Review / 397 21.2 Arrow Plot / 399 21.2.1 DE Genes Versus Special Genes / 399 21.2.2 Definition and Properties of the ROC Curve / 400 21.2.3 AUC and Degenerate ROC Curves / 401 21.2.4 Overlapping Coefficient / 402 21.2.5 Arrow Plot Construction / 403 21.3 Significance Analysis of Microarrays / 404 21.4 Correspondence Analysis / 405 21.4.1 Basic Principles / 405 21.4.2 Interpretation of CA Maps / 406 21.5 Impact of the Preprocessing Methods / 407 21.5.1 Class Prediction Context / 408 21.5.2 Class Comparison Context / 408 21.6 Conclusions / 412 Acknowledgments / 413 References / 413 VI PATTERN RECOGNITION IN PHYLOGENETIC TREES 417 22 PATTERN RECOGNITION IN PHYLOGENETICS: TREES AND NETWORKS 419David A. Morrison 22.1 Introduction / 419 22.2 Networks and Trees / 420 22.3 Patterns and Their Processes / 424 22.4 The Types of Patterns / 427 22.5 Fingerprints / 431 22.6 Constructing Networks / 433 22.7 Multi-Labeled Trees / 435 22.8 Conclusion / 436 References / 437 23 DIVERSE CONSIDERATIONS FOR SUCCESSFUL PHYLOGENETIC TREE RECONSTRUCTION: IMPACTS FROM MODEL MISSPECIFICATION, RECOMBINATION, HOMOPLASY, AND PATTERN RECOGNITION 439Diego Mallo, Agustín Sánchez-Cobos, and Miguel Arenas 23.1 Introduction / 440 23.2 Overview on Methods and Frameworks for Phylogenetic Tree Reconstruction / 440 23.2.1 Inferring Gene Trees / 441 23.2.2 Inferring Species Trees / 442 23.3 Influence of Substitution Model Misspecification on Phylogenetic Tree Reconstruction / 445 23.4 Influence of Recombination on Phylogenetic Tree Reconstruction / 446 23.5 Influence of Diverse Evolutionary Processes on Species Tree Reconstruction / 447 23.6 Influence of Homoplasy on Phylogenetic Tree Reconstruction: The Goals of Pattern Recognition / 449 23.7 Concluding Remarks / 449 Acknowledgments / 450 References / 450 24 AUTOMATED PLAUSIBILITY ANALYSIS OF LARGE PHYLOGENIES 457David Dao, Tomáš Flouri, and Alexandros Stamatakis 24.1 Introduction / 457 24.2 Preliminaries / 459 24.3 A Naïve Approach / 462 24.4 Toward a Faster Method / 463 24.5 Improved Algorithm / 467 24.5.1 Preprocessing / 467 24.5.2 Computing Lowest Common Ancestors / 468 24.5.3 Constructing the Induced Tree / 468 24.5.4 Final Remarks / 471 24.6 Implementation / 473 24.6.1 Preprocessing / 473 24.6.2 Reconstruction / 473 24.6.3 Extracting Bipartitions / 474 24.7 Evaluation / 474 24.7.1 Test Data Sets / 474 24.7.2 Experimental Results / 475 24.8 Conclusion / 479 Acknowledgment / 481 References / 481 25 A NEW FAST METHOD FOR DETECTING AND VALIDATING HORIZONTAL GENE TRANSFER EVENTS USING PHYLOGENETIC TREES AND AGGREGATION FUNCTIONS 483Dunarel Badescu, Nadia Tahiri, and Vladimir Makarenkov 25.1 Introduction / 483 25.2 Methods / 485 25.2.1 Clustering Using Variability Functions / 485 25.2.2 Other Variants of Clustering Functions Implemented in the Algorithm / 487 25.2.3 Description of the New Algorithm / 488 25.2.4 Time Complexity / 491 25.3 Experimental Study / 491 25.3.1 Implementation / 491 25.3.2 Synthetic Data / 491 25.3.3 Real Prokaryotic (Genomic) Data / 495 25.4 Results and Discussion / 501 25.4.1 Analysis of Synthetic Data / 501 25.4.2 Analysis of Prokaryotic Data / 502 25.5 Conclusion / 502 References / 503 VII PATTERN RECOGNITION IN BIOLOGICAL NETWORKS 505 26 COMPUTATIONAL METHODS FOR MODELING BIOLOGICAL INTERACTION NETWORKS 507Christos Makris and Evangelos Theodoridis 26.1 Introduction / 507 26.2 Measures/Metrics / 508 26.3 Models of Biological Networks / 511 26.4 Reconstructing and Partitioning Biological Networks / 511 26.5 PPI Networks / 513 26.6 Mining PPI Networks—Interaction Prediction / 517 26.7 Conclusions / 519 References / 519 27 BIOLOGICAL NETWORK INFERENCE AT MULTIPLE SCALES: FROM GENE REGULATION TO SPECIES INTERACTIONS 525Andrej Aderhold, V Anne Smith, and Dirk Husmeier 27.1 Introduction / 525 27.2 Molecular Systems / 528 27.3 Ecological Systems / 528 27.4 Models and Evaluation / 529 27.4.1 Notations / 529 27.4.2 Sparse Regression and the LASSO / 530 27.4.3 Bayesian Regression / 530 27.4.4 Evaluation Metric / 531 27.5 Learning Gene Regulation Networks / 532 27.5.1 Nonhomogeneous Bayesian Regression / 533 27.5.2 Gradient Estimation / 534 27.5.3 Simulated Bio-PEPA Data / 534 27.5.4 Real mRNA Expression Profile Data / 535 27.5.5 Method Evaluation and Learned Networks / 536 27.6 Learning Species Interaction Networks / 540 27.6.1 Regression Model of Species interactions / 540 27.6.2 Multiple Global Change-Points / 541 27.6.3 Mondrian Process Change-Points / 542 27.6.4 Synthetic Data / 544 27.6.5 Simulated Population Dynamics / 544 27.6.6 Real World Plant Data / 546 27.6.7 Method Evaluation and Learned Networks / 546 27.7 Conclusion / 550 References / 550 28 DISCOVERING CAUSAL PATTERNS WITH STRUCTURAL EQUATION MODELING: APPLICATION TO TOLL-LIKE RECEPTOR SIGNALING PATHWAY IN CHRONIC LYMPHOCYTIC LEUKEMIA 555Athina Tsanousa, Stavroula Ntoufa, Nikos Papakonstantinou, Kostas Stamatopoulos, and Lefteris Angelis 28.1 Introduction / 555 28.2 Toll-Like Receptors / 557 28.2.1 Basics / 557 28.2.2 Structure and Signaling of TLRs / 558 28.2.3 TLR Signaling in Chronic Lymphocytic Leukemia / 559 28.3 Structural Equation Modeling / 560 28.3.1 Methodology of SEM Modeling / 560 28.3.2 Assumptions / 561 28.3.3 Estimation Methods / 562 28.3.4 Missing Data / 562 28.3.5 Goodness-of-Fit Indices / 563 28.3.6 Other Indications of a Misspecified Model / 565 28.4 Application / 566 28.5 Conclusion / 580 References / 581 29 ANNOTATING PROTEINS WITH INCOMPLETE LABEL INFORMATION 585Guoxian Yu, Huzefa Rangwala, and Carlotta Domeniconi 29.1 Introduction / 585 29.2 Related Work / 587 29.3 Problem Formulation / 589 29.3.1 The Algorithm / 591 29.4 Experimental Setup / 592 29.4.1 Data sets / 592 29.4.2 Comparative Methods / 593 29.4.3 Experimental Protocol / 594 29.4.4 Evaluation Criteria / 594 29.5 Experimental Analysis / 596 29.5.1 Replenishing Missing Functions / 596 29.5.2 Predicting Unlabeled Proteins / 600 29.5.3 Component Analysis / 604 29.5.4 Run Time Analysis / 604 29.6 Conclusions / 605 Acknowledgments / 606 References / 606 INDEX 609

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Book SynopsisA new approach to the issue of data quality in pattern recognition Detailing foundational concepts before introducing more complex methodologies and algorithms, this book is a self-contained manual for advanced data analysis and data mining. Top-down organization presents detailed applications only after methodological issues have been mastered, and step-by-step instructions help ensure successful implementation of new processes. By positioning data quality as a factor to be dealt with rather than overcome, the framework provided serves as a valuable, versatile tool in the analysis arsenal. For decades, practical need has inspired intense theoretical and applied research into pattern recognition for numerous and diverse applications. Throughout, the limiting factor and perpetual problem has been dataits sheer diversity, abundance, and variable quality presents the central challenge to pattern recognition innovation. Pattern Recognition: A Quality of Data PersTable of ContentsPREFACE ix PART 1 FUNDAMENTALS 1 CHAPTER 1 PATTERN RECOGNITION: FEATURE SPACE CONSTRUCTION 3 1.1 Concepts 3 1.2 From Patterns to Features 8 1.3 Features Scaling 17 1.4 Evaluation and Selection of Features 23 1.5 Conclusions 47 Appendix 1.A 48 Appendix 1.B 50 References 50 CHAPTER 2 PATTERN RECOGNITION: CLASSIFIERS 53 2.1 Concepts 53 2.2 Nearest Neighbors Classification Method 55 2.3 Support Vector Machines Classification Algorithm 57 2.4 Decision Trees in Classification Problems 65 2.5 Ensemble Classifiers 78 2.6 Bayes Classifiers 82 2.7 Conclusions 97 References 97 CHAPTER 3 CLASSIFICATION WITH REJECTION PROBLEM FORMULATION AND AN OVERVIEW 101 3.1 Concepts 102 3.2 The Concept of Rejecting Architectures 107 3.3 Native Patterns-Based Rejection 112 3.4 Rejection Option in the Dataset of Native Patterns: A Case Study 118 3.5 Conclusions 129 References 130 CHAPTER 4 EVALUATING PATTERN RECOGNITION PROBLEM 133 4.1 Evaluating Recognition with Rejection: Basic Concepts 133 4.2 Classification with Rejection with No Foreign Patterns 145 4.3 Classification with Rejection: Local Characterization 149 4.4 Conclusions 156 References 156 CHAPTER 5 RECOGNITION WITH REJECTION: EMPIRICAL ANALYSIS 159 5.1 Experimental Results 160 5.2 Geometrical Approach 175 5.3 Conclusions 191 References 192 PART 2 ADVANCED TOPICS: A FRAMEWORK OF GRANULAR COMPUTING 195 CHAPTER 6 CONCEPTS AND NOTIONS OF INFORMATION GRANULES 197 6.1 Information Granularity and Granular Computing 197 6.2 Formal Platforms of Information Granularity 201 6.3 Intervals and Calculus of Intervals 205 6.4 Calculus of Fuzzy Sets 208 6.5 Characterization of Information Granules: Coverage and Specificity 216 6.6 Matching Information Granules 219 6.7 Conclusions 220 References 221 CHAPTER 7 INFORMATION GRANULES: FUNDAMENTAL CONSTRUCTS 223 7.1 The Principle of Justifiable Granularity 223 7.2 Information Granularity as a Design Asset 230 7.3 Single-Step and Multistep Prediction of Temporal Data in Time Series Models 235 7.4 Development of Granular Models of Higher Type 236 7.5 Classification with Granular Patterns 241 7.6 Conclusions 245 References 246 CHAPTER 8 CLUSTERING 247 8.1 Fuzzy C-Means Clustering Method 247 8.2 k-Means Clustering Algorithm 252 8.3 Augmented Fuzzy Clustering with Clusters and Variables Weighting 253 8.4 Knowledge-Based Clustering 254 8.5 Quality of Clustering Results 254 8.6 Information Granules and Interpretation of Clustering Results 256 8.7 Hierarchical Clustering 258 8.8 Information Granules in Privacy Problem: A Concept of Microaggregation 261 8.9 Development of Information Granules of Higher Type 262 8.10 Experimental Studies 264 8.11 Conclusions 272 References 273 CHAPTER 9 QUALITY OF DATA: IMPUTATION AND DATA BALANCING 275 9.1 Data Imputation: Underlying Concepts and Key Problems 275 9.2 Selected Categories of Imputation Methods 276 9.3 Imputation with the Use of Information Granules 278 9.4 Granular Imputation with the Principle of Justifiable Granularity 279 9.5 Granular Imputation with Fuzzy Clustering 283 9.6 Data Imputation in System Modeling 285 9.7 Imbalanced Data and their Granular Characterization 286 9.8 Conclusions 291 References 291 INDEX 293

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Book SynopsisImage Segmentation Summarizes and improves new theory, methods, and applications of current image segmentation approaches, written by leaders in the field The process of image segmentation divides an image into different regions based on the characteristics of pixels, resulting in a simplified image that can be more efficiently analyzed. Image segmentation has wide applications in numerous fields ranging from industry detection and bio-medicine to intelligent transportation and architecture. Image Segmentation: Principles, Techniques, and Applications is an up-to-date collection of recent techniques and methods devoted to the field of computer vision. Covering fundamental concepts, new theories and approaches, and a variety of practical applications including medical imaging, remote sensing, fuzzy clustering, and watershed transform. In-depth chapters present innovative methods developed by the authorssuch as convolutional neural networks, graph convolutional networks, deformable convolution, and model compressionto assist graduate students and researchers apply and improve image segmentation in their work. Describes basic principles of image segmentation and related mathematical methods such as clustering, neural networks, and mathematical morphology. Introduces new methods for achieving rapid and accurate image segmentation based on classic image processing and machine learning theory. Presents techniques for improved convolutional neural networks for scene segmentation, object recognition, and change detection, etc. Highlights the effect of image segmentation in various application scenarios such as traffic image analysis, medical image analysis, remote sensing applications, and material analysis, etc. Image Segmentation: Principles, Techniques, and Applications is an essential resource for undergraduate and graduate courses such as image and video processing, computer vision, and digital signal processing, as well as researchers working in computer vision and image analysis looking to improve their techniques and methods.Table of ContentsPreface About the Authors List of Abbreviations Part One: Principle 1 Introduction to Image Segmentation 2 Principles of Clustering 3 Principles of Mathematical Morphology 4 Principles of Neural Network Part Two: Methods 5 Fast and Robust Image Segmentation Using Clustering 6 Fast Image Segmentation Using Watershed Transform 7 Superpixel-based Fast Image Segmentation Part Three: Application 8 Image Segmentation for Traffic Scene Analysis 9 Image Segmentation for Medical Analysis 10 Image Segmentation for Remote Sensing Analysis 11 Image Segmentation for Material Analysis

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Book Synopsis Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You''ll see the OpenCV algorithms and how to use them for image processing.  The next section looks at advanced machine learning and deep learning methods for image processing and classification. You''ll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you''ll explore how models are made in real time and then deployed using various DevOps tools.  All the concepTable of ContentsChapter 1: Installation and Environment Setup Chapter Goal: Making System Ready for Image Processing and Analysis No of pages 20 Sub -Topics (Top 2) 1. Installing Jupyter Notebook 2. Installing OpenCV and other Image Analysis dependencies 3. Installing Neural Network Dependencies Chapter 2: Introduction to Python and Image Processing Chapter Goal: Introduction to different concepts of Python and Image processing Application on it. No of pages: 50 Sub - Topics (Top 2) 1. Essentials of Python 2. Terminologies related to Image Analysis Chapter 3: Advanced Image Processing using OpenCV Chapter Goal: Understanding Algorithms and their applications using Python No of pages: 100 Sub - Topics (Top 2): 1. Operations on Images 2. Image Transformations Chapter 4: Machine Learning Approaches in Image Processing Chapter Goal: Basic Implementation of Machine and Deep Learning Models, which takes care of Image Processing, before applications in real-time scenario No of pages: 100 Sub - Topics (Top 2): 1. Image Classification and Segmentation 2. Applying Supervised and Unsupervised Learning approaches on Images using Python Chapter 5: Real Time Use Cases Chapter Goal: Working on 5 projects using Python, applying all the concepts learned in this book No of pages: 100 Sub - Topics (Top 5): 1. Facial Detection 2. Facial Recognition 3. Hand Gesture Movement Recognition 4. Self-Driving Cars Conceptualization: Advanced Lane Finding 5. Self-Driving Cars Conceptualization: Traffic Signs Detection Chapter 6: Appendix A Chapter Goal: Advanced concepts Introduction No of pages: 50 Sub - Topics (Top 2): 1. AdaBoost and XGBoost 2. Pulse Coupled Neural Networks

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Book SynopsisIn the second edition of this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world datasets together to teach you how to approach analytics problems by example.

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Book SynopsisChange Detection and Image Time Series Analysis 2 presents supervised machine-learning-based methods for temporal evolution analysis by using image time series associated with Earth observation data. Chapter 1 addresses the fusion of multisensor, multiresolution and multitemporal data. It proposes two supervised solutions that are based on a Markov random field: the first relies on a quad-tree and the second is specifically designed to deal with multimission, multifrequency and multiresolution time series.Chapter 2 provides an overview of pixel based methods for time series classification, from the earliest shallow learning methods to the most recent deep-learning-based approaches.Chapter 3 focuses on very high spatial resolution data time series and on the use of semantic information for modeling spatio-temporal evolution patterns.Chapter 4 centers on the challenges of dense time series analysis, including pre processing aspects and a taxonomy of existing methodologies. Finally, since the evaluation of a learning system can be subject to multiple considerations,Chapters 5 and 6 offer extensive evaluations of the methodologies and learning frameworks used to produce change maps, in the context of multiclass and/or multilabel change classification issues.Table of ContentsContents Preface ix Abdourrahmane M. ATTO, Francesca BOVOLO and Lorenzo BRUZZONE List of Notations Chapter 1 Hierarchical Markov Random Fields for High Resolution Land Cover Classification of Multisensor and Multiresolution Image Time Series 1 Ihsen HEDHLI, Gabriele MOSER, Sebastiano B. SERPICO and Josiane ZERUBIA 1.1. Introduction 1 1.1.1. The role of multisensor data in time series classification 1 1.1.2. Multisensor and multiresolution classification 2 1.1.3.Previouswork 5 1.2. Methodology 9 1.2.1. Overview of the proposed approaches 9 1.2.2. Hierarchical model associated with the first proposed method 10 1.2.3. Hierarchical model associated with the second proposed method 13 1.2.4. Multisensor hierarchical MPM inference 14 1.2.5. Probability density estimation through finite mixtures 17 1.3.Examplesofexperimentalresults 19 1.3.1.Resultsofthefirstmethod 19 1.3.2.Resultsofthesecondmethod 22 1.4.Conclusion 26 xiii 1.5.Acknowledgments 26 1.6.References 27 Chapter 2 Pixel-based Classification Techniques for Satellite Image Time Series 33 Charlotte PELLETIER and Silvia VALERO 2.1. Introduction 33 2.2. Basic concepts in supervised remote sensing classification 35 2.2.1. Preparing data before it is fed into classification algorithms 35 2.2.2. Key considerations when training supervised classifiers 39 2.2.3. Performance evaluation of supervised classifiers 41 2.3.Traditionalclassificationalgorithms 45 2.3.1. Support vector machines 45 2.3.2. Random forests 51 2.3.3. k-nearest neighbor 56 2.4. Classification strategies based on temporal feature representations 59 2.4.1. Phenology-based classification approaches 60 2.4.2 Dictionary-based classificationapproaches 61 2.4.3 Shapelet-based classificationapproaches 62 2.5.Deeplearningapproaches 63 2.5.1. Introduction to deep learning 64 2.5.2.Convolutionalneuralnetworks 68 2.5.3.Recurrentneuralnetworks 71 2.6.References 75 Chapter 3 Semantic Analysis of Satellite Image Time Series 85 Corneliu Octavian DUMITRU and Mihai DATCU 3.1. Introduction 85 3.1.1.TypicalSITSexamples 89 3.1.2. Irregular acquisitions 90 3.1.3.Thechapterstructure 96 3.2.WhyaresemanticsneededinSITS? 96 3.3.Similaritymetrics 97 3.4. Feature methods 98 3.5. Classification methods 98 3.5.1.Activelearning 99 3.5.2.Relevancefeedback 100 3.5.3. Compression-based pattern recognition 100 3.5.4.LatentDirichletallocation 101 3.6.Conclusion 102 vii 3.7.Acknowledgments 105 3.8.References 105 Chapter 4 Optical Satellite Image Time Series Analysis for Environment Applications: From Classical Methods to Deep Learning and Beyond 109 Matthieu MOLINIER, Jukka MIETTINEN,DinoIENCO,ShiQIU and Zhe ZHU 4.1. Introduction 109 4.2. Annual time series 111 4.2.1. Overview of annual time series methods 111 4.2.2 Examples of annual times series analysis applications for environmentalmonitoring 112 4.2.3.Towardsdensetimeseriesanalysis 116 4.3. Dense time series analysis using all available data 117 4.3.1. Making dense time series consistent 118 4.3.2. Change detection methods 121 4.3.3.Summaryandfuturedevelopments 125 4.4. Deep learning-based time series analysis approaches 126 4.4.1 Recurrent Neural Network (RNN) for Satellite Image TimeSeries 129 4.4.2 Convolutional Neural Networks (CNN) for Satellite Image TimeSeries 131 4.4.3. Hybrid models: Convolutional Recurrent Neural Network (ConvRNN) models for Satellite Image Time Series 134 4.4.4. Synthesis and future developments 136 4.5. Beyond satellite image time series and deep learning: convergence between time series and video approaches 136 4.5.1 Increased image acquisition frequency: from time series to spacebornetime-lapseandvideos 137 4.5.2. Deep learning and computer vision as technology enablers 138 4.5.3.Futuresteps 139 4.6.References 140 Chapter 5 A Review on Multi-temporal Earthquake Damage Assessment Using Satellite Images 155 Gülşen TAŞKIN, EsraERTEN and Enes Oğuzhan ALATAŞ 5.1. Introduction 155 5.1.1. Research methodology and statistics 159 5.2. Satellite-based earthquake damage assessment 165 5.3. Pre-processing of satellite images before damage assessment 167 5.4. Multi-source image analysis 168 5.5. Contextual feature mining for damage assessment 169 5.5.1.Texturalfeatures 170 5.5.2. Filter-based methods 173 5.6. Multi-temporal image analysis for damage assessment 175 5.6.1. Use of machine learning in damage assessment problem 176 5.6.2. Rapid earthquake damage assessment 180 5.7. Understanding damage following an earthquake using satellite-based SAR 181 5.7.1. SAR fundamental parameters and acquisition vector 185 5.7.2. Coherent methods for damage assessment 188 5.7.3. Incoherent methods for damage assessment 192 5.7.4. Post-earthquake-only SAR data-based damage assessment 195 5.7.5 Combination of coherent and incoherent methods for damage assessment 196 5.7.6.Summary 198 5.8. Use of auxiliary data sources 200 5.9.Damagegrades 200 5.10.Conclusionanddiscussion 203 5.11.References 205 Chapter 6 Multiclass Multilabel Change of State Transfer Learning from Image Time Series 223 Abdourrahmane M. ATTO,HélaHADHRI, FlavienVERNIER and Emmanuel TROUVÉ 6.1. Introduction 223 6.2. Coarse- to fine-grained change of state dataset 225 6.3. Deep transfer learning models for change of state classification 232 6.3.1.Deeplearningmodellibrary 232 6.3.2.GraphstructuresfortheCNNlibrary 234 6.3.3. Dimensionalities of the learnables for the CNN library 236 6.4.Changeofstateanalysis 237 6.4.1 Transfer learning adaptations for the change of state classificationissues 238 6.4.2.Experimentalresults 239 6.5.Conclusion 243 6.6.Acknowledgments 244 6.7.References 244 List of Authors 247 Index 249 Summary of Volume 1 253

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Book SynopsisStereoscopic processes are increasingly used in virtual reality and entertainment. This technology is interesting because it allows for a quick immersion of the user, especially in terms of depth perception and relief clues. However, these processes tend to cause stress on the visual system if used over a prolonged period of time, leading some to question the cause of side effects that these systems generate in their users, such as eye fatigue. This book explores the mechanisms of depth perception with and without stereoscopy and discusses the indices which are involved in the depth perception. The author describes the techniques used to capture and retransmit stereoscopic images. The causes of eyestrain related to these images are then presented along with their consequences in the long and short term. The study of the causes of eyestrain forms the basis for an improvement in these processes in the hopes of developing mechanisms for easier virtual viewing.Table of ContentsAcknowledgments ix Introduction xi Chapter 1. Principles of Depth and Shape Perception 1 1.1. Function of the eye 1 1.2. Depth perception without stereoscopy 2 1.2.1. Monocular cues 2 1.2.2. Proprioceptive cues 7 1.3. Depth perception through stereoscopic vision 9 1.4. Perception of inclinations and curves 10 1.4.1. Perception of inclination and obliqueness 10 1.4.2. Perception of curves 14 1.5. Artificial stereoscopic vision 22 Chapter 2. Technological Elements 25 2.1. Taking a picture 25 2.2. Reproduction 26 2.2.1. Colorimetric differentiation 27 2.2.2. Differentiation by polarization 28 2.2.3. Active glasses 30 2.2.4. Auto-stereoscopic screens 31 2.2.5. Virtual reality headsets 33 2.3. Motion parallax restitution 34 2.3.1. Pseudoscopic movement 34 2.3.2. Correcting pseudoscopic movements 35 2.3.3. Monoscopic motion parallax 40 Chapter 3. Causes of Visual Fatigue in Stereoscopic Vision 41 3.1. Conflict between accommodation and convergence 41 3.2. Too much depth 44 3.3. High spatial frequencies 46 3.3.1. Limits of fusion 49 3.3.2. Comfort and high frequencies. 50 3.4. High temporal frequency 52 3.5. Conflicts with monoscopic cues 52 3.6. Vertical disparities 53 3.7. Improper device settings 55 3.7.1. Quality of image and display 55 3.7.2. Differences between left and right images 56 3.7.3. Speed of correction of pseudoscopic movements 57 Chapter 4. Short- and Long-term Consequences 59 4.1. Short-term effects 59 4.1.1. Decreasing ease of accommodation 59 4.1.2. Decrease in stereoscopic acuity 59 4.1.3. Effects on the punctum proximum 61 4.1.4. More subjective effects 61 4.2. Long-term consequences 62 4.2.1. Long-term effects on children 62 Chapter 5. Measuring Visual Fatigue 63 5.1. Visual acuity 63 5.1.1. Different possible measurements 64 5.1.2. Optotypes 64 5.2. Proximum accommodation function 65 5.3. Ease of accommodation 66 5.4. Stereoscopic acuity 67 5.4.1. Tests of distance vision 67 5.4.2. Tests of near vision 68 5.5. Disassociated heterophorias 71 5.6. Fusional reserves 72 5.7. Subjective tests 74 Chapter 6. Reducing Spatial Frequencies 75 6.1. Principle 75 6.2. Technical solution 75 6.2.1. Wavelets 76 6.2.2. BOX FILTER 92 6.2.3. Using a rolling average and other “blurs” 98 6.2.4. Comparison of algorithms 103 6.2.5. Chosen solution 114 6.3. Experiment 116 6.3.1. The task 116 6.4. Measurements of fatigue taken 118 6.4.1. Objective measurements 118 6.4.2. Procedure 119 6.4.3. The subjects 120 6.5. Result 120 6.5.1. Proximum accommodation function 120 6.5.2. Ease of accommodation 121 6.5.3. Stereoscopic acuity 122 6.5.4. Effectiveness in execution of the task 122 6.5.5. Subjective measurements 123 6.5.6. Conclusions 124 6.5.7. Discussion 124 Chapter 7. Reducing the Distance Between the Virtual Cameras 131 7.1. Principle 131 7.1.1. Usefulness of stereoscopy in depth perception 132 7.1.2. The objects 133 7.1.3. Hypothesis 142 7.2. Experiment 142 7.2.1. Tasks 142 7.2.2. Experimental conditions 143 7.2.3. Subjects 144 7.2.4. Measurements 144 7.3. Results 145 7.3.1. Results for fatigue 145 7.3.2. Perception results 147 7.4. Discussion 152 7.4.1. Influence on visual fatigue 152 7.4.2. Influence on visual perception 153 Conclusion 155 Bibliography 157 Index 167

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Book SynopsisThis open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area.Table of ContentsPart I - Introduction: 1. Digital Face Manipulation: An Introduction.- 2. Face Manipulation in Biometric Systems.- 3. Face Manipulation in Media Forensics.- Part II - Face Manipulation Detection Methods: 4. DeepFakes Manipulation.- 5. DeepFakes Detection.- 6. Attacking Face Recognition Systems with DeepFakes.- 7. Vulnerability of Face Recognition Systems to Morphing Attacks.- 8. Face Morphing Attack Detection.- 9. Face Synthesis Detection.- 10. Expression Swap Detection.- 11. Audio- and Text-to-Video Detection.- 12. Detection of Facial Retouching.- 13. Face De-Identification Detection.- Part III - Further Topics: 14. All-in-One Face Manipulation Detection: Generalization Analysis.- 15. Reversion of Face Manipulation.- 16. 3D Face Manipulation Detection.- 17. Improving Face Recognition with Face Image Manipulation.- 18. Impact of Post-Processing on Face Manipulation Detection.- 19. Societal and Legal Aspects of Face Manipulation.- 20. Face Manipulation for Privacy Protection.- 21. Privacy-preserving Face Manipulation Detection.- 22. Face Manipulation in Operational Systems.- Part IV - Open Issues, Trends, and Challenges: 23. All: Future trends in face Manipulation and Fake Detection.

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Book SynopsisThis book is open access. This book undertakes a multifaceted and integrated examination of biometric identification, including the current state of the technology, how it is being used, the key ethical issues, and the implications for law and regulation. The five chapters examine the main forms of contemporary biometrics–fingerprint recognition, facial recognition and DNA identification– as well the integration of biometric data with other forms of personal data, analyses key ethical concepts in play, including privacy, individual autonomy, collective responsibility, and joint ownership rights, and proposes a raft of principles to guide the regulation of biometrics in liberal democracies.Biometric identification technology is developing rapidly and being implemented more widely, along with other forms of information technology. As products, services and communication moves online, digital identity and security is becoming more important. Biometric identification facilitates this transition. Citizens now use biometrics to access a smartphone or obtain a passport; law enforcement agencies use biometrics in association with CCTV to identify a terrorist in a crowd, or identify a suspect via their fingerprints or DNA; and companies use biometrics to identify their customers and employees. In some cases the use of biometrics is governed by law, in others the technology has developed and been implemented so quickly that, perhaps because it has been viewed as a valuable security enhancement, laws regulating its use have often not been updated to reflect new applications. However, the technology associated with biometrics raises significant ethical problems, including in relation to individual privacy, ownership of biometric data, dual use and, more generally, as is illustrated by the increasing use of biometrics in authoritarian states such as China, the potential for unregulated biometrics to undermine fundamental principles of liberal democracy. Resolving these ethical problems is a vital step towards more effective regulation.Table of ContentsAcknowledgment1. The Rise of Biometric Identification, Fingerprints and Applied Ethics2. Facial Recognition and Privacy Rights3. DNA Identification, Joint Rights and Collective Responsibility4. Biometric and Non-Biometric Integration: Dual Use Dilemmas5. The Future of Biometrics and Liberal DemocracyIndex

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Book SynopsisComputer vision has become increasingly important and effective in recent years due to its wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and medicine, sports and recreation, robotics, drones, and self-driving cars. Visual recognition tasks, such as image classification, localization, and detection, are the core building blocks of many of these applications, and recent developments in Convolutional Neural Networks (CNNs) have led to outstanding performance in these state-of-the-art visual recognition tasks and systems. As a result, CNNs now form the crux of deep learning algorithms in computer vision. This self-contained guide will benefit those who seek to both understand the theory behind CNNs and to gain hands-on experience on the application of CNNs in computer vision. It provides a comprehensive introduction to CNNs starting with the essential concepts behind neural networks: training, regularization, and optimization of CNNs. The book also discusses a wide range of loss functions, network layers, and popular CNN architectures, reviews the different techniques for the evaluation of CNNs, and presents some popular CNN tools and libraries that are commonly used in computer vision. Further, this text describes and discusses case studies that are related to the application of CNN in computer vision, including image classification, object detection, semantic segmentation, scene understanding, and image generation. This book is ideal for undergraduate and graduate students, as no prior background knowledge in the field is required to follow the material, as well as new researchers, developers, engineers, and practitioners who are interested in gaining a quick understanding of CNN models.Table of ContentsPreface.- Acknowledgments.- Introduction.- Features and Classifiers.- Neural Networks Basics.- Convolutional Neural Network.- CNN Learning.- Examples of CNN Architectures.- Applications of CNNs in Computer Vision.- Deep Learning Tools and Libraries.- Conclusion.- Bibliography.- Authors' Biographies.

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Book SynopsisThis book constitutes the refereed proceedings of the 10th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2022, held in Aveiro, Portugal, in May 2022. The 54 papers accepted for these proceedings were carefully reviewed and selected from 72 submissions. They deal with document analysis; medical image processing; biometrics; pattern recognition and machine learning; computer vision; and other applications. Table of ContentsDOCUMENT ANALYSIS.- Test Sample Selection for Handwriting Recognition through Language Modeling.- Classification of Untranscribed Handwritten Notarial Documents by Textual Contents.- Incremental Vocabularies in Machine Translation through Aligned Embedding Projections.- An Interactive Machine Translation Framework for Modernizing the Language of Historical Documents.- From Captions to Explanations: A Multimodal Transformer-based Architecture for Natural Language Explanation Generation.- MEDICAL IMAGE PROCESSING.- Diagnosis of Skin Cancer Using Hierarchical Neural Networks and Metadata.- Lesion-Based Chest Radiography Image Retrieval for Explainability in Pathology Detection.- Deep Learning for Diagnosis of Alzheimer’s Disease with FDG-PET Neuroimaging.- Deep Aesthetic Assessment and Retrieval of Breast Cancer Treatment Outcomes.- Increased Robustness in Chest X-ray Classification through Clinical Report-driven regularization.- MEDICAL APPLICATIONS.- Deep Detection Models for Measuring Epidermal Bladder Cells.- On the performance of deep learning models for respiratory sound classification trained on unbalanced data.- Automated Adequacy Assessment of Cervical Cytology Samples using Deep Learning.- Exploring Alterations in Electrocardiogram during the Postoperative Pain.- Differential Gene Expression Analysis of the Most Relevant Genes for Lung Cancer Prediction and Sub-type Classification.- Detection of epilepsy in EEGs using Deep Sequence models - A Comparative Study.- BIOMETRICS.- Facial Emotion Recognition for Sentiment Analysis of Social Media Data.- Heartbeat selection based on outlier removal.- Characterization of emotions through facial Electromyogram signals.- Feature selection for emotional well-being monitorization.- Temporal Convolutional Networks for Robust Face Liveness Detection.- PATTERN RECOGNITION & MACHINE LEARNING.- MaxDropoutV2: An Improved Method to Drop out Neurons in Convolutional Neural Networks.- Transparent management of adjacencies in the cubic grid.- Abbreviating Labelling Cost for Sentinel-2 Image Scene Classification through Active Learning.- Feature-based classification of archaeal sequences using compression-based methods.- A first approach to Image Transformation Sequence Retrieval.- Discriminative Learning of Two-Dimensional Probabilistic Context-Free Grammars for Mathematical Expression Recognition and Retrieval.- COMPUTER VISION.- Golf Swing Sequencing using Computer Vision.- Domain Adaptation in Robotics: A Study Case on Kitchen Utensil Recognition.- An Innovative Vision System for Floor-Cleaning Robots based on YOLOv5.- LIDAR Signature based Node Detection and Classification in graph topological maps for indoor navigation.- Event Vision in Egocentric Human Action Recognition.- An edge-based computer vision approach for determination of sulfonamides in water.- IMAGE PROCESSING.- Visual Semantic Context Encoding for Aerial Data Introspection and Domain Prediction.- An End-to-End Approach for Seam Carving Detection using Deep Neural Networks.- Proposal of a comparative framework for face super-resolution algorithms in forensics.- On the use of Transformers for end-to-end Optical Music Recognition.- Retrieval of Music-Notation Primitives via Image-to-Sequence.- Digital image conspicuous features classification using TLCNN model with SVM classifier.- Contribution of low, mid and high-level image features in predicting human similarity judgements.- On the Topological Disparity Characterization of Square-pixel Binary Image Data by a Labeled Bipartite Graph.- Learning Sparse Masks for Diffusion-based Image Inpainting.- Extracting Descriptive Words from Untranscribed Handwritten Images.- OTHER APPLICATIONS.- GMM-aided DNN Bearing Fault Diagnosis using Sparse Autoencoder Feature Extraction.- Identification of External Defects on FruitsUsing Deep Learning.- Improving Action Quality Assessment using Weighted Aggregation.- Improving Licence Plate Detection using Generative Adversarial Networks.- Film shot type classification based on camera movement styles.- The CleanSea Set: A Benchmark Corpus for Underwater Debris Detection and Recognition.- A case of study on traffic cone detection for autonomous racing on a Jetson platform.- Energy savings in residential buildings based on adaptive thermal comfort models.- Opt-SSL: An Enhanced Self-Supervised Framework for Food Recognition.- Using bus tracking data to detect potential hazard driving zones.- Dynamic PCA based statistical monitoring of air pollutant concentrations in wildfire scenarios.

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Book SynopsisThis book constitutes the proceedings of the 16th Chinese Conference on Biometric Recognition, CCBR 2022, which took place in Beijing, China, in November 2022.The 70 papers presented in this volume were carefully reviewed and selected from 115 submissions. The papers cover a wide range of topics such as Fingerprint, Palmprint and Vein Recognition; Face Detection, Recognition and Tracking; Gesture and Action Recognition; Affective Computing and Human-Computer Interface; Speaker and Speech Recognition; Gait, Iris and Other Biometrics; Multi-modal Biometric Recognition and Fusion; Quality Evaluation and Enhancement of Biometric Signals; Animal Biometrics; Trustworthy, Privacy and Personal Data Security; Medical and Other Applications.Table of ContentsFingerprint, Palmprint and Vein Recognition.- A Finger BiModal Fusion Algorithm based on Improved DenseNet.- A lightweight segmentation network based on extraction.- A novel multi-layered minutiae extractor based on OCT fingerprints.- An overview and forecast of biometric recognition technology used in forensic science.- Combining Band-Limited OTSDF Filter and Directional Representation for Palmprint Recognition.- Cross-Dataset Image Matching Network for Heterogeneous Palmprint Recognition.- DUAL MODE NEAR-INFRARED SCANNER FOR IMAGING DORSAL HAND VEINS.- Multi-Stream Convolutional Neural Networks Fusion for Palmprint Recognition.- Multi-view Finger Vein Recognition using Attention-based MVCNN.- SELECTIVE DETAIL ENHANCEMENT ALGORITHM FOR FINGER VEIN IMAGES.- SP-FVR: SuperPoint-based Finger Vein Recognition.- TransFinger: Transformer based Finger Tri-modal Biometrics.- Face Detection, Recognition and Tracking.- A Survey of Domain Generalization-based Face Anti-spoofing.- An Empirical Comparative Analysis of Africans with Asians using DCNN Facial Biometric Models.- Disentanglement of Deep Features for Adversarial Face Detection.- Estimation of Gaze-Following Based on Transformer and the Guiding Offset.- Learning Optimal Transport Mapping of Joint Distribution for Cross-Scenario Face Anti-Spooffing.- MLFW: A Database for Face Recognition on Masked Faces.- Multi-scale object detection algorithm based on adaptive feature fusion.- Sparsity-Regularized Geometric Mean Metric Learning for Kinship Verification.- YoloMask: An Enhanced YOLO Model for Detection of Face Mask Wearing Normality, Irregularity and Spoofing.- Gesture and Action Recognition.- Adaptive Joint Interdependency Learning for 2D Occluded Hand Pose Estimation.- Contrastive and Consistent Learning for Unsupervised Human Parsing.- Dynamic Hand Gesture Authentication Based on Improved Two-stream CNN.- Efficient Video Understanding-based Random Hand Gesture Authentication.- Multidimension Joint Networks for Action Recognition.- Multi-Level Temporal-Guided Graph Convolutional Networks for Skeleton-Based Action Recognition.- Research on Gesture Recognition of Surface EMG Based on Machine Learning.- Affective Computing and Human-Computer Interface.- Adaptive Enhanced Micro-expression Spotting Network based on Multi-stage Features Extraction.- Augmented Feature Representation with Parallel Convolution for Cross-domain Facial Expression Recognition.- Hemispheric Asymmetry Measurement Network for Emotion Classification.- Human Action Recognition Algorithm of Non-Local Two-Stream Convolution Network Based on Image Depth Flow.- Synthetic Feature Generative Adversarial Network for Motor Imagery Classification: Create Feature from Sampled Data.- Speaker and Speech Recognition.- An End-to-end Conformer-based Speech Recognition Model for Mandarin Radiotelephony Communications in Civil Aviation.- ATRemix: An Auto-Tune Remix Dataset for Singer Recognition.- Low-resource speech keyword search based on residual neural network.- Online Neural Speaker Diarization with Core Samples.- Pose-unconstrainted 3D Lip Behaviometrics via Unsupervised Symmetry Correction.- Virtual Fully-Connected Layer for a Large-Scale Speaker Verification Dataset.- Gait, Iris and Other Biometrics.- A Simple Convolutional Neural Network for Small Sample Multi-lingual Offline Handwritten Signature Recognition.- Attention Skip Connection Dense Network for Accurate Iris Segmentation.- Gait Recognition with Various Data Modalities: A Review.- INCREMENTAL EEG BIOMETRIC RECOGNITION BASED ON EEG RELATION NETWORK.- Salient Foreground-Aware Network for Person Search.- Shoe print retrieval algorithm based on improved ecientnetV2.- Multi-modal Biometric Recognition and Fusion.- A novel dual-modal biometric recognition method based on weighted joint group sparse representation classification.- FINGER TRIMODAL FEATURES CODING FUSION METHOD.- Fusion of Gait and Face for Human Identification at the Feature Level.- Gait Recognition in Sensing Insoles: a study based on a Hybrid CNN-Attention-LSTM Network.- Identity Authentication Using a Multimodal Sensing Insole a Feasibility Study.- MDF-Net: Multimodal Deep Fusion for Large-scale Product Recognition.- Survey on Deep Learning based Fusion Recognition of Multimodal Biometrics.- Synthesizing Talking Face Videos with a Spatial Attention Mechanism.- Quality Evaluation and Enhancement of Biometric Signals.- Blind Perceptual Quality Assessment for Single Image Motion Deblurring.- Low-illumination Palmprint Image Enhancement Method Based On U-Net Neural Network.- Texture-guided multiscale feature learning network for palmprint image quality assessment.- Animal Biometrics.- An Adaptive Weight Joint Loss Optimization For Dog Face Recognition.- Improved YOLOv5 for Dense Wildlife Object Detection.- Self-Attention based Cross-level Fusion Network for Camou aged Object Detection.- Trustyworth, Privacy and Persondal Data Security.- Face Forgery Detection by Multi-dimensional Image Decomposition.- IrisGuard: Image Forgery Detection for Iris Anti-spooffing.- Multi-branch network with circle loss using voice conversion and channel robust data augmentation for synthetic speech detection.- Spoof Speech Detection Based on Raw Cross-dimension Interaction Attention Network.- Medical and Other Applications.- A Deformable Convolution Encoder with Multi-Scale Attention Fusion Mechanism for Classification of Brain Tumor MRI Images.- GI Tract Lesion Classification Using Multi-task Capsule Networks with Hierarchical Convolutional Layers.- Grading Diagnosis of Sacroiliitis in CT Scans Based on Radiomics and Deep Learning.- Noninvasive blood pressure waveform measurement method based on CNN-LSTM.- Recurrence Quantification Analysis of Cardiovascular System During Cardiopulmonary Resuscitation.- UAV AERIAL PHOTOGRAPHY TRAFFIC OBJECT DETECTION BASED ON LIGHTWEIGHT DESIGN AND FEATURE FUSION.- UMixer: A novel U-shaped convolutional mixer for multi-scale feature fusion in Medical Image Segmentation.

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Book SynopsisThis book constitutes the refereed proceedings of the 5th International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2022, held in Kingsville, TX, USA, in collaboration with the Applied AI Research Laboratory of the University of South Dakota, during December 01-02, 2022.The 31 full papers included in this book were carefully reviewed and selected from 69 submissions. They were organized in topical sections as follows: healthcare: medical imaging and informatics; computer vision and pattern recognition; internet of things and security; and signal processing and machine learning.Table of Contents​Healthcare: medical imaging and informatics.- Data Characterization for Reliable AI in Medicine.- Alzheimer’s Disease Detection using Ensemble Learning and Artificial Neural Networks.- Semi-supervised Multi-domain Learning for Medical Image Classification.- Significant CC400 functional brain parcellations based LeNet5 Convolutional Neural Network for Autism Spectrum Disorder detection.- 2D respiratory sound analysis to detect lung abnormalities.- Analyzing Chest X-Ray to Detect the Evidence of Lung Abnormality due to Infectious Disease.- Chest X-ray Image Super-resolution via Deep Contrast Consistent Feature Network.- A Novel Approach to Enhance Effectiveness of Image Segmentation Techniques on Extremely Noisy Medical Images.- Federated Learning for Lung Sound Analysis.- Performance Analysis of CNN and Quantized CNN Model for Rheumatoid Arthritis Identification using Thermal Image.- Image Processing and Pattern Recognition of Micropores of Polysulfone Membrane for the Bio-separation of Viruses from Whole Blood.- An Extreme Learning Machine-basedAutoEncoder (ELM-AE)for denoising knee X-ray images and grading knee osteoarthritis severity.- Computer Vision and Pattern Recognition.- Motor Imagery Classification CombiningRiemannian Geometry and Artificial Neural Networks.- Autism Spectrum Disorder Detection using Transfer Learning with VGG 19, Inception V3 and DenseNet 201.- Shrimp Shape Analysis by a Chord LengthFunction Based Methodology.- Supervised Neural Networks for Fruit Identification.- Targeted Clean-Label Poisoning Attacks On Federated Learning.- Building Marathi SentiWordNet.- A computational study on calibrated VGG19 formultimodal learning and representation insurveillance.- Automated Deep Learning based approach for Albinism Detection.- A Deep learning-based regression scheme for angle estimation in image dataset.- The classification of Native and Invasive Speciesin North America: A Transfer Learning and Random Forest Pipeline.- Internet of Things and Security.- Towards a Digital Twin Integrated DLT and IoT-based Automated Healthcare Ecosystem.- Enabling Edge Devices using Federated Learning and Big Data for Proactive Decisions.- IoT and Blockchain oriented gender determination of Bangladeshi populations.- Federated Learning based secured computational offloading in cyber-physical IoST systems.- A Hybrid Campus Security System Combined ofFace, Number-plate, and Voice Recognition.- Signal Processing and Machine.- Single-trial detection of event-related potentials with artificial examples based on coloring transformation.- Identifying the relationship between hypothesis and premise.- Data Poisoning Attack by Label Flipping onSplitFed Learning.- A Deep Learning-powered voice-enabled mathtutor for kids.

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Book SynopsisThe 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online.The 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop proceedings. They were organized in individual parts as follows: Part I: W01 - AI for Space; W02 - Vision for Art; W03 - Adversarial Robustness in the Real World; W04 - Autonomous Vehicle Vision Part II: W05 - Learning With Limited and Imperfect Data; W06 - Advances in Image Manipulation; Part III: W07 - Medical Computer Vision; W08 - Computer Vision for Metaverse; W09 - Self-Supervised Learning: What Is Next?; Part IV: W10 - Self-Supervised Learning for Next-Generation Industry-Level Autonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for Creative Video Editing and Understanding; W17 - Visual Inductive Priors for Data-Efficient Deep Learning; W18 - Mobile Intelligent Photography and Imaging; Part V: W19 - People Analysis: From Face, Body and Fashion to 3D Virtual Avatars; W20 - Safe Artificial Intelligence for Automated Driving; W21 - Real-World Surveillance: Applications and Challenges; W22 - Affective Behavior Analysis In-the-Wild; Part VI: W23 - Visual Perception for Navigation in Human Environments: The JackRabbot Human Body Pose Dataset and Benchmark; W24 - Distributed Smart Cameras; W25 - Causality in Vision; W26 - In-Vehicle Sensing and Monitorization; W27 - Assistive Computer Vision and Robotics; W28 - Computational Aspects of Deep Learning; Part VII: W29 - Computer Vision for Civil and Infrastructure Engineering; W30 - AI-Enabled Medical Image Analysis: Digital Pathology and Radiology/COVID19; W31 - Compositional and Multimodal Perception; Part VIII: W32 - Uncertainty Quantification for Computer Vision; W33 - Recovering 6D Object Pose; W34 - Drawings and Abstract Imagery: Representation and Analysis; W35 - Sign Language Understanding; W36 - A Challenge for Out-of-Distribution Generalization in Computer Vision; W37 - Vision With Biased or Scarce Data; W38 - Visual Object Tracking Challenge.

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Book SynopsisThe 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online.The 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop proceedings. They were organized in individual parts as follows: Part I: W01 - AI for Space; W02 - Vision for Art; W03 - Adversarial Robustness in the Real World; W04 - Autonomous Vehicle Vision Part II: W05 - Learning With Limited and Imperfect Data; W06 - Advances in Image Manipulation; Part III: W07 - Medical Computer Vision; W08 - Computer Vision for Metaverse; W09 - Self-Supervised Learning: What Is Next?; Part IV: W10 - Self-Supervised Learning for Next-Generation Industry-Level Autonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for Creative Video Editing and Understanding; W17 - Visual Inductive Priors for Data-Efficient Deep Learning; W18 - Mobile Intelligent Photography and Imaging; Part V: W19 - People Analysis: From Face, Body and Fashion to 3D Virtual Avatars; W20 - Safe Artificial Intelligence for Automated Driving; W21 - Real-World Surveillance: Applications and Challenges; W22 - Affective Behavior Analysis In-the-Wild; Part VI: W23 - Visual Perception for Navigation in Human Environments: The JackRabbot Human Body Pose Dataset and Benchmark; W24 - Distributed Smart Cameras; W25 - Causality in Vision; W26 - In-Vehicle Sensing and Monitorization; W27 - Assistive Computer Vision and Robotics; W28 - Computational Aspects of Deep Learning; Part VII: W29 - Computer Vision for Civil and Infrastructure Engineering; W30 - AI-Enabled Medical Image Analysis: Digital Pathology and Radiology/COVID19; W31 - Compositional and Multimodal Perception; Part VIII: W32 - Uncertainty Quantification for Computer Vision; W33 - Recovering 6D Object Pose; W34 - Drawings and Abstract Imagery: Representation and Analysis; W35 - Sign Language Understanding; W36 - A Challenge for Out-of-Distribution Generalization in Computer Vision; W37 - Vision With Biased or Scarce Data; W38 - Visual Object Tracking Challenge.

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Book SynopsisThe 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online.The 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop proceedings. They were organized in individual parts as follows: Part I: W01 - AI for Space; W02 - Vision for Art; W03 - Adversarial Robustness in the Real World; W04 - Autonomous Vehicle Vision Part II: W05 - Learning With Limited and Imperfect Data; W06 - Advances in Image Manipulation; Part III: W07 - Medical Computer Vision; W08 - Computer Vision for Metaverse; W09 - Self-Supervised Learning: What Is Next?; Part IV: W10 - Self-Supervised Learning for Next-Generation Industry-Level Autonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for Creative Video Editing and Understanding; W17 - Visual Inductive Priors for Data-Efficient Deep Learning; W18 - Mobile Intelligent Photography and Imaging; Part V: W19 - People Analysis: From Face, Body and Fashion to 3D Virtual Avatars; W20 - Safe Artificial Intelligence for Automated Driving; W21 - Real-World Surveillance: Applications and Challenges; W22 - Affective Behavior Analysis In-the-Wild; Part VI: W23 - Visual Perception for Navigation in Human Environments: The JackRabbot Human Body Pose Dataset and Benchmark; W24 - Distributed Smart Cameras; W25 - Causality in Vision; W26 - In-Vehicle Sensing and Monitorization; W27 - Assistive Computer Vision and Robotics; W28 - Computational Aspects of Deep Learning; Part VII: W29 - Computer Vision for Civil and Infrastructure Engineering; W30 - AI-Enabled Medical Image Analysis: Digital Pathology and Radiology/COVID19; W31 - Compositional and Multimodal Perception; Part VIII: W32 - Uncertainty Quantification for Computer Vision; W33 - Recovering 6D Object Pose; W34 - Drawings and Abstract Imagery: Representation and Analysis; W35 - Sign Language Understanding; W36 - A Challenge for Out-of-Distribution Generalization in Computer Vision; W37 - Vision With Biased or Scarce Data; W38 - Visual Object Tracking Challenge.

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Book SynopsisThe 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online.The 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop proceedings. They were organized in individual parts as follows: Part I: W01 - AI for Space; W02 - Vision for Art; W03 - Adversarial Robustness in the Real World; W04 - Autonomous Vehicle Vision Part II: W05 - Learning With Limited and Imperfect Data; W06 - Advances in Image Manipulation; Part III: W07 - Medical Computer Vision; W08 - Computer Vision for Metaverse; W09 - Self-Supervised Learning: What Is Next?; Part IV: W10 - Self-Supervised Learning for Next-Generation Industry-Level Autonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for Creative Video Editing and Understanding; W17 - Visual Inductive Priors for Data-Efficient Deep Learning; W18 - Mobile Intelligent Photography and Imaging; Part V: W19 - People Analysis: From Face, Body and Fashion to 3D Virtual Avatars; W20 - Safe Artificial Intelligence for Automated Driving; W21 - Real-World Surveillance: Applications and Challenges; W22 - Affective Behavior Analysis In-the-Wild; Part VI: W23 - Visual Perception for Navigation in Human Environments: The JackRabbot Human Body Pose Dataset and Benchmark; W24 - Distributed Smart Cameras; W25 - Causality in Vision; W26 - In-Vehicle Sensing and Monitorization; W27 - Assistive Computer Vision and Robotics; W28 - Computational Aspects of Deep Learning; Part VII: W29 - Computer Vision for Civil and Infrastructure Engineering; W30 - AI-Enabled Medical Image Analysis: Digital Pathology and Radiology/COVID19; W31 - Compositional and Multimodal Perception; Part VIII: W32 - Uncertainty Quantification for Computer Vision; W33 - Recovering 6D Object Pose; W34 - Drawings and Abstract Imagery: Representation and Analysis; W35 - Sign Language Understanding; W36 - A Challenge for Out-of-Distribution Generalization in Computer Vision; W37 - Vision With Biased or Scarce Data; W38 - Visual Object Tracking Challenge.

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Book SynopsisThe three-volume set LNCS 13623, 13624, and 13625 constitutes the refereed proceedings of the 29th International Conference on Neural Information Processing, ICONIP 2022, held as a virtual event, November 22–26, 2022.The 146 papers presented in the proceedings set were carefully reviewed and selected from 810 submissions. They were organized in topical sections as follows: Theory and Algorithms; Cognitive Neurosciences; Human Centered Computing; and Applications.The ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements.Table of ContentsTheory and Algorithms.- Solving Partial Differential Equations using Point-based Neural Networks.- Patch Mix Augmentation with Dual Encoders for Meta-Learning.- Tacit Commitments Emergence in Multi-agent Reinforcement Learning.- Saccade Direction Information Channel.- Shared-Attribute Multi-Graph Clustering with Global Self-Attention.- Mutual Diverse-Label Adversarial Training.- Multi-Agent Hyper-Attention Policy Optimization.- Filter Pruning via Similarity Clustering for Deep Convolutional Neural Networks.- FPD: Feature Pyramid Knowledge Distillation.- An effective ensemble model related to incremental learning in neural machine translation.- Local-Global Semantic Fusion Single-shot Classification Method.- Self-Reinforcing Feedback Domain Adaptation Channel.- General Algorithm for Learning from Grouped Uncoupled Data and Pairwise Comparison Data.- Additional Learning for Joint Probability Distribution Matching in BiGAN.- Multi-View Self-Attention for Regression Domain Adaptation with Feature Selection.- EigenGRF: Layer-Wise Eigen-Learning for Controllable Generative Radiance Fields.- Partial Label learning with Gradually Induced Error-Correction Output Codes.- HMC-PSO: A Hamiltonian Monte Carlo and Particle Swarm Optimization-based optimizer.- Heterogeneous Graph Representation for Knowledge Tracing.- Intuitionistic fuzzy universum support vector machine.- Support vector machine based models with sparse auto-encoder based features for classification problem.- Selectively increasing the diversity of GAN-generated samples.- Cooperation and Competition: Flocking with Evolutionary Multi-Agent Reinforcement Learning.- Differentiable Causal Discovery Under Heteroscedastic Noise.- IDPL: Intra-subdomain adaptation adversarial learning segmentation method based on Dynamic Pseudo Labels.- Adaptive Scaling for U-Net in Time Series Classification.- Permutation Elementary Cellular Automata: Analysis and Application of Simple Examples.- SSPR: A Skyline-Based Semantic Place Retrieval Method.- Double Regularization-based RVFL and edRVFL Networks for Sparse-Dataset Classification.- Adaptive Tabu Dropout for Regularization of Deep Neural Networks.- Class-Incremental Learning with Multiscale Distillation for Weakly Supervised Temporal Action Localization.- Nearest Neighbor Classifier with Margin Penalty for Active Learning.- Factual Error Correction in Summarization with Retriever-Reader Pipeline.- Context-adapted Multi-policy Ensemble Method for Generalization in Reinforcement Learning.- Self-attention based multi-scale graph convolutional networks.- Synesthesia Transformer with Contrastive Multimodal Learning.- Context-based Point Generation Network for Point Cloud Completion.- Temporal Neighborhood Change Centrality for Important Node Identification in Temporal Networks.- DOM2R-Graph: A Web Attribute Extraction Architecture with Relation-aware Heterogeneous Graph Transformer.- Sparse Linear Capsules for Matrix Factorization-based Collaborative Filtering.- PromptFusion: a Low-cost Prompt-based Task Composition for Multi-task Learning.- A fast and efficient algorithm for filtering the training dataset.- Entropy-minimization Mean Teacher for Source-Free Domain Adaptive Object Detection.- IA-CL: A Deep Bidirectional Competitive Learning Method for Traveling Salesman Problem.- Boosting Graph Convolutional Networks With Semi-Supervised Training.- Auxiliary Network: Scalable and agile online learning for dynamic system with inconsistently available inputs.- VAAC: V-value Attention Actor-Critic for Cooperative Multi-agent Reinforcement Learning.- An Analytical Estimation of Spiking Neural Networks Energy Efficiency.- Correlation Based Semantic Transfer with Application to Domain Adaptation.- Minimum Variance Embedded Intuitionistic Fuzzy Weighted Random Vector Functional Link Network.- Neural Network Compression by Joint Sparsity Promotion and Redundancy Reduction.

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Book SynopsisThis two-volume set (LNCS 13885-13886) constitutes the refereed proceedings of the 23rd Scandinavian Conference on Image Analysis, SCIA 2023, held in Lapland, Finland, in April 2023.The 67 revised papers presented were carefully reviewed and selected from 108 submissions. The contributions are structured in topical sections on datasets and evaluation; action and behaviour recognition; image and video processing, analysis, and understanding; detection, recognition, classification, and localization in 2D and/or 3D; machine learning and deep learning; segmentation, grouping, and shape; vision for robotics and autonomous vehicles; biometrics, faces, body gestures and pose; 3D vision from multiview and other sensors; vision applications and systems.Table of ContentsDatasets and Evaluation.- Action and Behaviour Recognition.- Image and Video Processing, Analysis, and Understanding.- Detection, Recognition, Classification, and Localization in 2D and/or 3D.- Machine Learning and Deep Learning.

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Book SynopsisThis two-volume set (LNCS 13885-13886) constitutes the refereed proceedings of the 23rd Scandinavian Conference on Image Analysis, SCIA 2023, held in Lapland, Finland, in April 2023.The 67 revised papers presented were carefully reviewed and selected from 108 submissions. The contributions are structured in topical sections on datasets and evaluation; action and behaviour recognition; image and video processing, analysis, and understanding; detection, recognition, classification, and localization in 2D and/or 3D; machine learning and deep learning; segmentation, grouping, and shape; vision for robotics and autonomous vehicles; biometrics, faces, body gestures and pose; 3D vision from multiview and other sensors; vision applications and systems.Table of ContentsSegmentation, Grouping, and Shape.- Vision for Robotics and Autonomous Vehicles.- Biometrics, Faces, Body Gestures and Pose.- 3D Vision from Multiview and other Sensors.- Vision Applications and Systems.

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