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Books in Machine vision

  • Advanced Methods and Deep Learning in Computer Vision

    • 1st book:metaData.edition
    • E. R. Davies contributors.plusContributors
    • publicationLanguages:en
    Advanced Methods and Deep Learning in Computer Vision presents advanced computer vision methods, emphasizing machine and deep learning techniques that have emerged during the past 5–10 years. The book provides clear explanations of principles and algorithms supported with applications. Topics covered include machine learning, deep learning networks, generative adversarial networks, deep reinforcement learning, self-supervised learning, extraction of robust features, object detection, semantic segmentation, linguistic descriptions of images, visual search, visual tracking, 3D shape retrieval, image inpainting, novelty and anomaly detection. This book provides easy learning for researchers and practitioners of advanced computer vision methods, but it is also suitable as a textbook for a second course on computer vision and deep learning for advanced undergraduates and graduate students.

  • Intravascular Ultrasound

    From Acquisition to Advanced Quantitative Analysis
    • 1st book:metaData.edition
    • Simone Balocco
    • publicationLanguages:en
    Intravascular Ultrasound: From Acquisition to Advanced Quantitative Analysis covers topics of the whole imaging pipeline, ranging from the definition of the clinical problem and image acquisition systems to image processing and analysis, including the assisted clinical-decision making procedures and treatment planning (stent deployment and follow up). Atherosclerosis, a disease of the vessel wall that produces vessel narrowing and obstruction, is the major cause of cardiovascular diseases, such as heart attack or stroke. This book covers all aspects of this imaging tool that allows for the visualization of internal vessel structures and the quantification and characterization of coronary plaque.

  • Deep Learning through Sparse and Low-Rank Modeling

    • 1st book:metaData.edition
    • Zhangyang Wang contributors.plusContributors
    • publicationLanguages:en
    Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models—those that emphasize problem-specific Interpretability—wit... recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide variety of applications in computer vision, machine learning, signal processing, and data mining. This book will be highly useful for researchers, graduate students and practitioners working in the fields of computer vision, machine learning, signal processing, optimization and statistics.

  • Multimodal Behavior Analysis in the Wild

    Advances and Challenges
    • 1st book:metaData.edition
    • Xavier Alameda-Pineda contributors.plusContributors
    • publicationLanguages:en
    Multimodal Behavioral Analysis in the Wild: Advances and Challenges presents the state-of- the-art in behavioral signal processing using different data modalities, with a special focus on identifying the strengths and limitations of current technologies. The book focuses on audio and video modalities, while also emphasizing emerging modalities, such as accelerometer or proximity data. It covers tasks at different levels of complexity, from low level (speaker detection, sensorimotor links, source separation), through middle level (conversational group detection, addresser and addressee identification), and high level (personality and emotion recognition), providing insights on how to exploit inter-level and intra-level links. This is a valuable resource on the state-of-the- art and future research challenges of multi-modal behavioral analysis in the wild. It is suitable for researchers and graduate students in the fields of computer vision, audio processing, pattern recognition, machine learning and social signal processing.

  • Cooperative and Graph Signal Processing

    Principles and Applications
    • 1st book:metaData.edition
    • Petar Djuric contributors.plusContributors
    • publicationLanguages:en
    Cooperative and Graph Signal Processing: Principles and Applications presents the fundamentals of signal processing over networks and the latest advances in graph signal processing. A range of key concepts are clearly explained, including learning, adaptation, optimization, control, inference and machine learning. Building on the principles of these areas, the book then shows how they are relevant to understanding distributed communication, networking and sensing and social networks. Finally, the book shows how the principles are applied to a range of applications, such as Big data, Media and video, Smart grids, Internet of Things, Wireless health and Neuroscience. With this book readers will learn the basics of adaptation and learning in networks, the essentials of detection, estimation and filtering, Bayesian inference in networks, optimization and control, machine learning, signal processing on graphs, signal processing for distributed communication, social networks from the perspective of flow of information, and how to apply signal processing methods in distributed settings.

  • Computer Vision for Assistive Healthcare

    • 1st book:metaData.edition
    • Leo Marco contributors.plusContributors
    • publicationLanguages:en
    Computer Vision for Assistive Healthcare describes how advanced computer vision techniques provide tools to support common human needs, such as mental functioning, personal mobility, sensory functions, daily living activities, image processing, pattern recognition, machine learning and how language processing and computer graphics cooperate with robotics to provide such tools. Users will learn about the emerging computer vision techniques for supporting mental functioning, algorithms for analyzing human behavior, and how smart interfaces and virtual reality tools lead to the development of advanced rehabilitation systems able to perform human action and activity recognition. In addition, the book covers the technology behind intelligent wheelchairs, how computer vision technologies have the potential to assist blind people, and about the computer vision-based solutions recently employed for safety and health monitoring.

  • Imaging Genetics

    • 1st book:metaData.edition
    • Adrian Dalca contributors.plusContributors
    • publicationLanguages:en
    Imaging Genetics presents the latest research in imaging genetics methodology for discovering new associations between imaging and genetic variables, providing an overview of the state-of the-art in the field. Edited and written by leading researchers, this book is a beneficial reference for students and researchers, both new and experienced, in this growing area. The field of imaging genetics studies the relationships between DNA variation and measurements derived from anatomical or functional imaging data, often in the context of a disorder. While traditional genetic analyses rely on classical phenotypes like clinical symptoms, imaging genetics can offer richer insights into underlying, complex biological mechanisms.

  • Methods and Techniques for Fire Detection

    Signal, Image and Video Processing Perspectives
    • 1st book:metaData.edition
    • A. Enis Cetin contributors.plusContributors
    • publicationLanguages:en
    This book describes the signal, image and video processing methods and techniques for fire detection and provides a thorough and practical overview of this important subject, as a number of new methods are emerging. This book will serve as a reference for signal processing and computer vision, focusing on fire detection and methods for volume sensors. Applications covered in this book can easily be adapted to other domains, such as multi-modal object recognition in other safety and security problems, with scientific importance for fire detection, as well as video surveillance. Coverage includes: Camera Based Techniques Multi-modal/Multi-se... fire analysis Pyro-electric Infrared Sensors for Flame Detection Large scale fire experiments Wildfire detection from moving aerial platforms

  • Readings in Computer Vision

    Issues, Problem, Principles, and Paradigms
    • 1st book:metaData.edition
    • Martin A. Fischler contributors.plusContributors
    • publicationLanguages:en
    The field of computer vision combines techniques from physics, mathematics, psychology, artificial intelligence, and computer science to examine how machines might construct meaningful descriptions of their surrounding environment. The editors of this volume, prominent researchers and leaders of the SRI International AI Center Perception Group, have selected sixty papers, most published since 1980, with the viewpoint that computer vision is concerned with solving seven basic problems:Reconstruct... 3D scenes from 2D imagesDecomposing images into their component partsRecognizing and assigning labels to scene objectsDeducing and describing relations among scene objectsDetermining the nature of computer architectures that can support the visual functionRepresenting abstractions in the world of computer memoryMatching stored descriptions to image representationEach chapter of this volume addresses one of these problems through an introductory discussion, which identifies major ideas and summarizes approaches, and through reprints of key research papers. Two appendices on crucial assumptions in image interpretation and on parallel architectures for vision applications, a glossary of technical terms, and a comprehensive bibliography and index complete the volume.

  • Computational Vision

    • 1st book:metaData.edition
    • Harry Wechsler
    • publicationLanguages:en
    The book is suitable for advanced courses in computer vision and image processing. In addition to providing an overall view of computational vision, it contains extensive material on topics that are not usually covered in computer vision texts (including parallel distributed processing and neural networks) and considers many real applications.