Image processing Books

Book SynopsisFetal Brain MRI: Reconstruction, Segmentation, and Development.- Automatic Quality Control in Multi-Centric Fetal Brain MRI Super-Resolution Reconstruction.- D SVG: Diffusion-Based Slice-to-Volume Generation with Implicit Neural Representation for Fetal Brain MRI.- Continuous Spatio Temporal Representation with Implicit Neural Networks for Fetal Brain MRI Atlas Construction.- Conditional Fetal Brain Atlas Learning for Automatic Tissue Segmentation.- Enhancing Corpus Callosum Segmentation in Fetal MRI via Pathology Informed Domain Randomization.- Physics Informed Joint Multi TE Super Resolution with Implicit Neural Representation for Robust Fetal T2 Mapping.- Robustness and Diagnostic Performance of Super Resolution Fetal Brain MRI.- Enhancing Fetal Brain MRI Segmentation in Ventriculomegaly Using Generative AI Augmented Pathological Data.- FetGEs a Deep Learning Approach for Fetal MRI Ganglionic Eminence Segmentation.- Quantifying Fetal Periventricular White Matter Development Using Multimodal MRI.- LLM and Functional Imaging in Fetal and Placental MRI.- FetalExtract LLM: Structured Information Extraction from Free Text Fetal MRI Reports Based on Privacy Ensuring Open weights Large Language Models.- Automated Biometry for Assessing Cephalopelvic Disproportion in 3D 0.55T Fetal MRI at Term.- Contrast Invariant Self Supervised Segmentation for Quantitative Placental MRI.- PUUMA Functional MRI Prediction of Gestational Age at Birth and Preterm Risk.- Robust Alignment of the Human Embryo in 3D Ultrasound Using PCA and Classifier Ensembles.- Neonatal and Pediatric Imaging.- NEUBORN: The Neurodevelopmental Evolution Framework Using Biomechanical Remodeling.- Anatomically Informed Dynamic Weighting for Semi Supervised Fetal MRI Segmentation.- Towards a Comprehensive Morphological, Dynamic, and Functional MRI Investigation of the Pediatric Bowel at 0.55T.

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Book SynopsisThis book focuses on the latest developments in the fields of visual AI, image processing and computer vision. It shows research in basic techniques like image pre-processing, feature extraction, and enhancement, along with applications in biometrics, healthcare, neuroscience and forensics. The book highlights algorithms, processes, novel architectures and results underlying machine intelligence with detailed execution flow of models.

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Book SynopsisDer „Klassiker" der Bildverarbeitung liefert eine fundierte und anwendungsgerechte Einführung in die wichtigsten Methoden und in ausgewählte Verfahren. Seine besondere Stärke: große Detailgenauigkeit, präzise algorithmische Beschreibung sowie die unmittelbare Verbindung zwischen mathematischer Beschreibung und konkreter Implementierung. Übungsaufgaben und Code-Beispiele runden die Darstellungen ab. Source Code und ergänzende Materialien finden sich auf der Internetseite www.imagingbook.com. Die Neuauflage wurde überarbeitet und erweitert.Table of ContentsCrunching Pixels.- Digitale Bilder.- ImageJ.- Histogramme.- Punktoperationen.- Filter.- Kanten und Konturen.- Auffinden von Eckpunkten.- Detektion einfacher Kurven.- Morphologische Filter.- Regionen in Binärbildern.- Farbbilder.- Einführung in Spektraltechniken.- Die diskrete Fouriertransformation in 2D.- Die diskrete Kosinustransformation (DCT).- Geometrische Bildoperationen.- Bildvergleich.- Automatische Schwellwertoperationen.- Filter für Farbbilder.- Lokale, invariante Bildmerkmale.- Erzeugung von Bildrauschen..- Quantitative Qualitätsmaße für Bilder.- Anhang A: Mathematische Grundlagen.- Anhang B: Java-Notizen.- Literaturverzeichnis.- Sachverzeichnis.

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Book SynopsisMarkus Kächele offers a detailed view on the different steps in the affective computing pipeline, ranging from corpus design and recording over annotation and feature extraction to post-processing, classification of individual modalities and fusion in the context of ensemble classifiers. He focuses on multimodal recognition of discrete and continuous emotional and medical states. As such, specifically the peculiarities that arise during annotation and processing of continuous signals are highlighted. Furthermore, methods are presented that allow personalization of datasets and adaptation of classifiers to new situations and persons. Table of ContentsClassification and Regression Approaches.- Applications and Affective Corpora.- Modalities and Feature Extraction.- Machine Learning for the Estimation of Affective Dimensions.- Adaptation and Personalization of Classifiers.- Experimental Validation.

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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 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 is a collection of the papers accepted by the ICIVIS 2021—The International Conference on Image, Vision and Intelligent Systems held on June 15–17, 2021, in Changsha, China. The topics focus but are not limited to image, vision and intelligent systems. Each part can be used as an excellent reference by industry practitioners, university faculties, research fellows and undergraduates as well as graduate students who need to build a knowledge base of the most current advances and state-of-practice in the topics covered by this conference proceedings.Table of ContentsImage and Vision.- Intelligent Systems.- Computation and Application.

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Book SynopsisThis book focuses on end-to-end robotic applications using vision and control algorithms, exposing its readers to design innovative solutions towards sensors-guided robotic bin-picking and assembly in an unstructured environment. The use of sensor fusion is demonstrated through a bin-picking task of texture-less cylindrical objects. The system identification techniques are also discussed for obtaining precise kinematic and dynamic parameters of an industrial robot which facilitates the control schemes to perform pick-and-place tasks autonomously without any interference from the user. The uniqueness of this book lies in a judicious balance between theory and technology within the context of industrial application. Therefore, it will be valuable to researchers working in the area of vision- and force control- based robotics, as well as beginners in this inter-disciplinary area, as it deals with the basics and technologically advanced research strategies.Table of ContentsIntroduction.- Vision System and Calibration.- Uncertainty and Sensitivity Analysis.- Identification.- Force Control and Assembly.- Integrated Assembly and Performance Evaluation.- Conclusion.- Vision and Uncertainty Analysis.- Robot Jacobian.- Code Snippets and Experimental Videos.

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Book SynopsisThis book consists of a collection of the high-quality research articles in the field of computer vision and robotics which are presented in the International Conference on Computer Vision and Robotics (CVR 2021), organized by BBD University Lucknow, India, during 7–8 August 2021. The book discusses applications of computer vision and robotics in the fields like medical science, defence, and smart city planning. The book presents recent works from researchers, academicians, industry, and policy makers.Table of ContentsAI Techniques for Swedish Leaf Classification.- Designing a Recommender System for Articles using Implicit Feedback.- Personality Prediction based on Twitter Tweets.- Music Therapy for Mood Transformation based on Deep Learning Framework.- Rice Crop Diseases and Pest Detection using Edge Detec-tion Techniques and Convolution Neural Network.- Spatial Analyses of Cyclone Amphan Induced Flood Inundation Mapping Using Sentinel-1A SAR Images Through GEE Cloud.- Classification through Data Mining Algorithm.- Early Detection of Diabetic Retinopathy using Multimodal Approach.- Intelligent Techniques for Crowd Detection And People Counting- A Systematic_Study.- Twitter Sentiment Analysis of Public Opinion on COVID-19 Vaccines.

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Book SynopsisThe book explains the important concepts and principles of image processing to implement the algorithms and techniques to discover new problems and applications. It contains numerous fundamental and advanced image processing algorithms and pattern recognition techniques to illustrate the framework. It presents essential background theory, shape methods, texture about new methods, and techniques for image processing and pattern recognition. It maintains a good balance between a mathematical background and practical implementation. This book also contains the comparison table and images that are used to show the results of enhanced techniques. This book consists of novel concepts and hybrid methods for providing effective solutions for society. It also includes a detailed explanation of algorithms in various programming languages like MATLAB, Python, etc. The security features of image processing like image watermarking and image encryption etc. are also discussed in this book. This book will be useful for those who are working in the field of image processing, pattern recognition, and security for digital images. This book targets researchers, academicians, industry, and professionals from R&D organizations, and students, healthcare professionals working in the field of medical imaging, telemedicine, cybersecurity, data scientist, artificial intelligence, image processing, digital hospital, intelligent medicine.Table of ContentsChapter 1: Introduction of Image processingChapter 2: Image Enhancement Chapter 3: Image segmentation and image colorization Chapter 4: Image classification, fusion, and reconstruction Chapter 5: Feature Extraction Chapter 6: Pattern recognition and computer vision applications in Agriculture, GIS, Surveillance and Robotics. Chapter 7: Statistical Pattern Recognition and Supervised Learning Chapter 8: Artificial Intelligence, Machine and Deep Learning for pattern recognition Chapter 9: Image compression and Cloud Imaging Chapter 10: Neural network and data science in image processing Chapter 11: Imaging acquisition and Biometrics Chapter 12: Application of medical Imaging in telemedicine. Chapter 13: Digital Image security, privacy, and future challenges Chapter 14: AI methodologies for medical images analysis

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Book SynopsisThe volume of data being collected in solar astronomy has exponentially increased over the past decade and we will be entering the age of petabyte solar data. Deep learning has been an invaluable tool exploited to efficiently extract key information from the massive solar observation data, to solve the tasks of data archiving/classification, object detection and recognition. Astronomical study starts with imaging from recorded raw data, followed by image processing, such as image reconstruction, inpainting and generation, to enhance imaging quality. We study deep learning for solar image processing. First, image deconvolution is investigated for synthesis aperture imaging. Second, image inpainting is explored to repair over-saturated solar image due to light intensity beyond threshold of optical lens. Third, image translation among UV/EUV observation of the chromosphere/corona, Ha observation of the chromosphere and magnetogram of the photosphere is realized by using GAN, exhibiting powerful image domain transfer ability among multiple wavebands and different observation devices. It can compensate the lack of observation time or waveband. In addition, time series model, e.g., LSTM, is exploited to forecast solar burst and solar activity indices. This book presents a comprehensive overview of the deep learning applications in solar astronomy. It is suitable for the students and young researchers who are major in astronomy and computer science, especially interdisciplinary research of them.Trade Review“Each application is described with sufficient detail to give the reader an understanding of how AI is used and how its use compares with older tools used for the same purposes. The writing is clear … and an excessive use of acronyms. Relevant images and tables enhance the reader’s understanding; many references accompany each chapter. This book should appeal to those interested in either AI or the field of solar astronomy.” (G. R. Mayforth, Computing Reviews, November 22, 2023)Table of ContentsChapter 1: Introduction Chapter 2: Classical deep learning models Chapter 3: Deep learning in solar image classification tasks Chapter 4: Deep learning in solar object detection tasks · Active Region (AR) detection · EUV waves detection Chapter 5: Deep learning in solar image generation tasks · Deconvolution of aperture synthesis · Recovering over-exposed solar image · Generating magnetogram from EUV image · Generating magnetogram from H-alpha Chapter 6: Deep learning in solar forecasting tasks · Flare forecast · F10.7c forecast

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Book SynopsisThis book constitutes the refereed proceedings of the Third International Conference on Machine Learning, Image Processing, Network Security and Data Sciences, MIND 2021. The papers are organized according to the following topical sections: data science and big data; image processing and computer vision; machine learning and computational intelligence; network and cybersecurity. This book aims to develop an understanding of image processing, networks, and data modeling by using various machine learning algorithms for a wide range of real-world applications. In addition to providing basic principles of data processing, this book teaches standard models and algorithms for data and image analysis. Table of ContentsA Methodological review of Time Series Forecasting with Deep Learning Model : A Case study on Electricity Load and Price Prediction.- A Robust Secure Access Entrance Method Based on Multi Model Biometric Credentials Iris and Finger Print.- Prostate Cancer Grading using Multistage DeepNeural Networks.

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Book SynopsisThis book is a collection of the high-quality research articles in the field of computer vision and robotics which are presented in International Conference on Computer Vision and Robotics (ICCVR 2022), organized by BBD University Lucknow India, during 21 – 22 May 2022. The book discusses applications of computer vision and robotics in the fields like medical science, defence and smart city planning. This book presents recent works from researchers, academicians, industry, and policy makers.Table of ContentsStory Telling: Learning to Visualize Sentences Through Generated Scenes.- A Novel Processing of Scalable Web Log Data using Map Reduce Framework.- A Review of Disease Detection of Pre and Post-Harvest Plant Diseases: Recent Developments and Future.- Comparative Study Based on Lung Cancer with Covid-19 Using Deep Learning and Machine Learning.- Low-cost Data Acquisition System for Electric Vehicles.- Machine Learning based Robotic-Assisted Upper Limb Rehabilitation Therapies: A Review.- Performance Analysis of Classic LEACH vs CC-LEACH.- Anomaly Detection in the Course Evaluation Process.- Self Attention Based Efficient U-Net for Crack Segmentation.- Lung Carcinoma Detection from CT Images Using Image Segmentation.- A Deep Learning Based Human Activity Recognition System for Monitoring the Elderly People.- Tweets Classification on the Base of Sentiments using Deep Learning.

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Book SynopsisThis book constitutes the refereed proceedings of the Third International Workshop on Human Brain and Artificial Intelligence, HBAI 2022, held in conjunction with IJCAI-ECAI 2022, Vienna, Austria, on July 23, 2022. The 19 full papers presented were carefully reviewed and selected from 21 submissions. The papers present most recent research in the fields of brain-inspired computing, brain-machine interfaces, computational neuroscience, brain-related health, neuroimaging, cognition and behavior, learning, and memory, neuron modulation, and closed-loop brain stimulation.Table of ContentsAI for brain related data analysis.- Classification of EEG signals based on GA-ELM Optimization algorithm.- Delving into Temporal-Spectral Connections in Spike-LFP Decoding by Transformer Networks.- A Mask Image Recognition Attention Network Supervised by Eye Movement.- DFC-SNN: A new approach for the recognition of brain states by fusing brain dynamics and spiking neural network.- DSNet: EEG-Based Spatial Convolutional Neural Network for Detecting Major Depressive Disorder.- SE-1DCNN-LSTM: A Deep Learning Framework for EEG-based Automatic Diagnosis of Major Depressive Disorder and Bipolar Disorder.- Emotion Recognition from EEG Using All-Convolution Residual Neural Network.- Salient Object Detection With Fusion of RGB Image and Eye Tracking Data.- Multi-Source Domain Adaptation Based on Data Selector with Soft Actor-Critic.- Transfer learning to decode brain states reflecting the relationship between cognitive tasks.- AI and brain interface.- Brain network analysis of hand motor execution and imagery based on conditional Granger causality.- A Hybrid Brain-Computer Interface for Smart Car Control.- A spiking neural network for brain-computer interface of four classes motor imagery.- Virtual Drone Control Using Brain-Computer Interface based on Motor Imagery Brain Magnetic Fields.- Brain controlled manipulator system based on improved target detection and Augmented Reality technology.- Optimization of stimulus color for SSVEP-based brain-computer interfaces in mixed reality.- Brain related research.- White matter maturation and hemispheric asymmetry during childhood based on Chinese population.- A Digital Gaming Intervention Combing Multitasking and Alternating Attention for ADHD: a preliminary study.- A BCI speller with 120 commands encoded by hybrid P300 and SSVEP features.

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Book SynopsisThis book constitutes the refereed proceedings of the 16th China Conference on Wireless Sensor Networks, CWSN 2022, which took place in Guangzhou, China, in November 2022. The 17 full papers presented in this volume were carefully reviewed and selected from 204 submissions, including 87 English papers and 117 Chinese papers. The conference provided an academic exchange of research and a development forum for IoT researchers, developers, enterprises, and users. Exchanging results and experience of research and applications in IoT, and discussing the key challenges and research hotspots, is the main goal of the forum. As a high-level forum for the design, implementation, and application of IoT, the conference promoted the exchange and application of the oriesand technologies of IoT-related topics.Table of ContentsMmLiquid: Liquid Identification using mmWave.- PASD: A Prioritized Action Sampling-Based Dueling DQN for Cloud-Edge Collaborative Computation Offloading in Industrial IoT.- Automatic construction of large-scale IoT datasets with multi-strategy fusion.- Research on Argo data anomaly detection based on improved DBSCAN algorithm.- Fog Federation Pricing and Resource Purchase based on the Stackelberg Model in Fog Computing.- Bandwidth Scheduling Scheme with AoI Guarantee for Heterogeneous Mobile Edge Caching.- An Explainable Machine Learning Framework for Lower Limb Exoskeleton Robot System.- A Community Detection Algorithm Fusing Node Similarity and Label Propagation .- A Community Detection Algorithm Fusing Node Similarity and Label Propagation.- A Graph Neural Network Based Model for IoT Binary Components Similarity Detection .- Multi-scale Temporal Feature Fusion for Time-limited Order Prediction .- Dynamics Modeling of Knowledge Dissemination Process in Online Social Networks.- The Impact of Time Delay and User’s Behavior on the Dissemination Process of Rumor in Mobile Social Networks.- Minimizing the Embedding Cost of Service Function Chains with Adjustable Order .- MDLpark: Available parking prediction for smart parking through mobile deep learning .- Image Attribute Modification Based on Text Guidance.- Industrial IoT Network Security Situation Prediction Based on Improved SSA-BiLSTM.

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Book SynopsisThis book presents and analyzes methods to perform image co-segmentation. In this book, the authors describe efficient solutions to this problem ensuring robustness and accuracy, and provide theoretical analysis for the same. Six different methods for image co-segmentation are presented. These methods use concepts from statistical mode detection, subgraph matching, latent class graph, region growing, graph CNN, conditional encoder–decoder network, meta-learning, conditional variational encoder–decoder, and attention mechanisms. The authors have included several block diagrams and illustrative examples for the ease of readers. This book is a highly useful resource to researchers and academicians not only in the specific area of image co-segmentation but also in related areas of image processing, graph neural networks, statistical learning, and few-shot learning.Table of ContentsIntroduction.- Survey of Image Co-segmentation.- Mathematical Background.- Co-segmentation using a Classification Framework.- Use of Maximum Common Subgraph Matching.- Maximally Occurring Common Subgraph Matching.- Co-segmentation using Graph Convolutional Neural Network.- Use of a Conditional Siamese Convolutional Network.- Few-shot Learning for Co-segmentation.- Conclusions.

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Book SynopsisThis book provides a comprehensive introduction to quantum image processing, which focuses on extending conventional image processing tasks to the quantum computing frameworks. It summarizes the available quantum image representations and their operations, reviews the possible quantum image applications and their implementation, and discusses the open questions and future development trends. It offers a valuable reference resource for graduate students and researchers interested in this emerging interdisciplinary field.Table of Contents1.Introduction and Overview.- 2. Quantum Image Representations.- 3. Quantum Image Operations.- 4. Quantum Image Security.- 5. Quantum Image Understanding.- 6. Quantum Multimedia Techniques.- 7. Summary and Discussion.

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Book SynopsisThe information presented in this book will startle the world. For centuries, the authenticity of the Holy Shroud has been argued about. Skeptics push their negative opinion based on a few highly questionable clues, while the authenticists continue to detect new facts confirming that the Relic wrapped the corpse of Jesus Christ and that the body image impressed on it was produced by a source of energy generated during the Resurrection.What is world-changing is that to explain this "impossible image" of a tortured and crucified man, the book presents a startling new hypothesis, the "Divine Photograph" taken at the instant of the Resurrection, based on a phenomenon, the "Miracle of the Holy Fire" that manifests on every Holy Saturday at the Holy Sepulcher in Jerusalem. As this amazing relationship becomes more broadly known, the world will be shocked.Table of Contents1. The Miracle of the Holy Fire 2. Miracle of the Holy Fire, History and Religious Significance 3. Science Investigates the Holy Fire 4. The Holy Shroud and the Impossible Image 5. The Divine Photography 6. Conclusion

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Book SynopsisChapter 1. Introduction to Multimodal Generative AI.- Chapter 2. ChatGPT and BERT: Comparative Analysis of Various Natural Language Processing Applications.- Chapter 3. Large Language Model on Multi-Modal Data.- Chapter 4.  Adaptive Learning Technologies: Navigating the Road from Hype to Reality.- Chapter 5. Generative Artificial Intelligence in Visual Content: A Review of the Influence on Consumer Perception and Perspective.- Chapter 6. Text-to-Image Synthesis: Techniques and Applications.- Chapter 7. Image-to-Text Generation: Bridging Visual and Linguistic Worlds.- Chapter 8. Sustainability in the Metaverse: Challenges, Implications, and Potential Solutions.- Chapter 9. Transcendent Artificial Intelligence in Education.- Chapter 10. Chat GPT in Academia and Research - A Comprehensive Review of Integrating AI in Higher Education.- Chapter 11. Exploring Multimodal Hate Speech Detection Using Machine Learning and Deep Learning Models.- Chapter 12. Multimodal Generative AI for People with Disabilities.- Chapter 13.                Single-Modality to Multimodality: The Evolutionary Trajectory of Artificial Intelligence in Integrating Diverse Data Streams for Enhanced Cognitive Capabilities.- Chapter 14. Interfacing Multimodal AI with IoT: Unlocking New Frontiers.-Chapter 15. Enhancing Safety and Reliability in VANETs for Autonomous Vehicles by M-XAI (Multi Model- Explainable-AI) .- Chapter 16. Future Directions In Multimodal Genrative AI.

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Book SynopsisMulti-spectral sensors in multi-field applications.- Active visual camera system.- Universal camera jammer system.- Biomedical optical sensors.- Healthcare Perfect realization of intelligent optical sensors.

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Book SynopsisThis 15-volume set LNCS 15031-15045 constitutes the refereed proceedings of the 7th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2024, held in Urumqi, China, during October 18?20, 2024.The 579 full papers presented were carefully reviewed and selected from 1526 submissions. The papers cover various topics in the broad areas of pattern recognition and computer vision, including machine learning, pattern classification and cluster analysis, neural network and deep learning, low-level vision and image processing, object detection and recognition, 3D vision and reconstruction, action recognition, video analysis and understanding, document analysis and recognition, biometrics, medical image analysis, and various applications.

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Book SynopsisContextual Feature-Based Medical Visual Question Answering Aided by Learnable Matrix.- ImgQuant: Towards adversarial defense with robust boundary via dual-image quantization.- Swelling-ViT: Rethink Data-efficient Vision Transformer from Locality.- Target-Specific Domain Adaptation via Geometry-Correlation Prediction for Point Cloud.- Dual-stream Network of Vision Mamba and CNN with Auto-scaling for Remote Sensing Image Segmentation.- PRM: A Pixel-Region-Matching Approach for Fast Video Object Segmentation.- A Novel Combined GAN for Defects Generation using Masking Mechanisms.- Semi-supervised lightweight fabric defect detection.- Semi-adaptive Synergetic Two-way Pseudoinverse Learning System.- Invariant Risk Minimization Augmentation for Graph Contrastive Learning.- Enhancing Fast Adversarial Training with Learnable Adversarial Perturbations.- DTAFORMER: Directional Time Attention Transformer For Long-Term Series Forecasting.- Unpaired Multi-scenario Sketch Synthesis via Texture Enh

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Book SynopsisThis 15-volume set LNCS 15031-15045 constitutes the refereed proceedings of the 7th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2024, held in Urumqi, China, during October 18?20, 2024.The 579 full papers presented were carefully reviewed and selected from 1526 submissions. The papers cover various topics in the broad areas of pattern recognition and computer vision, including machine learning, pattern classification and cluster analysis, neural network and deep learning, low-level vision and image processing, object detection and recognition, 3D vision and reconstruction, action recognition, video analysis and understanding, document analysis and recognition, biometrics, medical image analysis, and various applications.

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Book SynopsisThis 15-volume set LNCS 15031-15045 constitutes the refereed proceedings of the 7th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2024, held in Urumqi, China, during October 18?20, 2024.The 579 full papers presented were carefully reviewed and selected from 1526 submissions. The papers cover various topics in the broad areas of pattern recognition and computer vision, including machine learning, pattern classification and cluster analysis, neural network and deep learning, low-level vision and image processing, object detection and recognition, 3D vision and reconstruction, action recognition, video analysis and understanding, document analysis and recognition, biometrics, medical image analysis, and various applications.

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Book SynopsisThis 15-volume set LNCS 15031-15045 constitutes the refereed proceedings of the 7th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2024, held in Urumqi, China, during October 18?20, 2024.The 579 full papers presented were carefully reviewed and selected from 1526 submissions. The papers cover various topics in the broad areas of pattern recognition and computer vision, including machine learning, pattern classification and cluster analysis, neural network and deep learning, low-level vision and image processing, object detection and recognition, 3D vision and reconstruction, action recognition, video analysis and understanding, document analysis and recognition, biometrics, medical image analysis, and various applications.

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Book SynopsisThis book comprises the proceedings of the International Conference on Machine Vision and Augmented Intelligence (MAI 2022). The conference proceedings encapsulate the best deliberations held during the conference. The diversity of participants in the event from academia, industry, and research reflects in the articles appearing in the book. The book encompasses all industrial and non-industrial applications. This book covers a wide range of topics such as modeling of disease transformation, epidemic forecast, image processing, and computer vision, augmented intelligence, soft computing, deep learning, image reconstruction, artificial intelligence in health care, brain-computer interface, cybersecurity, social network analysis, and natural language processing.​Table of ContentsModelling of Disease Transformation.- Epidemic Forecast.- COVID-19: Theory and practice.- Image Processing and Computer Vision.- Augmented Intelligence: Theory and Applications.- Soft Computing: Theory and Applications.- Deep Learning: Theory and Applications.

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Book SynopsisThe 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis. Table of ContentsLearning Bottleneck Transformer for Event Image-Voxel Feature Fusion based Classification.- Multi-scale Dilated Attention Graph Convolutional Network for Skeleton-Based Action Recognition.- Auto-Learning-GCN: An Ingenious Framework for Skeleton-based Action Recognition.- Skeleton-based Action Recognition with Combined Part-wise Topology Graph Convolutional Networks.- Segmenting Key Clues to Induce Human-Object Interaction Detection.- Lightweight Multispectral Skeleton and Multi-Stream Graph Attention Networks for Enhanced Action Prediction with Multiple Modalities.- Spatio-temporal Self-supervision for Few-shot Action Recognition.- A Fuzzy Error based Fine-tune Method for Spatio-temporal Recognition Model.- Temporal-Channel Topology Enhanced Network for Skeleton-Based Action Recognition.- HFGCN-Based Action Recognition System for Figure Skating.- Image Priors Assisted Pre-training for Point Cloud Shape Analysis.-AMM-GAN: Attribute-Matching Memory for Person Text-to-Image Generation.- RecFormer: Recurrent Multi-modal Transformer with History-aware Contrastive Learning for Visual Dialog.- KV Inversion: KV Embeddings Learning for Text-Conditioned Real Image Action Editing.- Enhancing Text-Image Person Retrieval through Nuances Varied Sample.- Unsupervised Prototype Adapter for Vision-Language Models.- Multimodal Causal Relations Enhanced CLIP for Image-to-Text Retrieval.- Exploring Cross-Modal Inconsistency in Entities and Emotions for Multimodal Fake News Detection.- Deep Consistency Preserving Network for Unsupervised Cross-modal Hashing.- Learning Adapters for Text-guided Portrait Stylization with Pretrained Diffusion Models.- EdgeFusion: Infrared and Visible Image Fusion Algorithm in Low Light.- An Efficient Momentum Framework for Face-Voice Association Learning.- Multi-modal Instance Refinement for Cross-domain Action Recognition.- Modality Interference Decoupling and Representation Alignment for Caricature-Visual Face Recognition.- Plugging Stylized Controls in Open-Stylized Image Captioning.- MGT: Modality-Guided Transformer for Infrared and Visible Image Fusion.- Multimodal Rumor Detection by Using Additive Angular Margin with Class-aware Attention for Hard Samples.- An Effective Dynamic Reweighting Method for Unbiased Scene Graph Generation.- Multi-modal Graph and Sequence Fusion Learning for Recommendation.- Co-attention guided local-global feature fusion for aspect-level multimodal sentiment analysis.- Discovering Multimodal Hierarchical Structures with Graph Neural Networks for Multi-modal and Multi-hop Question Answering.- Enhancing Recommender System with Multi-modal Knowledge Graph.- Location Attention Knowledge Embedding Model for Image-Text Matching.- Pedestrian Attribute Recognition Based on Multimodal Transformer.- RGB-D Road Segmentation Based on Geometric Prior Information.- Contrastive Perturbation Network for Weakly Supervised Temporal Sentence Grounding.- MLDF-Net: Metadata Based Multi-level Dynamic Fusion Network.- Efficient Adversarial Training with Membership Inference Resistance.- Enhancing Image Comprehension for Computer Science Visual Question Answering.- Cross-Modal Attentive Recalibration and Dynamic Fusion for Multispectral Pedestrian Detection.

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Book SynopsisThe 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis. Table of ContentsDeep Stereo Matching with Superpixel-based Feature and Cost Aggregation.- GMA3D: Local-Global Attention Learning to Estimate Occluded Motions of Scene Flow.- Diffusion-based 3D Object Detection with Random Boxes.- Blendshape-based Migratable Speech-driven 3D Facial Animation with Overlapping Chunking-Transformer.- FIRE: Fine Implicit Reconstruction Enhancement with Detailed Body Part Labels and Geometric Features.- Sem-Avatar: Semantic Controlled Neural Field for High-Fidelity Audio Driven Avatar.- Depth optimization for accurate 3D Reconstruction from light field images.- TriAxial Low-Rank Transformer for Efficient Medical Image Segmentation.- SACFormer: Unify Depth Estimation and Completion with Prompt.- Rotation-Invariant Completion Network.- Towards Balanced RGB-TSDF Fusion for Consistent Semantic Scene Completion by 3D RGB Feature Completion and a Classwise Entropy Loss Function.- FPTNet: Full Point Transformer Network for Point Cloud Completion.- Efficient Point-based Single Scale 3D Obiect Detection from Traffic Scenes.- Matching-to-Detecting: Establishing Dense and Reliable Correspondences between Images.- Solving Generalized Pose Problem of Central and Non-central Cameras.- RICH: Robust Implicit Clothed Humans Reconstruction from Multi-Scale Spatial Cues.- An Efficient and Consistent Solution to the PnP Problem.- Autoencoder and Masked Image Encoding-based Attentional Pose Network.- A Voxel-Based Multiview Point Cloud Refinement Method via Factor Graph Optimization.- SwinFusion: channel query-response based feature fusion for monocular depth estimation.- PCRT: Multi-branch Point Cloud Reconstruction from a Single Image with Transformers.- Progressive Point Cloud Generating by Shape Decomposing and Upsampling.- Three-dimensional Plant Reconstruction with Enhanced Cascade-MVSNet.- Learning Key Features Transformer Network for Point Cloud Processing.- Unsupervised Domain Adaptation for 3D Object Detection via Self-Training.- Generalizable Neural Radiance Field with Hierarchical Geometry Constraint.- ACFNeRF: Accelerating and Cache-Free Neural Rendering via Point Cloud-based Distance Fields.- OctPCGC-Net: Learning Octree-Structured Context Entropy Model for Point Cloud Geometry Compression.- Multi-modal Feature Guided Detailed 3D Face Reconstruction from a Single Image.- Advanced License Plate Detector in Low-Quality Images with Smooth Regression Constraint.- A Feature Refinement Patch Embedding-Based Recognition Method for Printed Tibetan Cursive Script.- End-to-End Optical Music Recognition with Attention Mechanism and Memory Units Optimization.- Tripartite Architecture License Plate Recognition based on Transformer.- Focus the Overlapping Problem on Few-Shot Object Detection via Multiple Predictions.- Target-aware Bi-Transformer for Few-Shot Segmentation.- Convex Hull Collaborative Representation Learning on Grassmann Manifold with L_1 norm Regularization.- FUFusion: Fuzzy Sets Theory for Infrared and Visible Image Fusion.-Progressive Frequency-aware Network for Laparoscopic Image Desmoking.-A pixel-level segmentation method for water surface reflection detection

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Book SynopsisThe 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis. Table of ContentsLoss Filtering Factor for Crowd Counting.- Classifier Decoupled Training for Black-Box Unsupervised Domain Adaptation.- Unsupervised Concept Drift Detection via Imbalanced Cluster Discriminator Learning.- Unsupervised Domain Adaptation for Optical Flow Estimation.- Continuous Exploration via Multiple Perspectives in Sparse Reward Environment.- Network Transplanting for the Functionally Modular Architecture.- TiAM-GAN: Titanium Alloy Microstructure Image Generation Network.- A Robust Detection and Correction Framework for GNN-based Vertical Federated Learning.- QEA-Net: Quantum-Effects-based Attention Networks.- Learning Scene Graph for Better Cross-Domain Image Captioning.- Enhancing Rule Learning on Knowledge Graphs through Joint Ontology and Instance Guidance.- Explore Across-Dimensional Feature Correlations for Few-Shot Learning.- Pairwise-emotion Data Distribution Smoothing for Emotion Recognition.- SIEFusion: Infrared and Visible Image Fusion via Semantic Information Enhancement.- DeepChrom: A Diffusion-Based Framework for Long-Tailed Chromatin State Prediction.- Adaptable Conservative Q-Learning for Offline Reinforcement Learning.- Boosting Out-of-Distribution Detection with Sample Weighting.- Causal discovery via the subsample based reward and punishment mechanism.- Local Neighbor Propagation Embedding.- Inter-class sparsity based non-negative transition sub-space learning.- Incremental Learning Based on Dual-branch Network.- Inter-Image Discrepancy Knowledge Distillation for Semantic Segmentation.- Cascaded Bilinear Mapping Collaborative Hybrid Attention Modality Fusion Model.- CasFormer: Cascaded Transformer based on Dynamic Voxel Pyramid for 3D Object Detection from Point Clouds.- Generalizable and Accurate 6D Object Pose Estimation Network.- An Internal-external Constrained Distillation Framework for Continual Semantic Segmentation.- MTD: Multi-Timestep Detector for Delayed Streaming Perception.- Semi-Direct SLAM with Manhattan for Indoor Low-texture Environment.- L2T-BEV: Local Lane Topology Prediction from Onboard Surround-View Cameras in Bird’s Eye View Perspective.- CCLane: Concise Curve Anchor-based Lane Detection Model with MLP-Mixer.- Uncertainty-aware Boundary Attention Network for Real-time Semantic Segmentation.- URFormer: Unified Representation LiDAR-Camera 3D Object Detection with Transformer.- A Single-Stage 3D Object Detection Method Based on Sparse Attention Mechanism.- WaRoNav: Warehouse Robot Navigation Based on Multi-View Visual-Inertial Fusion.- Enhancing Lidar and Radar Fusion for Vehicle Detection in Adverse Weather via Cross-Modality Semantic Consistency.- Enhancing Active Visual Tracking under Distractor Environments.- Cross-modal and Cross-domain Knowledge Transfer for Label-free 3D Segmentation.- Cross-task Physical Adversarial Attack against Lane Detection System Based on LED Illumination Modulation.- RECO: Rotation Equivariant COnvolutional Neural Network for Human Trajectory Forecasting.- FGFusion: Fine-Grained Lidar-Camera Fusion for 3D Object Detection

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Book SynopsisThe 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and AnalysisTable of ContentsShared Nearest Neighbor Calibration For Few-Shot Classification.- Prototype Rectification with Region-Wise Foreground Enhancement for Few-Shot Classification.- Rotation Augmented Distillation for Exemplar-Free Class Incremental Learning with Detailed Analysis.- Nonconvex Tensor Hypergraph Learning for Multi-view Subspace Clustering.- A Novel Method for Identifying Bipolar Disorder based on Diagnostic Texts.- Deep Depression Detection based on Feature Fusion and Result Fusion.- Adaptive Cluster Assignment for Unsupervised Semantic Segmentation.- Confidence-Guided Open-World Semi-Supervised Learning.- SSCL: Semi-supervised Contrastive Learning for Industrial Anomaly Detection.- One Step Large-scale Multi-view Subspace Clustering based on Orthogonal Matrix Factorization with Consensus Graph Learning.- Deep Multi-Task Image Clustering with Attention-guided Patch Filtering and Correlation Mining.- Deep Structure and Attention Aware Subspace Clustering.- Broaden Your Positives: A General Rectification Approach for Novel Class Discovery.- CE2: A Copula Entropic Mutual Information Estimator for Enhancing Adversarial Robustness.- Two-step projection of sparse discrimination between classes for unsupervised domain adaptation.- Enhancing Adversarial Robustness via Stochastic Robust Framework.- Pseudo Labels Refinement with Stable Cluster Reconstruction for Unsupervised Re Identification.- Ranking Variance Reduced Ensemble Attack with Dual Optimization Surrogate Search.- PCR: A Large-Scale Benchmark for Pig Counting in Real World.- A Hierarchical Theme Recognition Model for Sandplay Therapy.- Change-Aware Network for Damaged Roads Recognition and Assessment Based on Multi-temporal Remote Sensing Imageries.- UAM-Net: An Attention-Based Multi-Level Feature Fusion UNet for Remote Sensing Image Segmentation.- Improved Conditional Generative Adversarial Networks for SAR-to-Optical Image Translation.- A Novel Cross Frequency-domain Interaction Learning for Aerial Oriented Object Detection.- DBDAN: Dual-Branch Dynamic Attention Network for Semantic Segmentation of Remote Sensing Images.- Multi-scale Contrastive Learning for Building Change Detection in Remote Sensing ImagesShadow Detection of Remote Sensing Image by Fusion of Involution and Shunted Transformer.- Few-shot Infrared Image Classification with Partial Concept Feature.- AGST-LSTM: The ConvLSTM model combines attention and gate structure for spatiotemporal sequence prediction learning.- A Shape-based Quadrangle Detector for Aerial Images.- End-to-end Unsupervised Style and Resolution Transfer Adaptation Segmentation Model for Remote Sensing ImagesA physically feasible counter-attack method for remote sensing imaging point clouds.- Adversarial Robustness via Multi-experts framework for SAR recognition with Class Imbalanced.- Recognizer Embedding Diffusion Generation for Few-shot SAR Recognization.- A Two-Stage Federated Learning Framework for Class Imbalance in Aerial Scene ClassificationSAR Image Authentic Assessment with Bayesian Deep Learning and Counterfactual Explanations.- Circle Representation Network for Specific Target Detection in Remote Sensing Image.- A Transformer-Based Adaptive Semantic Aggregation Method for UAV Visual Geo-Localization.- Lightweight Multiview Mask Contrastive Network for Small-sample Hyperspectral Image Classification.- Dim moving target detection based on imaging uncertainty analysis and hybrid entropy

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Book SynopsisThe 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis. Table of ContentsRSID: A remote sensing image dehazing network.- ContextNet: Learning Context Information for Texture-less Light Field Depth Estimation.- An Efficient Way for Active None-line-of-sight: End-to-end Learned Compressed NLOS Imaging.- DFAR-Net: Dual-Input Three-Branch Attention Fusion Reconstruction Network for Polarized Non-Line-of-Sight Imaging.- EVCPP:Example-driven Virtual Camera Pose Prediction for cloud performing arts scenes.- RBSR: Efficient and Flexible Recurrent Network for Burst Super-Resolution.- WDU-Net: Wavelet-Guided Deep Unfolding Network for Image Compressed Sensing Reconstruction.- Memory-Augmented Spatial-Temporal Consistency Network for Video Anomaly Detection.- Frequency and Spatial Domain Filter Network for Visual Object Tracking.- Enhancing Feature Representation for Anomaly Detection via Local-and-Global Temporal Relations and a Multi-Stage Memory.- DFAformer: A Dual Filtering Auxiliary Transformer for Efficient Online Action Detection in Streaming Videos.- Relation-guided Multi-stage Feature Aggregation Network for Video Object Detection.- Multimodal Local Feature Enhancement Network for Video Summarization.- Asymmetric Attention Fusion for Unsupervised Video Object Segmentatio.- Flow-Guided Diffusion Autoencoder for Unsupervised Video Anomaly detection.- Prototypical Transformer for Weakly Supervised Action Segmentation.- Unimodal-Multimodal Collaborative Enhancement for Audio-Visual Event Localization.- Dual-memory feature aggregation for video object detection.- Going Beyond Closed Sets: A Multimodal Perspective for Video Emotion Analysis.- Temporal-Semantic Context Fusion for Robust Weakly Supervised Video Anomaly Detection.- A Survey: the Sensor-based Method for Sign Language Recognition.- Utilizing Video Word Boundaries and Feature-based Knowledge Distillation Improving Sentence-level Lip Reading.- Denoised Temporal Relation Network for Temporal Action Segmentation.- 3D Lightweight Spatial-Spectral Attention Network for Hyperspectral Image Classification.- Deepfake Detection via Fine-Grained Classification and Global-Local Information Fusion.- Unsupervised Image-to-Image Translation with Style Consistency.- SemanticCrop: Boosting Contrastive Learning via Semantic-cropped Views.- Transformer-based multi-object tracking in unmanned aerial vehicles.- HEI-GAN: A Human-Environment Interaction based GAN for Multimodal Human Trajectory Prediction.- CenterMatch: A Center Matching Method for Semi-supervised Facial Expression Recognition.- Cross-Dataset Distillation with Multi-Tokens for Image Quality Assessment.- Quality-Aware CLIP for Blind Image Quality Assessment.- Multi-Agent Perception via Co-Attentive Communication Mechanism.- DBRNet:Dual-Branch Real-Time Segmentation NetWork For Metal Defect Detection.- MaskDiffuse: Text-guided Face Mask Removal based on Diffusion Models.- Image Generation Based Intra-class Variance Smoothing for Fine-grained Visual Classification.- Cross-Domain Soft Adaptive Teacher for Syn2Real Object Detection.- Dynamic Graph-Driven Heat Diffusion: Enhancing Industrial Semantic Segmentation.- EKGRL: Entity-based Knowledge Graph Representation Learning for Fact-based Visual Question Answering.- Disentangled Attribute Features Vision Transformer for Pedestrian Attribute Recognition.- A high-resolution network based on feature redundancy reduction and attention mechanism.

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Book SynopsisThe 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis. Table of ContentsFeature Enhancement with Text-specific Region Contrast for Scene Text Detection.- Learning Efficient Representations for Patent Drawing Retrieval.- HelixNet: Dual Helix Cooperative Decoders for Scene Text Removal.- Semantic-information Space Sharing Interaction Network for Arbitrary Shape Text Detection.- AIE-KB: Information Extraction Technology with Knowledge Base for Chinese Archival Scenario.- Deep Hough Transform For Gaussian Semantic Box-Lines Alignment.- Chinese-Vietnamese Cross-lingual Event Causality Identification Based on Syntactic Graph Convolution.- MCKIE: Multi-Class Key Information Extraction from Complex Documents based on Graph Convolutional Network.- A Pre-trained Model For Chinese Medical Record Punctuation Restoration.- "English and Spanish Bilinguals’ Language Processing: An ALE-based Meta-analysis of Neuroimaging Studies".- Robust Subspace Learning with Double Graph Embedding Unsupervised Feature Selection via Nonlinear Representation and Adaptive Structure Preservation.- Text Causal Discovery Based on Sequence Structure Information.- MetaSelection: A Learnable Masked AutoEncoder for Multimodal Sentiment Feature Selection.- Image Manipulation Localization based on Multiscale Convolutional Attention.- Bi-Stream Multiscale Hamhead Networks with Contrastive Learning for Image forgery Localization.- Fuse Tune: Hierarchical Decoder Towards Efficient Transfer Learning.- Industrial-SAM with Interactive Adapter.- Mining Temporal Inconsistency with 3D Face Model for Deepfake Video Detection.- DT-TransUNet: A Dual-task Model for Deepfake Detection and Segmentation.- Camouflaged Object Detection via Global-edge Context and Mixed-scale Refinement.- Enhancing CLIP-Based Text-Person Retrieval by Leveraging Negative Samples.- Global Selection and Local Attention Network for Referring Image Segmentation.- MTQ-Caps: A Multi-Task Capsule Network for Blind Image Quality Assessment.- VCD: Visual Causality Discovery for Cross-Modal Question Reasoning.- Multimodal Topic and Sentiment Recognition for Chinese Data Based on Pre-trained Encoders.- Multi-Feature Fusion-Based Central Similarity Deep Supervised Hashing.- VVA: Video Values Analysis.- Dynamic Multi-modal Prompting for Efficient Visual Grounding.- A Graph-involved Lightweight Semantic Segmentation Network.- User-Aware Prefix-Tuning is a Good Learner for Personalized Image Captioning.- An End-to-End Transformer with Progressive Tri-modal Attention for Multi-modal Emotion Recognition.- Target-oriented Multi-criteria Band Selection for Hyperspectral Image.- Pairwise Negative Sample Mining for Human-Object Interaction Detection.- An Evolutionary Multiobjective Optimization Algorithm based on Manifold Learning.- Path Planning of Automatic Parking System by A Point-based Genetic AlgorithmPenalty-Aware Memory Loss for Deep Metric Learning.- Central and Directional Muti-Neck Knowledge Distillation.- Online Class-incremental Learning in Image Classification based on AttentionOnline airline baggage packing based on hierarchical tree A2C-reinforcement learning framework

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Book SynopsisThe 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis. Table of ContentsA Quantum-based Attention Mechanism in Scene Text Detection.- NCMatch: Semi-Supervised Learning with Noisy Labels via Noisy Sample Filter and Contrastive Learning.- Data-free Low-bit Quantization via Dynamic Multi-teacher Knowledge Distillation.- LeViT-UNet: Make Faster Encoders with Transformer for Medical Image Segmentation.- DUFormer: Solving Power Line Detection Task in Aerial Images using Semantic Segmentation.- Space-Transform Margin Loss with Mixup for Long-tailed Visual Recognition.- A Multi-Perspective Squeeze Excitation Classifier Based on Vision Transformer for Few Shot Image Classification.- ITCNN: Incremental Learning Network Based on ITDA and Tree Hierarchical CNN.- Periodic-Aware Network for Fine-grained Action Recognition.- Learning Domain-invariant Representations from Text for Domain Generalization.- TSTD:A Cross-modal Two Stages Network with New Trans-Decoder for Point Cloud Semantic Segmentation.- NeuralMAE: Data-Efficient Neural Architecture Predictor with Masked Autoencoder.- Co-Regularized Facial Age Estimation with Graph-Causal Learning.- Online Distillation and Preferences Fusion for Graph Convolutional Network-based Sequential Recommendation.- Grassmann Graph Embedding for Few-Shot Class Incremental Learning.- Global Variational Convolution Network for Semi-Supervised Node Classification on Large-scale Graphs.- Frequency Domain Distillation for Data-Free Quantization of Vision Transformer.- An ANN-Guided Approach to Task-Free Continual Learning with Spiking Neural Networks.- Multi-Adversarial Adaptive Transformers for Joint Multi-Agent Trajectory Prediction.- Enhancing Open-Set Object Detection via Uncertainty-Boxes Identification.- Interventional Supervised Learning for Person Re-Identification.- DP-INNet: Dual-Path Implicit Neural Network for Spatial and Spectral Features Fusion in Pan-sharpening.- Stable Visual Pattern Mining via Pattern Probability Distribution.- Dynamic Visual Prompt Tuning for Parameter Efficient Transfer Learning.- C-volution: A Hybrid operator for Visual Recognition.- Motor Imagery EEG Recognition Based on an Improved Convolutional Neural Network with Parallel Gate Recurrent Unit.- A Stable Vision Transformer for Out-of-Distribution Generalization.- Few-Shot Classification with Semantic Augmented Activators.- MixPose: 3D Human Pose Estimation with Mixed Encoder.- Image Manipulation Detection Based on Ringed Residual Edge Artifact Enhancement and Multiple Attention Mechanisms.- Improving Masked Autoencoders by Learning Where to Mask.- An Audio Correlation-Based Graph Neural Network for Depression Recognition.- Dynamic Gesture Recognition based on 3D Central Difference Separable Residual LSTM Coordinate Attention Networks.- QESAR: Query Effective Decision-based Attack on Skeletal Action Recognition.- A Closer Look at Few-shot Object Detection.- Learning-without-Forgetting via Memory Index in Incremental Object Detection.- SAMDConv: Spatially Adaptive Multi-scale Dilated Convolution.- SADD:Generative Adversarial Networks via Self-Attention and Dual Discriminator in Unsupervised Domain Adaptation.- ELFLN: An Efficient Lightweight Facial Landmark Network Based on Hybrid Knowledge Distillation.- Enhancing Continual Noisy Label Learning with Uncertainty-based Sample Selection and Feature Enhancement

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Book SynopsisThe 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis. Table of ContentsDecoupled Contrastive Learning for Long-Tailed Distribution.- MFNet: A Channel Segmentation-based Hierarchical Network for Multi-Food Recognition.- Improving the Adversarial Robustness of Object Detection with Contrastive Learning.- CAWNet: A Channel Attention Watermarking Attack Network Based on CWABlock.- Global Consistency Enhancement Network for Weakly-Supervised Semantic Segmentation.- Enhancing Model Robustness against Adversarial Attacks with an Anti-Adversarial Module.- FGPTQ-ViT:Fine-grained Post-training Quantization for Vision Transformers.- Learning Hierarchical Representations in Temporal and Frequency Domains for Time Series Forecasting.- DeCAB: Debiased Semi-Supervised Learning for Imbalanced Open-Set Data.- An Effective Visible-Infrared Person Re-identification Network based on Second-Order Attention and Mixed Intermediate Modality.- Quadratic polynomial residual network for no-reference image quality assessment.- Interactive Learning for Interpretable Visual Recognition via Semantic-Aware Self-Teaching Framework.-Adaptive and Compact Graph Convolutional Network for Micro-Expression Recognition.- Consistency Guided Multiview Hypergraph Embedding Learning with Multiatlas-Based Functional Connectivity Networks Using Resting-State fMRI.- A Diffusion Simulation User Behavior Perception Attention Network for Information Diffusion Prediction.- A Representation Learning Link Prediction Approach Using Line Graph Neural Networks.- Event Sparse Net: Sparse Dynamic Graph Multi-representation Learning with Temporal Attention for Event-based Data.- Federated Learning Based on Diffusion Model to Cope with non-IID DataSFRSwin: A shallow significant feature retention Swin Transformer for fine-grained image classification of wildlife species.- A robust and high accurate method for hand kinematics decoding from neural populations.- Multi-head Attention Induced Dynamic Hypergraph Convolutional Networks.- Self Supervised Temporal Ultrasound Reconstruction for Muscle Atrophy EvaluationSalient Object Detection Using Reciprocal Learning.- Graphormer-based Contextual Reasoning Network for Small Object Detection.- PVT-Crowd:Bridging Multi-scale Features from Pyramid Vision Transformer for Weakly-Supervised Crowd Counting.- Multi-view Contrastive Learning Network for Recommendation.- Uncertainty-confidence fused pseudo-labeling for Graph Neural Networks.- FSCD-Net: A Few-Shot Stego Cross-Domain Net for Image Steganalysis.- Preference Contrastive Learning for Personalized Recommendation.- GLViG: Global and Local Vision GNN May be What You Need for Vision.- SVDML: Semantic and Visual space Deep Mutual Learning for Zero-Shot Learning.- Heterogeneous Graph Attribute Completion via Efficient Meta-path Context-aware Learning.- Fine-Grain Classification Method of Non-Small Cell Lung Cancer Based on Progressive Jigsaw and Graph Convolutional Network.- Improving Transferability of Adversarial Attacks with Gaussian Gradient Enhance Momentum.- Boundary Guided Feature fusion Network for Camouflaged Object Detection.- Saliency Driven Monocular Depth Estimation based on Multi-scale Graph Convolutional Network.- Mask-guided Joint Single Image Specular Highlight Detection and Removal.- CATrack: Convolution and Attention Feature Fusion for Visual Object TrackingSText-DETR: End-to-End Arbitrary-Shaped Text Detection with Scalable Query in Transformer.- SSHRF-GAN: Spatial-Spectral Joint High Receptive Field GAN for Old Photo Restoration

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Book SynopsisThe 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis. Table of ContentsDual-stream Context-aware Neural Network for Survival Prediction from Whole Slide Images.- A multi-label image recognition algorithm based on spatial and semantic correlation interaction.- Hierarchical Spatial-Temporal Network for Skeleton-Based Temporal Action Segmentation.- Multi-Behavior Enhanced Graph Neural Networks for Social Recommendation.- A Complex-valued Neural Network based Robust Image Compression.- Binarizing Super-resolution Neural Network without Batch Normalization.- Infrared and Visible Image Fusion via Test-Time Training.- Graph-based Dependency-aware Non-Intrusive Load Monitoring.- Few-shot Object Detection via Classify-free RPN.- IPFR: Identity-Preserving Face Reenactment with Enhanced Domain Adversarial Training and Multi-level Identity Priors.- L2MNet: Enhancing Continual Semantic Segmentation with Mask Matching.- Adaptive Channel Pruning for Trainability Protection.- Exploiting Adaptive Crop and Deformable Convolution for Road Damage Detection.- Cascaded-scoring Tracklet Matching for Multi-object Tracking.- Boosting Generalization Performance in Person Re-IdentificationSelf-Guided Transformer for Video Super-Resolution.- SAMP: Sub-task Aware Model Pruning with Layer-wise Channel Balancing for Person Search.- MKB: Multi-kernel Bures Metric for Nighttime Aerial Tracking.- Deep Arbitrary-Scale Unfolding Network for Color-Guided Depth Map Super-Resolution.- SSDD-Net: A Lightweight and Efficient Deep Learning Model for Steel Surface Defect Detection.- Effective Small Ship Detection with Enhanced-YOLOv7.- PiDiNeXt: An Efficient Edge Detector based on Parallel Pixel Difference Networks.- Transpose and Mask: Simple and Effective Logit-Based Knowledge Distillation for Multi-Attribute and Multi-Label Classification.- CCSR-Net: Unfolding coupled convolutional sparse representation for multi-focus image fusion.- FASONet: A Feature Alignment-Based SAR and Optical Image Fusion Network for Land Use Classification.- De Novo Design of Target-Specific Ligands Using BERT-Pretrained Transformer.- CLIP for Lightweight Semantic Segmentation.- Teacher-Student Cross-Domain Object Detection Model Combining Style Transfer and Adversarial Learning.- Computing 2D Skeleton via Generalized Electric Potential.- Illumination Insensitive Monocular Depth Estimation based on Scene Object Attention and Depth Map Fusion.- A Few-Shot Medical Image Segmentation Network with Boundary Category Correction.- Repdistiller: Knowledge Distillation Scaled by Re-parameterization for Crowd Counting.- Multi-depth Fusion Transformer and Batch Piecewise Loss for Visual Sentiment Analysis.- Expanding the Horizons: Exploring Further Steps in Open-Vocabulary Segmentation.- Exploring a Distillation with Embedded Prompts for Object Detection in Adverse Environments.- TEFNet: Target-Aware Enhanced Fusion Network for RGB-T Tracking.- DARN: Crowd Counting Network Guided by Double Attention Refinement.- DFR-ECAPA: Diffusion Feature Refinement for Speaker Verification based on ECAPA-TDNN.- Half Aggregation Transformer for Exposure Correction.- Deformable Spatial-Temporal Attention for Lightweight Video Super-Resolution

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Book SynopsisThe 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis. Table of ContentsOKGR: Occluded Keypoint Generation and Refinement for 3D Object Detection.- Camouflaged Object Segmentation Based on Fractional Edge Perception.- DecTrans: Person Re-identification with Multifaceted Part Features via Decomposed Transformer.- AHT: A Novel Aggregation Hyper-Transformer for Few-Shot Object Detection.- Feature Refinement from Multiple Perspectives for High Performance Salient Object Detection.- Feature Disentanglement and Adaptive Fusion for Improving Multi-Modal Tracking.- Modality Balancing Mechanism for RGB-Infrared Object Detection in Aerial Image.- Pacific Oyster Gonad Identification and Grayscale Calculation Based on Unapparent Object Detection.- Multi-task Self-supervised Few-Shot Detection.- CSTrack: A Comprehensive and Concise Vision Transformer Tracker.- Feature Implicit Enhancement via Super-Resolution for Small Object Detection.- Improved detection method for SODL-YOLOv7 intensive juvenile abalone.- MVP-SEG: Multi-View Prompt Learning for Open-Vocabulary Semantic Segmentation.- Context-FPN and Memory Contrastive Learning for Partially Supervised Instance Segmentation.- A Dynamic Tracking Framework Based on Scene Perception.- HPAN: A Hybrid Pose Attention Network for Person Re-identification.- SpectralTracker: Jointly High and Low-Frequency Modeling for Tracking.- DiffusionTracker: Targets Denoising based on Diffusion Model for Visual Tracking.- Instance-proxy Loss for Semi-supervised Learning with Coarse Labels.- FAFVTC: A Real-time Network for Vehicle Tracking and Counting.- Ped-Mix: Mix Pedestrians for Occluded Person Re-Identification.- Object-Aware Transfer-based Black-box Adversarial Attack on Object Detector.- HTNet: A Hybrid Model Boosted by Triple Self-Attention for Crowd Counting.- Reliable Boundary Samples-based Proxy Pairs for Unsupervised Person Re- dentification.- High-Resolution Feature Representation Driven Infrared Small-Dim Object Detection.- Few-Shot Object Detection Algorithm Based on Adaptive Relation Distillation.- A real-time safety detector based on re-parameterization multiscale feature fusion for forklift driving.- RTMDet-R2：An Improved Real-Time Rotated Object Detector.- Boosting Object Detection in Foggy Scenes via Dark Channel Map and Union Training Strategy.- Object Centric Body Part Attention Network for Human-Object Interaction Detection.- Salient Feature Enhanced Multi-Object Tracking with Soft-Sparse Attention in Transformer.- A BiGRU based Adaptive Gain Estimation for radar Multi-target Tracking.- Prompt based Lifelong Person Re-identification.- Hierarchical Focused Feature Pyramid Network for Small Object Detection JLInst: Boundary-Mask Joint Learning for Instance Segmentation.- Boosting One-stage Multi Object Tracking with Attention Learning.- TPNet: Enhancing Weakly Supervised Polyp Frame Detection with Temporal Encoder and Prototype-based Memory Bank.- Learning Frequency-based Disentanglement and Filtering for Generalizable Person Re-identification.- Stereo3DMOT: Stereo Vision Based 3D Multi-Object Tracking with Multimodal ReID.- Emphasizing Boundary-Positioning and Leveraging Multi-Scale Feature Fusion for Camouflaged Object Detection

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Book SynopsisThe 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis. Table of ContentsGrowth Simulation Network for Polyp Segmentation.- Brain Diffuser: An End-To-End Brain Image to Brain Network Pipeline.- CCJ-SLC: A Skin Lesion Image Classification Method based on Contrastive Clustering and Jigsaw Puzzle.- A Real-Time Network for Fast Breast Lesion Detection in Ultrasound Videos.- CBAV-Loss: Crossover and Branch Losses for Artery-vein Segmentation in OCTA Images.- Leveraging Data Correlations For Skin Lesion Classification.- CheXNet: Combing Transformer and CNN for Thorax Disease Diagnosis from Chest X-ray Images.- Cross Attention Multi Scale CNN-Transofmer Hybrid encoder is General Medical Image Learner.- Weakly/Semi-supervised Left Ventricle Segmentation in 2D Echocardiography with Uncertain Region-aware Contrastive Learning.- Spatial-Temporal Graph Convolutional Network for Insomnia Classification via Brain Functional Connectivity Imaging of rs-fMRI.- Probability-based Nuclei Detection and Critical-Region Guided Instance Segmentation.- FlashViT: A Flash Vision Transformer with Large-scale Token Merging for Congenital Heart Disease Detection.- Semi-supervised Retinal Vessel Segmentation through Point Consistency.- Knowledge Distillation of Attention and Residual U-Net: Transfer from Deep to Shallow Models for Medical Image Classification.- Two-stage deep learning segmentation for tiny brain regions.- Encoder Activation Diffusion and Decoder Transformer Fusion Network for Medical Image Segmentation.- Liver segmentation via learning cross-modality content-aware representation.- Semi-Supervised Medical Image Segmentation based on Multi-scale Knowledge Discovery and Multi-task Ensemble.- LATrans-Unet: Improving CNN-Transformer with Location-Adaptive for Medical Image Segmentation.- Adversarial Keyword Extraction and Semantic-Spatial Feature Aggregation for Clinical Report Guided Thyroid Nodule Segmentation.- A Multi-Modality Driven Promptable Transformer for Automated Parapneumonic Effusion Staging.- Assessing the Social Skills of Children with Autism Spectrum Disorder via Language-Image Pre-training Models.- PPS: Semi-supervised 3D Biomedical Image Segmentation via Pyramid Pseudo-Labeling Supervision.- A Novel Diffusion-Model-Based OCT Image Inpainting Algorithm for Wide Saturation Artifacts.- Only Classification Head is Sufficient for Medical Image Segmentation.- Task-incremental Medical Image Classification with Task-specific Batch Normalization.- Hybrid Encoded Attention Networks for Accurate Pulmonary Artery-Vein Segmentation in Noncontrast CT Images.- Multi-Modality Fusion based Lung Cancer Survival Analysis with Self-Supervised Whole Slide Image Representation Learning.- Incorporating Spiking Neural Network for Dynamic Vision Emotion Analysis.- PAT-Unet: Paired Attention Transformer for Efficient and Accurate Segmentation of 3D Medical Images.- Cell-CAEW: Cell Instance Segmentation based on ConvAttention and Enhanced Watershed.- A Comprehensive Multi-modal Domain Adaptative Aid Framework for Brain Tumor Diagnosis.- Joint Boundary-Enhanced and Topology-Preserving Dual-Path Network for Retinal Layer Segmentation in OCT Images with Pigment Epithelial Detachment.- Spatial Feature Regularization and Label Decoupling based Cross-Subject Motor Imagery EEG Decoding.- Autism spectrum disorder diagnosis using graph neural network based on graph pooling and self-adjust filter.- CDBIFusion: A Cross-Domain Bidirectional Interaction Fusion Network for PET and MRI Images.- LF-LVS: Label-Free Left Ventricular Segmentation for Transthoracic Echocardiogram.- Multi-atlas Representations based on Graph Convolutional Networks for Autism Spectrum Disorder Diagnosis.- MS-UNet: Swin Transformer U-Net with Multi-scale Nested Decoder for Medical Image Segmentation with Small Training Data.- GCUNET: Combining GNN and CNN for Sinogram Restoration in Low-Dose SPECT Reconstruction.- A two-stage whole body bone SPECT scan image inpainting algorithm for residual urine artifacts based on contextual attention.

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Book SynopsisThis full-color book will teach you how to use Adobe Illustrator's various tools to create infographics, as well as basic page layouts for them. This is the first of three volumes which will cover all the fundamentals of Illustrator, an industry standard application used by graphic designers and marketing and communication teams. How is an infographic different from a logo or any other illustration? What additional thought processes, skills, or software tools should be utilized to create an infographic? In this first volume of Illustrator Basics, you will learn the answers to all these questions. Author Jennifer Harder will walk you through creating basic infographics in Illustrator using Basic Shape tools, Pen Tools, Type Tools, and Symbols. Upon completing this volume, you will have an appreciation for how easy it is to design an infographic and discover how rudimentary shapes and colors can affect readability while conveying meaning to your audience. You will be able to use this knowledge to create your own infographics using Illustrator’s wide array of tools. Who This Book Is For Discover the tools within Illustrator that are ideal for creating basic infographics Develop a logo based upon a scanned sketch Gain an understanding of different infographic layouts and the process of reviewing them with your client Who This Book Is For Beginner-level designers and others who are interested in learning the process of creating infographics for their company, the classroom, for a visual resume, an article in a magazine, or a webpage.Table of ContentsChapter 1: What are Infographics? Chapter 2: Preparation for Creating a Logo and Infographics Chapter 3: Scanner Basics Chapter 4: Setting Up Your Workspace Chapter 5: Working with Artboards and Saving Files Chapter 6: A Basic Review of Illustrator’s Shape Tools Chapter 7: A Basic Review of Illustrator’s Pen Tools Chapter 8: Working with Illustrator’s Layers and additional Drawing and Type Tools Chapter 9: Creating your first Infographic Projects

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Book Synopsis1: Ready Yourself to Learn From AI.- 2: Reprogram Your Learning Patterns.- 3: Regulate How You Learn.- 4: Re-Learn While Working.-5: Design Your Own Learning.- 6: Re-energize Doing.- 7: Re-Assess with Generative AI.- 8: Re-Adjust with AI.- 9: Prototype Learning.- 10: Re-Iterate How You Learn.- 11:Reconciling Using Generative AI.- 12: Remember the Algorithms.- 13: Continuously Improve and Learn with AI.- 14: Build Your Own Teaching Bots.- 15: Re-Invent Reinforcement.- 16: Learn with Other Bots.- 17: Transform Your Organization.- 18: Reclaim Your Creative Content.- 19: Fill in the Blanks.- 20: An Intelligent Conclusion.

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Book SynopsisPart I: Foundations for Effective Visuals.- Chapter 1: Getting Started.- Part II: No-Cost Software Titles.- Chapter 2: Paint.NET: The Free Image Editor for Windows.- Chapter 3: GIMP: A Powerful Free Alternative to Photoshop.- Chapter 4: FotoSketcher: Turn Photos Into Art.- Chapter 5: Inkscape: The Free Program for Creating Scalable Vector Graphics.- Part III: Using Predesigned Templates, Stock Images and AI, and Resources for Large Format Graphics.- Chapter 6: Affordable Web-Based Solutions.- Chapter 7: No-Cost Stock Image Resources .- Chapter 8: Utilizing Generative AI Resources Chapter.- Chapter 9: Large Format and Vehicle Graphics.- Part IV: Employee Involvement.- Chapter 10: Cultivating a Visual Branding Culture. Part V: Useful Learning Resources.- Chapter 11: Appendix: Useful Learning Resources.

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