Pattern recognition Books

Book SynopsisThis comprehensive textbook offers a scientifically sound and at the same time practical introduction to Virtual and Augmented Reality (VR/AR). Readers will gain the theoretical foundation needed to design, implement or enhance VR/AR systems, evaluate and improve user interfaces and applications using VR/AR methods, assess and enrich user experiences, and develop a deeper understanding of how to apply VR/AR techniques. Whether utilizing the book for a principal course of study or reference reading, students of computer science, education, media, natural sciences, engineering and other subject areas can benefit from its in-depth content and vivid explanation. The modular structure allows selective sequencing of topics to the requirements of each teaching unit and provides an easy-to-use format from which to choose specific themes for individual self-study. Instructors are provided with extensive materials for creating courses as well as a foundational text upon which to build their advanced topics. The book enables users from both research and industry to deal with the subject in detail so they can properly assess the extent and benefits of VR/AR deployment and determine required resources. Technology enthusiasts and professionals can learn about the current status quo in the field of VR/AR and interested newcomers can gain insight into this fascinating world. Grounded on a solid scientific foundation, this textbook, addresses topics such as perceptual aspects of VR/AR, input and output devices including tracking, interactions in virtual worlds, real-time aspects of VR/AR systems and the authoring of VR/AR applications in addition to providing a broad collection of case studies.Table of Contents1 R. Doerner et al., Introduction to Virtual and Augmented Reality.- 2 R. Doerner and F. Steinicke, Perceptual Aspects of VR.- 3 B. Jung and A. Vitzhum, Virtual Worlds.- 4 P. Grimm at al., VR/AR Input Devices and Tracking.- 5 W. Broll et al., VR/AR Output Devices.- 6 R. Doerner et al., Interaction in Virtual Worlds.- 7 M. Buhr et al., Real-time Aspects of VR Systems.- 8 W. Broll, Augmented Reality.- 9 R. Doerner et al., VR/AR Case Studies.- 10 W. Broll et al., Authoring of VR/AR Applications.- 11 R. Doerner, Mathematical Foundations of VR/AR.

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Book SynopsisA major new professional reference work on fingerprint security systems and technology from leading international researchers in the field. Handbook provides authoritative and comprehensive coverage of all major topics, concepts, and methods for fingerprint security systems. This unique reference work is an absolutely essential resource for all biometric security professionals, researchers, and systems administrators.Table of ContentsIntroduction.- Fingerprint sensing.- Fingerprint analysis and representation.- Fingerprint matching.- Fingerprint classification and indexing.- Latent fingerprint recognition.- Fingerprint synthesis.- Fingerprint individuality.- Securing fingerprint systems.

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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 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 14th Mexican Conference on Pattern Recognition, MCPR 2022, which was held in planned to be held Ciudad Juárez, Mexico, in June 2022. The 33 papers presented in this volume were carefully reviewed and selected from 66 submissions. They are organized in the following topical sections: pattern recognition techniques; neural networks and deep learning; image and signal processing and analysis; natural language processing and recognition; robotics and remote sensing applications of pattern recognition; medical applications of pattern recognition.Table of ContentsPattern Recognition Techniques.- Hot Spots & Hot Regions Detection using Classification Algorithms in BMPs Complexes at the Protein-protein Interface with the Ground-state Energy Feature.- Clustering of Twitter Networks based on Users’ Structural Profile.- Changing Model from NGSIM Dataset.- A Robust Fault Diagnosis Method in Presence of Noise and Missing Information for Industrial Plants.- A Preliminary Study of SMOTE on Imbalanced Big Datasets when Dealing with Sparse and Dense High Dimensionality.- A Novel Survival Analysis-based Approach for Predicting Behavioral Probability of Mining Mixed Data Bases using Machine Learning Algorithms.- Networks and Deep Learning A CNN-based Driver’s Drowsiness and Distraction Detection System.- 3D Convolutional Neural Network to Enhance Small-Animal Positron Emission Tomography Images in the Sinogram Domain.- Learning Dendrite Morphological Neurons Using Linkage Trees for Pattern Classification.- Deep Variational Method with Attention for High-Definition Face Generation.- Indoor Air Pollution Forecasting using Deep Neural Networks.- Extreme Machine Learning Architectures based on Correlation.- Image & Signal Processing and Analysis Evaluating New Set of Acoustical Features for Cry Signal Classification.- Motor Imagery Classification Using Riemannian Geometry in Multiple Frequency Bands with a Weighted Nearest Neighbors Approach.- Virtualizing 3D Real Environments Using 2D Pictures Based on Photogrammetry.- Factorized U-net for Retinal Vessel Segmentation.- Multi-view Learning for EEG Signal Classification of Imagined Speech.- Escalante Emotion Recognition using Time-frequency Distribution and GLCM Features from EEG Signals.- Natural Language Processing and Recognition Leveraging Multiple Characterizations of Social Media Users for Depression Detection Using Data Fusion.- A Wide & Deep Learning Approach for Covid-19 Tweet Classification.- Does this Tweet Report an Adverse Drug Reaction? An Enhanced BERT-based Method to Identify Drugs Side Effects in Twitter.- We Will Know Them by Their Style: Fake News Detection based on Masked n-grams.- Multi-Document Text Summarization based on Genetic Algorithm and the Relevance of Sentence Features.- ´ Robotics & Remote Sensing Applications of Pattern Recognition On Labelling Pointclouds with the Nearest Facet of Triangulated Building Models.- Dust Deposition Classification on the Receiver Tube of the Parabolic Trough Collector: A Deep Learning-based Approach.- Detection of Pain Caused By A Thermal Stimulus Using EEG and Machine Learning.- Data Mining.- Natural Language Processing and Recognition.- Document Processing and Recognition.- Fuzzy and Hybrid Techniques in Pattern Recognition.- Image Coding, Processing and Analysis.- Industrial and Medical Applications of Pattern Recognition.- Bioinformatics.- Logical Combinatorial Pattern Recognition.- Mathematical Morphology.- Artificial Intelligence Techniques and Recognition.- Pattern Recognition Principles.- Robotics & Remote Sensing Applications of Pattern Recognition.- Shape and Texture Analysis.- Signal Processing and Analysis.

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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 10th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2022, held in Dubai, UAE, in November 2022. The 16 revised full papers presented were carefully reviewed and selected from 24 submissions. The conference presents papers on subject such as pattern recognition and machine learning based on artificial neural networks. Table of ContentsTransformer-Encoder generated context-aware embeddings for spell correction.- Graph Augmentation for Neural Networks Using Matching-Graphs.- Wavelet Scattering Transform Depth Benefit, An Application for Speaker Identification.- Assessment of Pharmaceutical Patent Novelty using Siamese Neural Network.- A Review of Capsule Networks in Medical Image Analysis.- Multi-stage Bias Mitigation for Individual Fairness in Algorithmic Decisions.- Introducing an Atypical Loss: A Perceptual Metric Learning for Image Pairing.- A Study on the Autonomous Detection of Impact Craters.- Minimizing Cross Intersections in Graph Drawing via Linear Splines.- Sequence-to-Sequence CNN-BiLSTM Based Glottal Closure Instant Detection from Raw Speech.- Do Minimal Complexity Least Squares Support Vector Machines Work?.- A Novel Representation of Graphical Patterns for Graph Convolution Networks.- Mono vs Multilingual BERT for Hate Speech Detection and Text Classification: A Case Study in Marathi Utilization of Vision Transformer for Classification and Ranking of Video Distortions.- White Blood Cell Classification of Porcine Blood Smear Images.- Medical Deepfake Detection using 3-Dimensional Neural Learning.

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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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Book SynopsisThis book constitutes the refereed proceedings of the 15th Mexican Conference on Pattern Recognition, MCPR 2023, held in Tepic, Mexico, during June 21–24, 2023.The 30 full papers presented in this book were carefully reviewed and selected from 61 submissions. The papers are divided into the following topical sections: pattern recognition and machine learning techniques; deep learning and neural networks; medical applications of pattern recognition; language processing and recognition; and industrial applications of pattern recognition.Table of ContentsPattern Recognition and Machine Learning Techniques: Feature Analysis and Selection for Water Stream Modeling.- A Cloud-based (AWS) Machine Learning Solution to Predict Account Receivables in a Financial Institution.- A New Approach for Road Type Classification using Multi-Stage Graph Embedding Method.- Removing the Black-Box from Machine Learning.- Using Machine Learning to Identify Patterns in Learner-Submitted Code for the Purpose of Assessment.- Fitness Function Comparison for Unsupervised Feature Selection with Permutational-Based Dierential Evolution.- A Method for Counting Models on Cubic Boolean Formulas.- Automatic Identication of Learning Styles through Behavioral Patterns.- Comparison of Classiers in Challenge Scheme.- Deep Learning and Neural Networks: Robust Zero-Watermarking for Medical Images based on Deep Learning Feature Extraction.- Plant Stress Recognition Using Deep Learning and 3D Reconstruction.- Segmentation and Classification Networks for Corn/Weed Detection under Excessive Field Variabilities.- Leukocyte Recognition Using a Modified AlexNet and Image to Image GAN Data Augmentation.- Spoofing Detection for Speaker Verification with Glottal Flow and 1D Pure Convolutional Networks.- Estimation of Stokes Parameters using Deep Neural Networks.- Experimental Study of the Performance of Convolutional Neural Networks Applied in Art Media Classification.- Medical Applications of Pattern Recognition: Hadamard Layer to Improve Semantic Segmentation in Medical Images.- Patterns in Genesis of Breast Cancer Tumor.- Realistic Simulation of Event-Related Potentials and their usual Noise and Interferences for Pattern Recognition.- Chest X-ray Imaging Severity Score of COVID-19 Pneumonia.- Leukocyte Detection with Novel Fully Convolutional Network and a New Dataset of Blood Smear Complete Samples.- Comparison of Deep Learning Architectures in Classification of Microcalcifications Clusters in Digital Mammograms.- Retinal Artery and Vein Segmentation using an Image-to-image Conditional Adversarial Network.- Evaluation of Heatmaps as an Explicative Method for Classifying Acute Lymphoblastic Leukemia Cells.- Language Processing and Recognition: Machine Learning Models Applied in Sign Language Recognition.- Urdu Semantic Parsing: An Improved SEMPRE Framework for Conversion of Urdu Language Web Queries to Logical forms.- Improving the Identification of Abusive Language through Careful Design of Pre-training Tasks.- Industrial Applications of Pattern Recognition: TOPSIS Method for Multiple-Criteria Decision-Making Applied to Trajectory Selection for Autonomous Driving.- Machine-learning based Estimation of the Bending Magnitude Sensed by a Fiber Optic Device.- Graph-based Semi-Supervised Learning using Riemannian Geometry Distance for Motor Imagery Classification.

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Book SynopsisThis book constitutes the refereed proceedings of the 10th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2023, held in conjunction with the 26th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2023, in Vancouver, Canada, in October 2023.The 16 papers presented at OMIA 2023 were carefully reviewed and selected from 27 submissions. The papers cover various topics in the field of ophthalmic medical image analysis and challenges in terms of reliability and validation, number and type of conditions considered, multi-modal analysis (e.g., fundus, optical coherence tomography, scanning laser ophthalmoscopy), novel imaging technologies, and the effective transfer of advanced computer vision and machine learning technologies.

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Book SynopsisThis book constitutes the proceedings of the 21st International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2023, held in Kumamoto, Japan, during August 2023. The 31 papers presented in this volume were carefully reviewed and selected from a total of 48 submissions. They were organized in topical sections named: Computer Vision, Affective Computing and Human Interactions, Managing the Biodiversity, Robotics and Drones, Machine Learning.Table of ContentsA hybrid quantum-classical segment-based Stereo Matching algorithm.- Continuous Exposure for Extreme Low-Light Imaging.- Semi-supervised Classification and Segmentation of Forest Fire using Autoencoders.- Descriptive and coherent paragraph generation for image paragraph captioning using vision transformer and post-processing.- Pyramid Swin Transformer for Multi-Task: Expanding to more computer vision tasks.- Person activity classification from an aerial sensor based on a multi-level deep features.- Person Quick-Search Approach based on a Facial Semantic Attributes Description.- Age-Invariant Face Recognition using Face Feature Vectors and Embedded Prototype Subspace Classifiers.- BENet: A lightweight bottom-up framework for context-aware emotion recognition.- Yolopoint: Joint Keypoint and Object Detection.- Less-than-one shot 3d segmentation hijacking a pre-trained space-time memory network.- Segmentation of Range-Azimuth Maps of FMCW radars with a deep convolutional neural network.- Segmentation of Range-Azimuth Maps of FMCW radars with a deep convolutional neural network.- A Single Image Neuro-Geometric Depth Estimation.- Wave-shaping Neural Activation for Improved 3D Model Reconstruction from Sparse Point Clouds.- A Deep Learning Approach to Segment High-Content Images of the E.coli Bacteria.- Multimodal Emotion Recognition System Through Three Different Channels (MER-3C).- Multi-Modal Obstacle Avoidance in USVs via Anomaly Detection and Cascaded Datasets.- A Contrario Mosaic Analysis for Image Forensics.- IRIS SEGMENTATION TECHNIQUE USING IRIS-UNet METHOD.- Image Acquisition by Image Retrieval with Color Aesthetics.- Improved Obstructed Facial Feature Reconstruction for Emotion Recognition with Minimal Change CycleGANs.- Quality assessment for high dynamic range stereoscopic omnidirectional image system.- Genetic Programming with Convolutional Operators for Albatross Nest Detection from Satellite Imaging.- Reinforcement Learning for truck Eco-driving: a serious game as driving assistance system.- Underwater mussel segmentation using smoothed shape descriptors with random forest.- A 2D Cortical Flat Map Space for Computationally Efficient Mammalian Brain simulation.- Construction of a novel data set for pedestrian tree species detection using google street view data.- Texture-based Data Augmentation for Small Datasets.- Multimodal Representations for Teacher-Guided Compositional Visual Reasoning.- Enhanced Color QR Codes with Resilient Error Correction for Dirt-Prone Surfaces.

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Book SynopsisThe five-volume set LNCS 14355, 14356, 14357, 14358 and 14359 constitutes the refereed proceedings of the 12th International Conference on Image and Graphics, ICIG 2023, held in Nanjing, China, during September 22–24, 2023.The 166 papers presented in the proceedings set were carefully reviewed and selected from 409 submissions. They were organized in topical sections as follows: computer vision and pattern recognition; computer graphics and visualization; compression, transmission, retrieval; artificial intelligence; biological and medical image processing; color and multispectral processing; computational imaging; multi-view and stereoscopic processing; multimedia security; surveillance and remote sensing, and virtual reality.The ICIG 2023 is a biennial conference that focuses on innovative technologies of image, video and graphics processing and fostering innovation, entrepreneurship, and networking. It will feature world-class plenary speakers, exhibits, and high quality peer reviewed oral and poster presentations. Table of Contents​Computer Vision and Pattern Recognition: Temporal Global Re-Detection Based on Interaction-Fusion Attention in Long-Term Visual Tracking.- VLNet: A Multi-task Network for Joint Vehicle and Lane Detection.- Adaptive Cost Aggregation in Iterative Depth Estimation for Efficient Multi-View Stereo.- A Novel Semantic Segmentation Method for High-Resolution Remote Sensing Images Based on Visual Attention Network.- Efficient Few-shot Image Generation via Lightweight Octave Generative Adversarial Networks.- Incorporating Global Correlation and Local Aggregation for Efficient Visual Localization.- Deep Interactive Image Semantic and Instance Segmentation.- Learning High-Performance Spiking Neural Networks With Multi-Compartment Spiking Neurons.- Attribute Space Analysis for Image Editing.-SAGAN: Self-Attention Generative Adversarial Net-work for RGB-D Saliency Prediction.- Behavioural State Detection Algorithm for Infants and Toddlers Incorporating Multi-scale Contextual Features.- Motion-Scenario Decoupling for Rat-Aware Video Position Prediction: Strategy and Benchmark.- Dual Fusion Network for Hyperspectral Semantic Segmentation.- Strip-FFT Transformer for single image deblurring.- Vision-Language Adaptive Mutual Decoder for OOV-STR.- DensityLayout: Density-conditioned Layout GAN for Visual-textual Presentation Designs.- GLTCM: Global-Local Temporal and Cross-Modal Network for Audio-Visual Event Localization.- Recent Advances in Class-Incremental Learning.- TTA-GCN: Temporal Topology Aggregation for Skeleton-Based Action Recognition.- A Stable Long-Term Tracking Method For Group-Housed Pigs.- Dynamic Attention for Isolated Sign Language Recognition with Reinforcement Learning.- A Segmentation Method based on SE Attention and U-Net for Apple Image.- Human Action Recognition Method based on spatio-temporal relationship.- Facial expression recognition from occluded images using deep convolution neural network with Vision Transformer.- Learning Discriminative Proposal Representation for Multi-Object Tracking.- Virtual-Violence: A Brand-New Dataset for Video Violence Recognition.- A novel Attention-DeblurGAN-Based Defogging Algorithm.- Multi-Modal Context-Aware Network for Scene Graph Generation.- VQA-CLPR: Turning a Visual Question Answering Model into a Chinese License Plate Recognizer.- Unsupervised Vehicle Re-Identification via Raw UAV Videos.- Distance-Aware Vector-Field and Vector Screening Strategy for 6D Object Pose Estimation.- SSTA-Net: Self-supervised Spatio-Temporal Attention Network for Action Recognition.- Gesture recognition method based on Sim-ConvNeXt model.- Research on Airborne Infrared Target Recognition Method based on Target-Environment Coupling.- Semantic and Gradient Guided Scene Text Image Super-Resolution.

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Book SynopsisThe five-volume set LNCS 14355, 14356, 14357, 14358 and 14359 constitutes the refereed proceedings of the 12th International Conference on Image and Graphics, ICIG 2023, held in Nanjing, China, during September 22–24, 2023.The 166 papers presented in the proceedings set were carefully reviewed and selected from 409 submissions. They were organized in topical sections as follows: computer vision and pattern recognition; computer graphics and visualization; compression, transmission, retrieval; artificial intelligence; biological and medical image processing; color and multispectral processing; computational imaging; multi-view and stereoscopic processing; multimedia security; surveillance and remote sensing, and virtual reality.The ICIG 2023 is a biennial conference that focuses on innovative technologies of image, video and graphics processing and fostering innovation, entrepreneurship, and networking. It will feature world-class plenary speakers, exhibits, and high-quality peer reviewed oral and poster presentations.Table of Contents​Biological and Medical Image Processing: An efficient medical image fusion via online convolutional sparse coding with sample-dependent dictionary.- Multimodal Medical Image Fusion based on Multichannel Aggregated Network.- Multi-Path Feature Fusion and Channel Feature Pyramid for Brain Tumor Segmentation in MRI.- Near infrared video heart rate detection based on multi-region selection and robust principal component analysis- Neural video: A novel framework for interpreting the spatiotemporal activities of the human brain.- Local Fusion Synthetic CT Network for Improving the Quality of CBCT in Cervical Cancer Radiotherapy.- Causality-Inspired Source-Free Domain Adaptation for Medical Image Classification.- Pixel-Correlation-Based Scar Screening in Hypertrophic Myocardium.- Dictionary Matching Based 2D Thin Slice Generalized Slice-dithered Enhanced Resolution (gSlider) Variable Flip Angle T1 Mapping.- Deep Low-rank Multimodal Fusion with Inter-modal Distribution Difference Constraint for ASD Diagnosis.- Cross-domain Tongue Image Segmentation based on Deep Adversarial Networks and Entropy Minimization.- Residual Inter-slice Feature Learning for 3D Organ Segmentation.- Color and Multispectral Processing: A Cross-paired Wavelet Based Spatiotemporal Fusion Network for Remote Sensing Images.- Coupled Dense Convolutional Neural Networks with Autoencoder For Unsupervised Hyperspectral Super Resolution.- Computational Imaging: Multi-scale Non-Local Bidirectional Fusion for Video Super-Resolution.- Multi-View and Stereoscopic Processing: Learning a Deep Fourier Channel Attention Generative Adversarial Network for Light Field Image Super-Resolution.- Synthesizing a large scene with multiple NeRFs.- Neural Implicit 3D Shapes from Single Images with Spatial Patterns.- Multimedia Security: Dense Visible Watermark Removal with Progressive Feature Propagation.- When Diffusion Model Meets with Adversarial Attack: Generating Transferable Adversarial Examples on Face Recognition.- TPM: Two-stage prediction mechanism for universal adversarial patch defense.- Surveillance and remote sensing: WSAD-Net: Weakly Supervised Anomaly Detection in Untrimmed Surveillance Videos.- Multi-object Tracking in Remote Sensing Video Based on Motion and Multi-scale Local Cost Volume.- Density Map Augmentation-based Point-to-point Vehicle Counting and Localization in Remote Sensing Imagery with Limited Resolution.- Large Window Attention based Transformer Network for Change Detection of Remote Sensing Images.- U-TEN:An Unsupervised Two-branch Enhancement Network for Object Detection under Complex-Light Condition.- Virtual Reality: Design of virtual assembly guidance system based on mixed reality.- Blind Omnidirectional Image Quality Assessment Based on Swin Transformer with Scanpath-Oriented.- Audio-visual Saliency for Omnidirectional Videos.

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Book SynopsisThis three-volume set LNCS 14406-14408 constitutes the refereed proceedings of the 7th Asian Conference on Pattern Recognition, ACPR 2023, held in Kitakyushu, Japan, in November 2023. The 93 full papers presented were carefully reviewed and selected from 164 submissions. The conference focuses on four important areas of pattern recognition: pattern recognition and machine learning, computer vision and robot vision, signal processing, and media processing and interaction, covering various technical aspects.Table of Contentsartificial intelligence.- computer networks.- computer science.- computer systems.- computer vision.- databases.- education.- engineering.- image analysis.- image processing.- image segmentation.- internet learning.- machine learning.- mathematics.- neural networks.- object recognition.- pattern recognition semantics.- signal processing.

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Book SynopsisThis three-volume set LNCS 14406-14408 constitutes the refereed proceedings of the 7th Asian Conference on Pattern Recognition, ACPR 2023, held in Kitakyushu, Japan, in November 2023. The 93 full papers presented were carefully reviewed and selected from 164 submissions. The conference focuses on four important areas of pattern recognition: pattern recognition and machine learning, computer vision and robot vision, signal processing, and media processing and interaction, covering various technical aspects.Table of Contentsartificial intelligence.- computer networks.- computer science.- computer systems.- computer vision.- databases.- education.- engineering.- image analysis.- image processing.- image segmentation.- internet learning.- machine learning.- mathematics.- neural networks.- object recognition.- pattern recognition semantics.- signal processing.

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Book SynopsisThis book offers an overview of the field of continuous biometric authentication systems, which capture and continuously authenticate biometrics from user devices. This book first covers the traditional methods of user authentication and discusses how such techniques have become cumbersome in the world of mobile devices and short usage sessions. The concept of continuous biometric authentication systems is introduced and their construction is discussed. The different biometrics that these systems may utilise (e.g.: touchscreen-gesture interactions) are described and relevant studies surveyed. It also surveys important considerations and challenges.This book brings together a wide variety of key motivations, components and advantages of continuous biometric authentication systems. The overview is kept high level, so as not to limit the scope to any single device, biometric trait, use-case, or scenario. Therefore, the contents of this book are applicable to devices ranging from smartphones to desktop computers, utilising biometrics ranging from face recognition to keystroke dynamics. It also provides metrics from a variety of existing systems such that users can identify the advantages and disadvantages of different approaches.This book targets researchers and lecturers working in authentication, as well as advanced-level students in computer science interested in this field. The book will also be of interest to technical professionals working in cyber security.Table of ContentsIntroduction.- Traditional Authentication.- Continuous Authentication.- Biometrics for Continuous Authentication.- Considerations and Challenges.- Conclusion.

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Book SynopsisResearchers, academicians and professionals expone in this book their research in the application of intelligent computing techniques to software engineering. As software systems are becoming larger and complex, software engineering tasks become increasingly costly and prone to errors. Evolutionary algorithms, machine learning approaches, meta-heuristic algorithms, and others techniques can help the effi ciency of software engineering.

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Book SynopsisThis book explores the possible applications of Artificial Intelligence in Virtual environments. These were previously mainly associated with gaming, but have largely extended their area of application, and are nowadays used for promoting collaboration in work environments, for training purposes, for management of anxiety and pain, etc.. The development of Artificial Intelligence has given new dimensions to the research in this field.

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Book SynopsisCognitive computing simulates human thought processes with self-learning algorithms that utilize data mining, pattern recognition, and natural language processing. The integration of deep learning improves the performance of Cognitive computing systems in many applications, helping in utilizing heterogeneous data sets and generating meaningful insights.

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Book SynopsisThis book focuses on the use of AI/ML-based techniques to solve issues related to IoT-based environments, as well as their applications. It addresses, among others, signal detection, channel modeling, resource optimization, routing protocol design, transport layer optimization, user/application behavior prediction, software-defi ned networking, congestion control, communication network optimization, security, and anomaly detection.

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Book SynopsisThis engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com) Trade Review “The book is more than a typical manual. In fact, the author himself designates it as a textbook for an introductory course on data science. The chapters are richly equipped with exercises. The topics are always explained starting with a proper motivation and continuing with practical examples. This is perhaps the most outstanding feature of the book. It can serve as a regular textbook for an academic course. In fact, I should like to recommend it exactly for this purpose. On the other hand, it provides a wealth of material for people from industry, such as software engineers, and can serve as a manual for them to accomplish data science tasks. It should be noted that the book is not just a text, but a much more complex product, including a full set of lecture slides available online as well as a solutions wiki.” (P. Navrat, Computing Reviews, February, 23, 2018) ​Table of ContentsWhat is Data Science? Mathematical Preliminaries Data Munging Scores and Rankings Statistical Analysis Visualizing Data Mathematical Models Linear Algebra Linear and Logistic Regression Distance and Network Methods Machine Learning Big Data: Achieving Scale

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Book SynopsisThe two-volume set LNCS 10269 and 10270 constitutes the refereed proceedings of the 20th Scandinavian Conference on Image Analysis, SCIA 2017, held in Tromsø, Norway, in June 2017. The 87 revised papers presented were carefully reviewed and selected from 133 submissions. The contributions are structured in topical sections on history of SCIA; motion analysis and 3D vision; pattern detection and recognition; machine learning; image processing and applications; feature extraction and segmentation; remote sensing; medical and biomedical image analysis; faces, gestures and multispectral analysis.Table of ContentsHistory of SCIA.- Motion analysis and 3D vision.- Pattern detection and recognition.- Machine learning.- Image processing and applications.- Feature extraction and segmentation.- Remote sensing.- Medical and biomedical image analysis.- Faces, gestures and multispectral analysis.

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With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different applications. However, because of the nature of “black-box” in ML methods, ML still needs to be interpreted to link human and machine learning for transparency and user acceptance of delivered solutions. This edited book addresses such links from the perspectives of visualisation, explanation, trustworthiness and transparency. The book establishes the link between human and machine learning by exploring transparency in machine learning, visual explanation of ML processes, algorithmic explanation of ML models, human cognitive responses in ML-based decision making, human evaluation of machine learning and domain knowledge in transparent ML applications. This is the first book of its kind to systematically understand the current active research activities and outcomes related to human and machine learning. The book will not only inspire researchers to passionately develop new algorithms incorporating human for human-centred ML algorithms, resulting in the overall advancement of ML, but also help ML practitioners proactively use ML outputs for informative and trustworthy decision making. This book is intended for researchers and practitioners involved with machine learning and its applications. The book will especially benefit researchers in areas like artificial intelligence, decision support systems and human-computer interaction.

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Book SynopsisDas Rahmenthema der 25. Jahrestagung der Deutschen Gesellschaft fur Medizinische DOkumentation, Informatik und Statistik e. V. - Nachsorge und Krankheitsverlaufsanalyse - hat einen engen Bezug zu aktuellen Problemen des Gesundheitswesens. Insbesondere in Kliniken, in denen schwere Erkrankungen mit modernen erfolgversprechenden Massnahmen be- handelt werden, wird die Notwendigkeit einer systematischen weiteren Uberwachung dieser Patienten immer dringlicher erachtet. Die Beob- achtung des weiteren Schicksals, die arztliche Betreuung und die Bewertung der zeitlichen Verlaufe ergeben Ansatzpunkte fur die weitere Verbesserung der Therapie. Diese Aufgabe lasst sich nur be- waltigen, wenn die dabei auftretenden Probleme der planvollen Doku- mentation, der Informationsubermittlung, der Datenspeicherung und der statistischen Auswertung von den Vertretern unseres Faches aktiv in Angriff genommen werden. Nach 25 Jahren einer sturmischen techno- logischen und Methodenentwicklung ist unsere junge medizinische Dis- ziplin in der Lage - wenn die erforderliche apparative und perso- nelle Ausstattung zur Verfugung steht -, die Probleme der Nachsorge und Krankheitsverlaufsanalyse in Zusammenarbeit mit Klinikern und Allgemeinmedizinern wirksam zu bearbeiten und Ergebnisse zu zeitigen, die fur den Arzt relevant sind. Die Tagung soll dazu Anregungen ver- mitteln und Losungswege aufzeigen. Funf Workshops und apparative, organisatorische und methodische Pro- bleme runden den Bezug unserer Arbeit auf die Probleme der !1edizin von heute ab. Erlangen hat eine traditionelle Verbundenheit mit der technologischen Entwicklung in der Medizin. Auch heute besteht, sowohl in der Medi- zinischen wie in der Technischen Fakultat, auf deren Campus wir tragen, ein waches Interesse fur den humanen Einsatz technologischer Neuerungen, insbesondere auch der elektronischen Datenverarbeitung. L.Table of ContentsErÖffnung der 25. Jahrestagung der Deutschen Gesellschaft FÜr Medizinische Dokumentation, Informatik und Statistik.- Zufall und lebendiges Geschehen.- Nachsorge und Krankheitsverlaufsanalyse-Einführung in die Thematik.- Nachsorge nach Krebsoperationen.- Probleme der Verlaufsbeobachtung und der prognostischen Beurteilung bei Herzkrankheiten.- Prognosestellung beim Rektumkarzinom mit Hilfe des COX-Modells.- Mathematische Modelle zur Analyse des Krankheitsverlaufs von Patienten mit Hirntumoren.- Analyse des Krankheitsverlaufs bei Prostatakarzinompatienten.- Statistische Auswertung des Krankheitsverlaufs von Tumorpatienten am Beispiel einer Studie über Karzinome der Mundhöhle.- Mehrkompartiment-Modelle in der Carcinogenese:Numerische Realisierung der Kleinste-Quadrate-Anpassung von Konzentrationsmessungen in der Maus.- Latenzzeitmessung bei Krebs am Beispiel einer Seite Fall-Kontroll-Studie an Lymphom- und Leukämiefällen in der amerikanischen Reifen- und Gummiindustrie.- Verlaufsuntersuchungen bei oralen Leukoplakien und Carcinomen.- Verteilungsfreie Teststatistiken bei Zen- sorierten Daten - Neue Entwicklungen.- Mathematisches Modell zur Prognose des Krankheitsverlaufs der Hepatitis B.- Methodische Probleme bei Langzeitstudien; insbesondere das Problem des Therapie-Abbruchs.- Nichtparametrischer Vergleich zweier Scharen von Verlaufskurven.- Anwendung eines Kompartimentmodelles zur Beurteilung von Behandlungsmethoden.- Probleme der statistischen Analyse einer Kohlenhydrat-Infusionsstudie.- Parametrische Tests für den Vergleich von Mittelwertsprofilen bei unverbundenen Beobachtungen mit homogenen Varianzen.- Variabilitätsuntersuchungen wesentlicher Spektralparameter im Verlaufe von EEG- Routine-Ableitungen.- Alternativen zur Bonferroni-Prozedur.- Variablenselektion bei multinomialen Klassifikationsproblemen.- Zur Problematik der Beurteilung abhängiger Häufigkeiten.- Explorative Datenanalyse - Schlußfolgerungen aus der Frühjahrstagung.- Die Integration der Nachsorgeorganisation und der Krankheitsverlaufsorganisation in ein allgemeines Befunddokumentationssystem.- Computerunterstützte Nachsorge und Krankheitsverlaufsanalyse – eine Komponente des medizinischen Auswertungssystems WAMAS.- Basisfunktionen für die Analyse von Verlaufsdaten.- ZEISIG Zytologisches Erfassungs- und Informationssystem in der Gynäkologie.- Betriebsärztliche Informationssysteme Schlußfolgerungen aus der Frühjahrstagung 1980.- Paket-Konzept und Refinement-Konstrukt Erste Erfahrungen mit einem Software-Entwicklungs-Instrument.- Verfahren zur Vereinheitlichung der Darstellung und Speicherung von Laborresultaten.- Implementierung eines Datenmodells auf einer operativen Intensivstation.- Das computergestützte Nachsorgesystem der I. Chirutgischen Universitätsklinik in Wien.- Computer-gestützte Nachsorge von Schrittmacher-Patienten.- Befunddokumentation in der hämostaseologischen Ambulanz der Medizinischen Hochschule Hannover.- Zur Frage des Aussagewertes einer routinemäßigen Thoraxübersichtsaufnahme bei der Diagnostik des Emphysems der Quarzstaublunge und dem Cor Pulmonale.- Auswertung von Krankheitsverläufen - Probleme und Lösungsmöglichkeiten: Dargestellt am Beispiel der akuten Virushepatitis.- Stoffwechselmetaboliten-Verlauf unter 48-stündiger Dauerinfusion von Glukose allein und in Mischung mit Sorbit, Fruktose oder Xylit bei Diabetikern.- EDV- Einsatz für die Bakteriologische Verlaufsund Befunddokumentation.- Erfassen und Auswerten von Antibiogrammen.- Institutionskarrieren schizophrener Kranker.- Das Fallregister psychisch Behinderter am PLK Weinsberg. Konzeption, Realisierung und erste Erfahrungen.- Prognose und Probleme der Verlaufsbeobachtung fokaler zerebraler Ischämie/Infarkte bei jungen Erwachsenen.- Langzeitverlauf nach Karotis-Operationen: Bedeutung der Neuropsychiatrischen Symptomatik.- Neuere Entwicklungen und Technologische Möglichkeiten der Mikroelektronik.- Ein Mikrorechner für die Eingliederung eines Analysenautomaten in dezentral organisierte Laborautomatisierungssysteme.- Mikroprozessoreinsatz im Physiologischen Labor.- Zur Bestimmung der Pulswellengeschwindigkeit.- On-line Verarbeitung von Hämoglobin-Reflexionsspektren hoher Repetitionsraten.- Anforderung an ein Mikroprozessorsystem zur Biosignalverarbeitung.- Entwurf und Aufbau eines Mikroprozessorsystems zur Biosignalverarbeitung.- Ein Mikrocomputer als Subsystem im 24-Stunden Betrieb.- Der Mikroprozessor als integrierender Bestandteil eines autonomen Meßplatzes im klinischen Laboratorium.- Implementierung des Programmes HES EKG in vor Ort auswertende Mikroprozessoren.- Erfahrungen im 3-jährigen Einsatz eines dezentralen Dokumentations- und Auskunftssystems für chronisch Kranke mit einem Minicomputer.- Ergebnisbericht der Moderatoren: Workshop 1 Mikroelektronik in der Medizin.- Die Basisdokumentation für Tumorkranke der Arbeitsgemeinschaft Deutscher Tumorzentren (ADT).- Das klinische Krebsregister des Tumorzentrums Köln.- Das Register für Onkologische Nachsorge der GBK in Münster.- Bericht über ein computergestütztes klinischpathologisches Krebsregister der ersten Ausbaustufe.- Ein klinisches Krebsregister als Basis für Nachsorge und statistische Auswertung – ein Erfahrungsbericht.- Das Dokumentations-, Kommunikations- und Organisations-System des Tumorzentrums Heidelberg/ Mannheim mit KRAZTUR.- Computerunterstützte Nachsorge und Basisdokumentation in der Radioonkologie.- Ein Patienteninformationssystem für die Strahlentherapie – Nachsorgeorganisation und Langzeitanalyse -.- Kooperative Dokumentation von Malignomen im Kindesalter.- Computerunterstütztes Magenbiopsieregister.- Computergestützte Erfassung und Nachsorge von Patienten mit kolorektalen Polypen.- Ergebnisbericht der Moderatoren: Workshop 2 Dokumentation, Datenverarbeitung und Statistik in medizinischen Krebszentren.- Therapiestudien im Kontext der Evaluationsforschung.- Organisatorische und methodische Probleme bei der Durchführung kontrollierter Psychopharmakastudien in der Praxis niedergelassener Ärzte.- Methodology and results of a long-term, controlled study of the effectiveness of immunosuppressive treatment of multiple sclerosis.- Der Wirksamkeitsnachweis in der Therapie des Ovarialkarzinoms.- Strategien zum Abbruch von kontrollierten Therapiestudien - Probleme und gegenwärtig diskutierte Ansätze.- Integrierung von Beobachtungen aus dem nichtärztlichen Bereich in die Krankheitsverlaufsanalysen.- Ergebnisbericht der Moderatoren: Workshop 3 Kontrollierte klinische Studien.- Klinische Datenverarbeitung in der Fakultät für Medizin der Technischen Universität München.- Klinische Basisdokumentation als Teil eines Informations-Systems in einem Rehabilitations-Krankenhaus Konzeption und Implementierung.- Klinische Dokumentation an einer Neurochirurgischen Klinik.- Dialogunterstützte klinische Dokumentation am Universitätsklinikum Göttingen.- Ergebnisbericht der Moderatoren: Workshop 4 Dokumentation und Verarbeitung klinischer Daten.- Verwaltung und Krankenhaus-Informationssystem Eine Strukturanalyse.- Untersuchung zur Inanspruchnahme eines Universitäts klinikums im stationären und ambulanten Bereich - durchgeführt an den Universitätskliniken Marburg.- Sind “Kurzlieger” einer Medizinischen Klinik für die Unterbringung in Hostelbetten geeignet? Die Bedeutung der Diagnosestatistik bei einer Planungsaufgabe.- Personalbedarfsplanung für den Krankenhaus-Pflegebereich mit Modellen der linearen Programmierung.- Lagerhaltung verderblicher medizinischer Güter.- Bedarfsgesteuerte Blutspenden mit TRAMIDIS.- Ergebnisbericht der Moderatoren: Workshop 5 Medizinökonomie.- Autorenverzeichnis.

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Book SynopsisNeural networks are a computing paradigm that is finding increasing attention among computer scientists. In this book, theoretical laws and models previously scattered in the literature are brought together into a general theory of artificial neural nets. Always with a view to biology and starting with the simplest nets, it is shown how the properties of models change when more general computing elements and net topologies are introduced. Each chapter contains examples, numerous illustrations, and a bibliography. The book is aimed at readers who seek an overview of the field or who wish to deepen their knowledge. It is suitable as a basis for university courses in neurocomputing.Trade Review"If you want a systematic and thorough overview of neural networks, need a good reference book on this subject, or are giving or taking a course on neural networks, this book is for you." Computing ReviewsTable of Contents1. The Biological Paradigm.- 1.1 Neural computation.- 1.1.1 Natural and artificial neural networks.- 1.1.2 Models of computation.- 1.1.3 Elements of a computing model.- 1.2 Networks of neurons.- 1.2.1 Structure of the neurons.- 1.2.2 Transmission of information.- 1.2.3 Information processing at the neurons and synapses.- 1.2.4 Storage of information — learning.- 1.2.5 The neuron — a self-organizing system.- 1.3 Artificial neural networks.- 1.3.1 Networks of primitive functions.- 1.3.2 Approximation of functions.- 1.3.3 Caveat.- 1.4 Historical and bibliographical remarks.- 2. Threshold Logic.- 2.1 Networks of functions.- 2.1.1 Feed-forward and recurrent networks.- 2.1.2 The computing units.- 2.2 Synthesis of Boolean functions.- 2.2.1 Conjunction, disjunction, negation.- 2.2.2 Geometric interpretation.- 2.2.3 Constructive synthesis.- 2.3 Equivalent networks.- 2.3.1 Weighted and unweighted networks.- 2.3.2 Absolute and relative inhibition.- 2.3.3 Binary signals and pulse coding.- 2.4 Recurrent networks.- 2.4.1 Stored state networks.- 2.4.2 Finite automata.- 2.4.3 Finite automata and recurrent networks.- 2.4.4 A first classification of neural networks.- 2.5 Harmonic analysis of logical functions.- 2.5.1 General expression.- 2.5.2 The Hadamard—Walsh transform.- 2.5.3 Applications of threshold logic.- 2.6 Historical and bibliographical remarks.- 3.Weighted Networks — The Perceptron.- 3.1 Perceptrons and parallel processing.- 3.1.1 Perceptrons as weighted threshold elements.- 3.1.2 Computational limits of the perceptron model.- 3.2 Implementation of logical functions.- 3.2.1 Geometric interpretation.- 3.2.2 The XOR problem.- 3.3 Linearly separable functions.- 3.3.1 Linear separability.- 3.3.2 Duality of input space and weight space.- 3.3.3 The error function in weight space.- 3.3.4 General decision curves.- 3.4 Applications and biological analogy.- 3.4.1 Edge detection with perceptrons.- 3.4.2 The structure of the retina.- 3.4.3 Pyramidal networks and the neocognitron.- 3.4.4 The silicon retina.- 3.5 Historical and bibliographical remarks.- 4. Perceptron Learning.- 4.1 Learning algorithms for neural networks.- 4.1.1 Classes of learning algorithms.- 4.1.2 Vector notation.- 4.1.3 Absolute linear separability.- 4.1.4 The error surface and the search method.- 4.2 Algorithmic learning.- 4.2.1 Geometric visualization.- 4.2.2 Convergence of the algorithm.- 4.2.3 Accelerating convergence.- 4.2.4 The pocket algorithm.- 4.2.5 Complexity of perceptron learning.- 4.3 Linear programming.- 4.3.1 Inner points of polytopes.- 4.3.2 Linear separability as linear optimization.- 4.3.3 Karmarkar’s algorithm.- 4.4 Historical and bibliographical remarks.- 5. Unsupervised Learning and Clustering Algorithms.- 5.1 Competitive learning.- 5.1.1 Generalization of the perceptron problem.- 5.1.2 Unsupervised learning through competition.- 5.2 Convergence analysis.- 5.2.1 The one-dimensional case — energy function.- 5.2.2 Multidimensional case — the classical methods.- 5.2.3 Unsupervised learning as minimization problem.- 5.2.4 Stability of the solutions.- 5.3 Principal component analysis.- 5.3.1 Unsupervised reinforcement learning.- 5.3.2 Convergence of the learning algorithm.- 5.3.3 Multiple principal components.- 5.4 Some applications.- 5.4.1 Pattern recognition.- 5.4.2 Image compression.- 5.5 Historical and bibliographical remarks.- 6. One and Two Layered Networks.- 6.1 Structure and geometric visualization.- 6.1.1 Network architecture.- 6.1.2 The XOR problem revisited.- 6.1.3 Geometric visualization.- 6.2 Counting regions in input and weight space.- 6.2.1 Weight space regions for the XOR problem.- 6.2.2 Bipolar vectors.- 6.2.3 Projection of the solution regions.- 6.2.4 Geometric interpretation.- 6.3 Regions for two layered networks.- 6.3.1 Regions in weight space for the XOR problem.- 6.3.2 Number of regions in general.- 6.3.3 Consequences.- 6.3.4 The Vapnik—Chervonenkis dimension.- 6.3.5 The problem of local minima.- 6.4 Historical and bibliographical remarks.- 7. The Backpropagation Algorithm.- 7.1 Learning as gradient descent.- 7.1.1 Differentiable activation functions.- 7.1.2 Regions in input space.- 7.1.3 Local minima of the error function.- 7.2 General feed-forward networks.- 7.2.1 The learning problem.- 7.2.2 Derivatives of network functions.- 7.2.3 Steps of the backpropagation algorithm.- 7.2.4 Learning with backpropagation.- 7.3 The case of layered networks.- 7.3.1 Extended network.- 7.3.2 Steps of the algorithm.- 7.3.3 Backpropagation in matrix form.- 7.3.4 The locality of backpropagation.- 7.3.5 Error during training.- 7.4 Recurrent networks.- 7.4.1 Backpropagation through time.- 7.4.2 Hidden Markov Models.- 7.4.3 Variational problems.- 7.5 Historical and bibliographical remarks.- 8. Fast Learning Algorithms.- 8.1 Introduction — classical backpropagation.- 8.1.1 Backpropagation with momentum.- 8.1.2 The fractal geometry of backpropagation.- 8.2 Some simple improvements to backpropagation.- 8.2.1 Initial weight selection.- 8.2.2 Clipped derivatives and offset term.- 8.2.3 Reducing the number of floating-point operations.- 8.2.4 Data decorrelation.- 8.3 Adaptive step algorithms.- 8.3.1 Silva and Almeida’s algorithm.- 8.3.2 Delta-bar-delta.- 8.3.3 Rprop.- 8.3.4 The Dynamic Adaption algorithm.- 8.4 Second-order algorithms.- 8.4.1 Quickprop.- 8.4.2 QRprop.- 8.4.3 Second-order backpropagation.- 8.5 Relaxation methods.- 8.5.1 Weight and node perturbation.- 8.5.2 Symmetric and asymmetric relaxation.- 8.5.3 A final thought on taxonomy.- 8.6 Historical and bibliographical remarks.- 9. Statistics and Neural Networks.- 9.1 Linear and nonlinear regression.- 9.1.1 The problem of good generalization.- 9.1.2 Linear regression.- 9.1.3 Nonlinear units.- 9.1.4 Computing the prediction error.- 9.1.5 The jackknife and cross-validation.- 9.1.6 Committees of networks.- 9.2 Multiple regression.- 9.2.1 Visualization of the solution regions.- 9.2.2 Linear equations and the pseudoinverse.- 9.2.3 The hidden layer.- 9.2.4 Computation of the pseudoinverse.- 9.3 Classification networks.- 9.3.1 An application: NETtalk.- 9.3.2 The Bayes property of classifier networks.- 9.3.3 Connectionist speech recognition.- 9.3.4 Autoregressive models for time series analysis.- 9.4 Historical and bibliographical remarks.- 10. The Complexity of Learning.- 10.1 Network functions.- 10.1.1 Learning algorithms for multilayer networks.- 10.1.2 Hilbert’s problem and computability.- 10.1.3 Kolmogorov’s theorem.- 10.2 Function approximation.- 10.2.1 The one-dimensional case.- 10.2.2 The multidimensional case.- 10.3 Complexity of learning problems.- 10.3.1 Complexity classes.- 10.3.2 NP-complete learning problems.- 10.3.3 Complexity of learning with AND-OR networks.- 10.3.4 Simplifications of the network architecture.- 10.3.5 Learning with hints.- 10.4 Historical and bibliographical remarks.- 11. Fuzzy Logic.- 11.1 Fuzzy sets and fuzzy logic.- 11.1.1 Imprecise data and imprecise rules.- 11.1.2 The fuzzy set concept.- 11.1.3 Geometric representation of fuzzy sets.- 11.1.4 Fuzzy set theory, logic operators, and geometry.- 11.1.5 Families of fuzzy operators.- 11.2 Fuzzy inferences.- 11.2.1 Inferences from imprecise data.- 11.2.2 Fuzzy numbers and inverse operation.- 11.3 Control with fuzzy logic.- 11.3.1 Fuzzy controllers.- 11.3.2 Fuzzy networks.- 11.3.3 Function approximation with fuzzy methods.- 11.3.4 The eye as a fuzzy system — color vision.- 11.4 Historical and bibliographical remarks.- 12. Associative Networks.- 12.1 Associative pattern recognition.- 12.1.1 Recurrent networks and types of associative memories.- 12.1.2 Structure of an associative memory.- 12.1.3 The eigenvector automaton.- 12.2 Associative learning.- 12.2.1 Hebbian learning — the correlation matrix.- 12.2.2 Geometric interpretation of Hebbian learning.- 12.2.3 Networks as dynamical systems — some experiments.- 12.2.4 Another visualization.- 12.3 The capacity problem.- 12.4 The pseudoinverse.- 12.4.1 Definition and properties of the pseudoinverse.- 12.4.2 Orthogonal projections.- 12.4.3 Holographic memories.- 12.4.4 Translation invariant pattern recognition.- 12.5 Historical and bibliographical remarks.- 13. The Hopfield Model.- 13.1 Synchronous and asynchronous networks.- 13.1.1 Recursive networks with stochastic dynamics.- 13.1.2 The bidirectional associative memory.- 13.1.3 The energy function.- 13.2 Definition of Hopfield networks.- 13.2.1 Asynchronous networks.- 13.2.2 Examples of the model.- 13.2.3 Isomorphism between the Hopfield and Ising models.- 13.3 Converge to stable states.- 13.3.1 Dynamics of Hopfield networks.- 13.3.2 Covergence proof.- 13.3.3 Hebbian learning.- 13.4 Equivalence of Hopfield and perceptron learning.- 13.4.1 Perceptron learning in Hopfield networks.- 13.4.2 Complexity of learning in Hopfield models.- 13.5 Parallel combinatorics.- 13.5.1 NP-complete problems and massive parallelism.- 13.5.2 The multiflop problem.- 13.5.3 The eight rooks problem.- 13.5.4 The eight queens problem.- 13.5.5 The traveling salesman.- 13.5.6 The limits of Hopfield networks.- 13.6 Implementation of Hopfield networks.- 13.6.1 Electrical implementation.- 13.6.2 Optical implementation.- 13.7 Historical and bibliographical remarks.- 14. Stochastic Networks.- 14.1 Variations of the Hopfield model.- 14.1.1 The continuous model.- 14.2 Stochastic systems.- 14.2.1 Simulated annealing.- 14.2.2 Stochastic neural networks.- 14.2.3 Markov chains.- 14.2.4 The Boltzmann distribution.- 14.2.5 Physical meaning of the Boltzmann distribution.- 14.3 Learning algorithms and applications.- 14.3.1 Boltzmann learning.- 14.3.2 Combinatorial optimization.- 14.4 Historical and bibliographical remarks.- 15. Kohonen Networks.- 15.1 Self-organization.- 15.1.1 Charting input space.- 15.1.2 Topology preserving maps in the brain.- 15.2 Kohonen’s model.- 15.2.1 Learning algorithm.- 15.2.2 Mapping high-dimensional spaces.- 15.3 Analysis of convergence.- 15.3.1 Potential function — the one-dimensional case.- 15.3.2 The two-dimensional case.- 15.3.3 Effect of a unit’s neighborhood.- 15.3.4 Metastable states.- 15.3.5 What dimension for Kohonen networks?.- 15.4 Applications.- 15.4.1 Approximation of functions.- 15.4.2 Inverse kinematics.- 15.5 Historical and bibliographical remarks.- 16. Modular Neural Networks.- 16.1 Constructive algorithms for modular networks.- 16.1.1 Cascade correlation.- 16.1.2 Optimal modules and mixtures of experts.- 16.2 Hybrid networks.- 16.2.1 The ART architectures.- 16.2.2 Maximum entropy.- 16.2.3 Counterpropagation networks.- 16.2.4 Spline networks.- 16.2.5 Radial basis functions.- 16.3 Historical and bibliographical remarks.- 17. Genetic Algorithms.- 17.1 Coding and operators.- 17.1.1 Optimization problems.- 17.1.2 Methods of stochastic optimization.- 17.1.3 Genetic coding.- 17.1.4 Information exchange with genetic operators.- 17.2 Properties of genetic algorithms.- 17.2.1 Convergence analysis.- 17.2.2 Deceptive problems.- 17.2.3 Genetic drift.- 17.2.4 Gradient methods versus genetic algorithms.- 17.3 Neural networks and genetic algorithms.- 17.3.1 The problem of symmetries.- 17.3.2 A numerical experiment.- 17.3.3 Other applications of GAs.- 17.4 Historical and bibliographical remarks.- 18. Hardware for Neural Networks.- 18.1 Taxonomy of neural hardware.- 18.1.1 Performance requirements.- 18.1.2 Types of neurocomputers.- 18.2 Analog neural networks.- 18.2.1 Coding.- 18.2.2 VLSI transistor circuits.- 18.2.3 Transistors with stored charge.- 18.2.4 CCD components.- 18.3 Digital networks.- 18.3.1 Numerical representation of weights and signals.- 18.3.2 Vector and signal processors.- 18.3.3 Systolic arrays.- 18.3.4 One-dimensional structures.- 18.4 Innovative computer architectures.- 18.4.1 VLSI microprocessors for neural networks.- 18.4.2 Optical computers.- 18.4.3 Pulse coded networks.- 18.5 Historical and bibliographical remarks.

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Book SynopsisDie Biometrie ist eine neue Arbeitsmethode zur Aufklärung der Ursachen des Phänomens der reproduzierbaren Bewegung in der Biologie. Die Arbeitsweise der Biometrie macht es möglich, aus dem Längenverhältnis der Kreuzbänder des Kniegelenkes und der konkreten Längenangabe des hinteren Kreuzbandes das Kniesteuersystem a priori räumlich zu entwickeln, die Beinlänge und die dazugehörende Körpergröße zu bestimmen und das Hüftgelenk aus dem Kniesteuersystem abzuleiten. Die Erkenntnis, daß die unbekannten biologischen Bewegungssysteme selbstverwirklichte biometrische kräftefreie Gesetzlichkeiten verkörpern, ist richtungsweisend für alle Disziplinen, die sich mit dem Lebendigen beschäftigen.Table of ContentsEinführung.- I Empirisch-geometrische Untersuchungen des Kniegelenks. Historischer Überblick, Problemstellung, Bandstrukturen, Beuge- und Streckbewegung sowie Transversalbewegung.- 1 Historischer Überblick und Problemstellung.- 2 Klassische und relativistische Denkweise in der Physik.- 3 Grundsätzliches zur Analyse unbekannter Bewegungssysteme.- 3.1 Die nicht zielführende Analyse eines unbekannten Bewegungssystems.- 3.2 Die zielführende Analyse eines „unbekannten“ technischen Bewegungssystems.- 3.2.1 Untersuchung aus der Bewegung heraus.- 3.2.2 Untersuchung von Bewegungssystemen in Ruhelage.- 3.3 Die zielführende Analyse eines unbekannten, vom menschlichen Geist nicht erfundenen biologischen Bewegungssystems (Kniegelenk).- 4 Kinematik der Beuge- und Streckbewegung des Kniegelenks.- 4.1 Kinematik der Kreuzbänder.- 4.2 Achsen des Kniegelenks und Krümmungsmittelpunkte der Oberschenkelkondylen (Koppelhüllkurven und Hüllflächen).- 4.3 Kondylenformen.- 4.4 Dach der Fossa intercondylaris.- 4.5 Retroposition des Condylus femoris und des Tibiaplateaus.- 4.6 Abroll- und Gleitbewegung von Ober- und Unterschenkelkondylen.- 5 Die orthogonale Kraftübertragung an den Berührungsstellen der Gelenkflächen.- 6 Die Geschwindigkeitsverteilung bei der Bewegung des Unterschenkels.- 7 Das Kniegelenk — ein stufenloses Getriebe.- 8 Die Schlußrotation und die sekundäre Verformung der Oberschenkelkondylen.- 8.1 Schlußrotation.- 8.2 Condylus lateralis femoris.- 8.3 Condylus medialis femoris.- 9 Die kinematische Beziehung der Kreuzbänder (Steuersystem) zu den Kollateralbändern — Scheitel- und Angelkubik.- 9.1 Lig. collaterale mediale.- 9.2 Das Lig. collaterale laterale.- 9.3 Der Ball-Punkt.- 9.4 Definition der Scheitel- und Angelkubik.- 9.5 Zur Konstruktion der Scheitel- und Angelkubik.- 9.6 Symmetrische Winkelschleife.- 10 Das Kniegelenk — ein Vierstabgetriebe.- 11 Die Synoviapumpe des Kniegelenks.- 11.1 Die Gelenkkapsel.- 12 Pro- und Supination des Unterschenkels und die Gegenbewegung des Oberschenkels.- 12.1 Das „nichtdurchschlagende Gelenkviereck“.- 12.2 Die Drehachsen für die Transversalbewegung (Polkurven des nichtdurchschlagenden Gelenkvierecks“).- 12.3 Der „Nachlauf des Kniegelenks.- 12.4 Die Asymptoten des „nichtdurchschlagenden Gelenkvierecks“.- II Inversion r·?r = ± c2, die grundsätzliche Beziehung des ruhenden zum bewegten System. Elementargeometrie mit Anwendungsbeispielen aus der Physik.- 13 Die inverse Transformation, das Ordnungsprinzip, der Algorithmus der Vertebraten.- 13.1 Reproduzierbare ebene Bewegung und Inversion.- 13.1.1 Ableitung der Euler-Savary-Gleichung.- 13.2 Die Inversion-das Plücker-Rechenverfahren (1834).- 13.3 Antiparallele.- 13.4 Der Inversionskreis „i“.- 13.5 Polarität.- 13.6 Potenz.- 13.7 Inversion einer Geraden.- 13.8 Winkeltreue der Inversion.- 13.9 Winkel- und Längenverhältnisse am Einheitskreis.- 13.10 Abteilung der 2. Elementargleichung.- 13.11 Das Abstandslängenverhältnis inverser Punktepaare P und P?.- 13.12 Das Längenverhältnis r: z?.- 13.13 Die duale Bedeutung von „?,“.- 13.14 Inversion eines Punktes mit dem Zirkel.- 13.15 Symmetrische Teilung einer Strecke AB mit dem Zirkel.- 13.16 Inversion einer Geraden mit dem Zirkel.- 13.17 Inversion eines Kreises mit dem Zirkel.- 13.18 Die Verhältniszahl ? vom Punkt „S“ aus betrachtet.- 13.18.1 Konstruktion der Leitlinien der Parabel mit dem Zirkel..- 13.19 Das Längenverhältnis ? und die Ellipse.- 14 Fokalkegelschnitt.- 14.1 Die Beziehung der Parameter der Ellipse und Hyperbel eines Fokalkegelschnitts.- 14.2 Die inversen Beziehungen der Parameter eines Fokalkegelschnitts.- 14.3 Scheitelkreise.- 14.4 Die Beziehung der Halbparameter pb und pe.- 14.5 Inversion der Kegelschnitte.- 15 Inversion und Influenz.- 16 Inversion und Ohm-Widerstand.- 17 Der „goldene Schnitt“ — ein Spezialfall der Inversion.- III Konstruktion des Steuersystems des Kniegelenks (windschiefes Gelenkviereck) — kinetostatische Untersuchung.- 18 Das Steuersystem der Beuge- und Streckbewegung.- 19 Die konstruktive Entwicklung des Steuersystems im Aufriß (überschlagenes Gelenkviereck).- 19.1 Das Längenverhältnis ? des vorderen und hinteren Kreuzbandes und seine damit bestimmten Winkel ?, ?, ?.- 19.2 Der Abstand f des Tibiaplateaus vom Dach der Fossa intercondylaris.- 19.3 Das Tibiaplateau p ist das inverse Abbild der Scheitelkubik des inversen Steuersystems.- 19.4 Die Wälznormale n0 und Wälztangente ?0.- 19.5 Konstruktion des Parameters h.- 19.6 Entwicklung des kleinen Steuersystems ABA*B*.- 19.7 Die Radien der zerfallenen Scheitel-und Angelkubik.- 19.8 Die Radien der Scheitelkubik rs und der Angelkubik rA durch ? und hk ausgedrückt.- 19.9 Der Winkel ? durch ? ausgedrückt.- 19.10 Der Wendekreis w und sein Durchmesser ?.- 19.11 Tibiaplateau und Dach der Fossa intercondylaris.- 19.12 Der Proportionalitätsfaktor ?.- 19.13 Der Proportionalitätsfaktor ? durch ? ausgedrückt.- 19.14 Der Durchmesser 2rA der zerfallenen Angelkubik Ak des kleinen Steuersystems und der Wendekreisdurchmesser ?0 des großen Steuersystems.- 19.15 Das Verhältnis der Konstanten sv und shk und die entsprechenden Winkel ?1 und ?2.- 19.16 Die Retroversion des Tibiaplateaus.- 20 Das Steuersystem der Transversalbewegung. Ableitung des Grundrisses — „nichtdurchschlagendes Gelenkviereck“ — aus dem Aufriß — „überschlagenes Gelenkviereck“.- 20.1 Konstruktion der Wälztangente nach Bobillier.- 20.2 Die Beziehung von rv und $$ {r_{{{h_k}}}} $$ bzw. $$ {r_{{v{d_0}}}} $$.- und $$ {r_{{hk{d_0}}}} $$ im Aufriß.- 20.3 Entwicklung des nichtdurchschlagenden Gelenkvierecks (Grundriß des Steuersystems).- 20.4 Darstellung der räumlich versetzten Kreuzbandursprünge und Ansätze (Abstandslängen a? und b?).- 20.5 Die Beziehung von d0 und d?0.- 20.6 Lage der Wälztangenten t(As) (der Winkel o).- 20.7 Die Länge des vorderen und hinteren Kreuzbandes im Grundriß.- 20.8 Die Beziehung der Koppel p0 im Aufriß zum Steg d?0 im Grundriß.- 20.9 Die Beziehung zwischen ? und ?.- 20.10 Die Projektion der Asymptotenflächen t(As) des Grundrisses im Auf- und Seitenriß.- 20.10.1 Der Winkel ? im Aufriß.- 20.11 Der Winkel ? im Seitenriß.- 20.12 Die Krümmungsverwandtschaft X?X* des „nichtdurchschlagenden Gelenkvierecks“.- 20.13 Das räumliche Steuersystem.- 20.13.1 Die wahre Länge des vorderen Kreuzbandes ?* und des hinteren Kreuzbandes hk*.- 20.13.2 Darstellung der Kreuzungswinkel ? zwischen dem vorderen Kreuzband v0* und dem hinteren Kreuzband $$ h_{{{k_o}}}^{ * } $$ durch die entsprechenden Richtungskosinusse.- IV Die konstruktive Entwicklung der Beinlänge und Körpergröße aus dem Längenverhältnis ? der Kreuzbänder.- 21 Die Beinlänge und das Bewegungssystem Oberschenkel-Unterschenkel.- 21.1 Ober-und Unterschenkellänge.- 21.2 Der „Einbau“ des Steuersystems des Kniegelenks (kleines System) in das Bewegungssystem OSCH-USCH (großes System).- 21.3 Überstreckbarkeit des Bewegungssystems OSCH-USCH.- 21.4 Das hinfällige „Nußknackerprinzip“ des Kniegelenks.- 21.5 Die Fußhöhe und die Bewegungssysteme des Beines.- 21.6 Körpergröße.- V Entwicklung des proximalen Femurendes aus den Parametern des Kniegelenks.- 22 Entwicklung des Hüftgelenks aus dem Steuersystem des Kniegelenks.- 22.1 Das geometrische Grundkonzept des Hüftkopfes und der Hüftpfanne.- 22.2 Die individuelle Form des Hüftkopfes und der Hüftpfanne.- 22.3 Die Pascal-Schnecke als meridiane Schnittfigur der Kugelkonchoide und der Rotationskugelkonchoide.- 22.4 Kugelkonchoide.- 22.5 Rotationskugelkonchoid.- 22.6 Die Beziehung von Hüftkopf und Hüftpfanne.- 22.7 Der Drehpunkt des Hüftgelenks bei Schwingbewegungen.- 22.8 Berechnung der Scheitelkreisradien der Hüftpfanne und des Hüftkopfes.- 22.9 Inversion in der Gauß-Zahlenebene.- 22.10 Hyperbolische Inversion.- 22.11 Elliptische Inversion.- 22.12 Warum ist der Hüftkopf von Masse (Zellmasse) erfüllt?.- 22.13 Warum bildet die Hüftpfanne einen Hohlraum, an dem sich die Zellmassen außen anlagern?.- 22.14 Fovea capitis femoris.- 22.15 Antetorsion des proximalen Femurendes.- 22.16 Die konstruktive Lösung der Anlenkung des Hüftkopfes an den Schenkelhals und seine Beziehung zur Oberschenkelschaftachse..- 22.17 Die analytische Lösung der Anlenkung des Hüftkopfes an den Schenkelhals und seine Beziehung zum Oberschenkelschaft (Die Beziehung von CCD-Winkel und AT-Winkel).- 22.17.1 Zentralprojektion.- 22.17.2 Die konstruktive Entwicklung des AT-Winkels und die Länge des Schenkelhalses im Grundriß aus den Parametern ah (Kehlkreis) und ?1 (Öffnungswinkel des Richtkegels).- 22.17.3 Entwicklung des CCD-Winkels und die Schenkelhalslänge im Aufriß.- 22.17.4 Konstruktive Entwicklung des CCD- und AT-Winkels und die Schenkelhalslänge aus dem Längenverhältnis der Kreuzbänder ? und dem Hüftkopfparameter ah.- 22.17.5 Der AT-Winkel und die Ursache der verschiedenen Meßwerte.- 22.18 Schwankungsbreite der Winkelwerte der reellen CCD-Winkel.- 22.18.1 Mittelwert des CCD-Winkels.- 22.18.2 Obere Grenze der mittleren Schwankungsbreite des CCD-Winkels.- 22.18.3 Untere Grenze der mittleren Schwankungsbreite des CCD-Winkels.- 22.18.4 Oberer Extremwert des CCD-Winkels.- 22.18.5 Unterer Extremwert des CCD-Winkels.- Schlußwort und Zusammenfassung.

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Book SynopsisDie einzig anerkannte, kurative Therapieform zur Behandlung primärer und sekundärer Lebertumore ist die chirurgische Resektion. Nicht alle Tumore sind aufgrund ihrer Lage resektabel. Eine Navigationsunterstützung kann hier Abhilfe leisten. Exakte Bildregistrierungsverfahren für die Registrierung von Gefäßbäumen aus CT und Ultraschall sind dazu jedoch unerlässlich. Janine Olesch stellt Verfahren vor, mit deren Hilfe im intra-operativen Kontext eine Nachführung der Planungsdaten an die intra-operative Situation gelingt. Neben der Technik der Registrierung mit inexakten Landmarken beschreibt die Autorin auch das Konzept der 2D-3D-Registrierung. Zur methodischen Beschleunigung der Verfahren kommt eine Fokussierungsstrategie zur Registrierung besonders interessanter Regionen zum Einsatz.​Table of Contents​Problemstellung Navigierte Leberchirurgie.- Medizintechnische Grundlagen.- Grundlagen der numerischen Optimierung.- Grundlagen der Bildregistrierung.- Spezialisierte Registrierungsansätze.- Anwendungen in der navigierten Leberchirurgie​.

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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 SynopsisIn the last decade unsupervised pattern discovery in time series, i.e. the problem of finding recurrent similar subsequences in long multivariate time series without the need of querying subsequences, has earned more and more attention in research and industry. Pattern discovery was already successfully applied to various areas like seismology, medicine, robotics or music. Until now an application to automotive time series has not been investigated. This dissertation fills this desideratum by studying the special characteristics of vehicle sensor logs and proposing an appropriate approach for pattern discovery. To prove the benefit of pattern discovery methods in automotive applications, the algorithm is applied to construct representative driving cycles. Table of ContentsIntroduction.- RelatedWork.- Development of Pattern Discovery Algorithms for Automotive Time Series.- Pattern-based Representative Cycles.- Evaluation.- Conclusion.

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Book SynopsisIn den letzten Jahren hat sich der Workshop "Bildverarbeitung für die Medizin" durch erfolgreiche Veranstaltungen etabliert. Ziel ist auch 2022 wieder die Darstellung aktueller Forschungsergebnisse und die Vertiefung der Gespräche zwischen Wissenschaftlern, Industrie und Anwendern. Die Beiträge dieses Bandes - einige davon in englischer Sprache - umfassen alle Bereiche der medizinischen Bildverarbeitung, insbesondere Bildgebung und -akquisition, Maschinelles Lernen, Bildsegmentierung und Bildanalyse, Visualisierung und Animation, Zeitreihenanalyse, Computerunterstützte Diagnose, Biomechanische Modellierung, Validierung und Qualitätssicherung, Bildverarbeitung in der Telemedizin u.v.m.

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Book SynopsisIm Buch „Computergrafik und Bildverarbeitung“ finden Sie alles, was Sie für Studium und Praxis über Generierung und Verarbeitung von digitalen Bildern wissen möchten und wie Sie es anwenden. Das erfolgreiche didaktische Konzept wurde weiterentwickelt und liegt ab dieser dritten Auflage in zwei Teilen vor. „Computergrafik und Bildverarbeitung“ Band I führt den Leser durch die Themen der Computergrafik. Dabei werden das alte und neue OpenGL parallel dargestellt, um einen guten Zugang für Einsteiger und einen leichteren Übergang für Fortgeschrittene zu gewährleisten. Profitieren Sie von dem kostenlosen Online-Service: Bildverarbeitungswerkzeuge, Beispiel-Software und interaktive Vorlesungen (als HTML-Seiten mit Java-Applets und Praktikumsaufgaben).Trade Review"'Computergrafik und Bildverarbeitung' ist bestens für Informatik-Studenten oder Praktiker geeignet. Beide Themen werden in einfacher Sprache, aber auch mit Mathematik erklärt."www.it-rezensionen.de, 20.08.2007"Das Werk ist eine anspruchsvolle, aber verständlich geschriebene und didaktisch gut gemachte Darstellung der beiden Bereiche [Computergrafik und Bildbearbeitung]."ekz-Informationsdienst, ID 18/07.Table of ContentsInteraktive 3D-Computergrafik – OpenGL – Geometrische Grundobjekte – Koordinatensysteme und Transformationen – Verdeckung – Farben - Beleuchtungsmodelle – Textur-Mapping – Schatten – Szenengraphen – Cull-Algorithmen – GPU Programmierung mit CUDA und OpenCL

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Book SynopsisHuman action analyses and recognition are challenging problems due to large variations in human motion and appearance, camera viewpoint and environment settings. The field of action and activity representation and recognition is relatively old, yet not well-understood by the students and research community. Some important but common motion recognition problems are even now unsolved properly by the computer vision community. However, in the last decade, a number of good approaches are proposed and evaluated subsequently by many researchers. Among those methods, some methods get significant attention from many researchers in the computer vision field due to their better robustness and performance. This book will cover gap of information and materials on comprehensive outlook – through various strategies from the scratch to the state-of-the-art on computer vision regarding action recognition approaches. This book will target the students and researchers who have knowledge on image processing at a basic level and would like to explore more on this area and do research. The step by step methodologies will encourage one to move forward for a comprehensive knowledge on computer vision for recognizing various human actions.Table of ContentsIntroduction.- Low-level Image Processing for Action Representations.- Action Representation Approaches.- MHI – A Global-based Generic Approach.- Shape Representation and Feature Vector Analysis .- Action Datasets.-Challenges Ahead.

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Book SynopsisThis book constitutes the refereed proceedings of the 14th National Conference on Man-Machine Speech Communication, NCMMSC 2017, held in Lianyungang, China, in October 2017. The 13 revised full papers presented were carefully reviewed and selected from 39 submissions. The papers address issues such as challenging issues in speech recognition and enhancement, speaker and language recognition, speech synthesis, corpus and phonetic in speech technology, speech generation, speech analyzing and modelling, speech processing of ethnic minorities, speech emotion recognition and audio signal processing.Table of ContentsChallenging issues in speech recognition and enhancement.- Speaker and language recognition, speech synthesis.- Corpus and phonetic in speech technology.- Speech generation, speech analyzing and modelling.- Speech processing of ethnic minorities.- Speech emotion recognition.- Audio signal processing.

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Book SynopsisThis book features high-quality research papers presented at the 3rd International Conference on Computational Intelligence in Pattern Recognition (CIPR 2021), held at the Institute of Engineering and Management, Kolkata, West Bengal, India, on 24 – 25 April 2021. It includes practical development experiences in various areas of data analysis and pattern recognition, focusing on soft computing technologies, clustering and classification algorithms, rough set and fuzzy set theory, evolutionary computations, neural science and neural network systems, image processing, combinatorial pattern matching, social network analysis, audio and video data analysis, data mining in dynamic environments, bioinformatics, hybrid computing, big data analytics and deep learning. It also provides innovative solutions to the challenges in these areas and discusses recent developments.Table of ContentsApplication of Big Data and Machine Learning for Astrological Predictions.- A Comparative Analysis of Machine Learning Approaches in Personality Prediction Using MBTI.- Performance Analysis of OFDM System on Multipath Fading and Inter Symbol Interference (ISI) Using AWGN.- Object Detection and Classification from a Real Time Video using SSD and YOLO Models.- A skin cancer image detection interface tool using VLF support vector machine classification.- Convolutional Neural Networks based Sentence Level Classification of Crime Documents.- An optimum fuzzy EOQ model for deteriorating items with shortages in a particular period.- Bangla Spoken numerals recognition by using HMM.

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Book SynopsisThis book constitutes the refereed proceedings of the 16th Conference on Image and Graphics Technologies and Applications, IGTA 2021, held in Beijing, China in June, 2021. The 21 papers presented were carefully reviewed and selected from 86 submissions. They provide a forum for sharing progresses in the areas of image processing technology; image analysis and understanding; computer vision and pattern recognition; big data mining, computer graphics and VR, as well as image technology applications. The volume contains the following thematic blocks: image processing and enhancement techniques (image information acquisition, image/video coding, image/video transmission, image/video storage, compression, completion, dehazing, reconstruction and display, etc.); biometric identification techniques (biometric identification and authentication techniques including face, fingerprint, iris and palm-print, etc.); machine vision and 3D reconstruction (visual information acquisition, camera calibration, stereo vision, 3D reconstruction, and applications of machine vision in industrial inspection, etc.); image/video big data analysis and understanding (object detection and recognition, image/video retrieval, image segmentation, matching, analysis and understanding); computer graphics (modeling, rendering, algorithm simplification and acceleration techniques, realistic scene generation, 3D reconstruction algorithm, system and application, etc.); virtual reality and human-computer interaction (virtual scene generation techniques, tracing and positioning techniques for large-scale space, augmented reality techniques, human-computer interaction techniques based on computer vision, etc.); applications of image and graphics (image/video processing and transmission, biomedical engineering applications, information security, digital watermarking, text processing and transmission, remote sensing, telemetering, etc.); other research works and surveys related to the applications of image and graphics technology.Table of ContentsImage processing and enhancement techniques.- Biometric identification techniques.- Machine vision and 3D reconstruction.- Image/Video big data analysis and understanding.- Computer graphics.- Virtual reality and human-computer interaction.- Applications of image and graphics.

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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 SynopsisChapter 1. Face Profile Biometric Systems: An Overview.- Chapter 2. Using Facial Attractiveness as a Soft Biometric Trait to Enhance Face Recognition Performance.- Chapter 3. An Examination and Comparison of Fairness of Face and Ocular Recognition Across Gender at NIR Spectrum.- Chapter 4. Assessment of Face Recognition Algorithms over Periocular and Binocular Areas of the Human Face to Check Authentication Accuracy in the Event of Occlusion.- Chapter 5. The Effectiveness of Quality Training on Facial Examination and Identification.- Chapter 6. AdvBiom: Adversarial Attacks on Biometric Matchers.- Chapter 7. Heterogeneous Face Recognition with Prepended Domain Transformers.- Chapter 8. Demographic Fairness and Accountability of Audio and Video based Unimodal and Bimodal Deepfake Detectors.- Chapter 9. On Assessing the Impact of Ocular Pathologies on the Performance of Deep Learning Ocular Based Recognition Systems in the Visible and NIR Bands.- Chapter 10. Distance-Based Classification of Biometric Images: Leveraging Deep Learning Models.- Chapter 11. BiFaceGAN: Bimodal Face Image Synthesis.- Chapter 12. On Designing a Head Pose Estimation Approach in the Thermal Band through GAN-based Image Synthesis.- Chapter 13. On Designing an Optimal Configuration and calibration of Computer Monitors for Facial Image Analysis in Forensic Examination.

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Book SynopsisThe four-volume set CCIS 1791, 1792, 1793 and 1794 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 213 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 Contents​Theory and Algorithms.- Knowledge Transfer from Situation Evaluation to Multi-agent Reinforcement Learning.- Sequential three-way rules class-overlap under-sampling based on fuzzy hierarchical subspace for imbalanced data.- Two-stage Multilayer Perceptron Hawkes Process.- The Context Hierarchical Contrastive Learning for Time Series in Frequency Domain.- Hawkes Process via Graph Contrastive Discriminant representation Learning and Transformer capturing long-term dependencies.- A Temporal Consistency Enhancement Algorithm Based On Pixel Flicker Correction.- Data representation and clustering with double low-rank constraints.- RoMA: a Method for Neural Network Robustness Measurement and Assessment.- Independent Relationship Detection for Real-Time Scene Graph Generation.- A multi-label feature selection method based on feature graph with ridge regression and eigenvector centrality.- O3GPT: A Guidance-Oriented Periodic Testing Framework with Online Learning, Online Testing, and Online Feedback.- AFFSRN: Attention-Based Feature Fusion Super-Resolution Network.- Temporal-Sequential Learning with Columnar-Structured Spiking Neural Networks.- Graph Attention Transformer Network for Robust Visual Tracking.- GCL-KGE:Graph Contrastive Learning for Knowledge Graph Embedding.- Towards a Unified Benchmark for Reinforcement Learning in Sparse Reward Environments.- Effect of Logistic Activation Function and Multiplicative Input Noise on DNN-kWTA model.- A High-Speed SSVEP-Based Speller Using Continuous Spelling Method.- AAT: Non-Local Networks for Sim-to-Real Adversarial Augmentation Transfer.- Aggregating Intra-class and Inter-class information for Multi-label Text Classification.- Fast estimation of multidimensional regression functions by the Parzen kernel-based method.- ReGAE: Graph autoencoder based on recursive neural networks.- Efficient Uncertainty Quantification for Under-constraint Prediction following Learning using MCMC.- SMART: A Robustness Evaluation Framework for Neural Networks.- Time-aware Quaternion Convolutional Network for Temporal Knowledge Graph Reasoning.- SumBART - An improved BART model for abstractive text summarization.- Saliency-Guided Learned Image Compression for Object Detection.- Multi-Label Learning with Data Self-Augmentation.- MnRec: A News Recommendation Fusion Model Combining Multi-granularity Information.- Infinite Label Selection Method for Mutil-label Classification.- Simultaneous Perturbation Method for Multi-Task Weight Optimization in One-Shot Meta-Learning.- Searching for Textual Adversarial Examples with Learned Strategy.- Multivariate Time Series Retrieval with Binary Coding from Transformer. -Learning TSP Combinatorial Search and Optimization with Heuristic Search.- A Joint Learning Model for Open Set Recognition with Post-processing.- Cross-Layer Fusion for Feature Distillation.- MCHPT: A Weakly Supervise Based Merchant Pre-trained Model.- Progressive Latent Replay for efficient Generative Rehearsal.- Generalization Bounds for Set-to-Set Matching with Negative Sampling.- ADA: An Attention-Based Data Augmentation Approach to Handle Imbalanced Textual Datasets.- Countering the Anti-detection Adversarial Attacks.- Evolving Temporal Knowledge Graphs by Iterative Spatio-Temporal Walks.- Improving Knowledge Graph Embedding Using Dynamic Aggregation of Neighbor Information.- Generative Generalized Zero-Shot Learning based on Auxiliary-Features.- Learning Stable Representations with Progressive Autoencoder (PAE).- Effect of Image Down-sampling on Detection of Adversarial Examples .- Boosting the Robustness of Neural Networks with M-PGD.- StatMix: Data augmentation method that relies on image statistics in federated learning.- Classification by Components Including Chow's Reject Option. -Community discovery algorithm based on improved deep sparse autoencoder.- Fairly Constricted Multi-Objective Particle Swarm Optimization.- Argument Classification with BERT plus Contextual, Structural and Syntactic Features as Text.- Variance Reduction for Deep Q-Learning using Stochastic Recursive Gradient.- Optimizing Knowledge Distillation Via Shallow Texture Knowledge Transfer.- Unsupervised Domain Adaptation Supplemented with Generated Images.- MAR2MIX: A Novel Model for Dynamic Problem in Multi-Agent Reinforcement Learning.- Adversarial Training with Knowledge Distillation Considering Intermediate Representations in CNNs.- Deep Contrastive Multi-view Subspace Clustering.

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