Intelligent Image And Video Interpretation

Due to increasing potential in real-world applications such as visual communications, computer assisted biomedical imaging, and video surveillance, image and video interpretations have become an area of growing interest. Intelligent Image and Video Interpretation: Algorithms and Applications covers all aspects of image and video analysis from low-level early visions to high-level recognition. This publication highlights how these techniques have become applicable and will prove to be a valuable tool for researchers, professionals, and graduate students working or studying the fields of imaging and video processing.

Provides up-to-date coverage of the state-of-the-art techniques in intelligent video analytics Explores important applications that require techniques from both artificial intelligence and computer vision Describes multimodality video analytics for different applications Examines issues related to multimodality data fusion and highlights research challenges Integrates various techniques from video processing, data mining and machine learning which has many emerging indoor and outdoor applications of smart cameras in smart environments, smart homes, and smart cities

"This book covers all aspects of image and video analysis from low-level early visions to high-level recognition, highlighting how these techniques have become applicable and will prove to be a valuable tool for researchers, professionals, and graduate students working or studying the fields of imaging and video processing"--Provided by publisher.

Intelligent Image and Video Compression: Communicating Pictures, Second Edition explains the requirements, analysis, design and application of a modern video coding system. It draws on the authors’ extensive academic and professional experience in this field to deliver a text that is algorithmically rigorous yet accessible, relevant to modern standards and practical. It builds on a thorough grounding in mathematical foundations and visual perception to demonstrate how modern image and video compression methods can be designed to meet the rate-quality performance levels demanded by today's applications and users, in the context of prevailing network constraints. "David Bull and Fan Zhang have written a timely and accessible book on the topic of image and video compression. Compression of visual signals is one of the great technological achievements of modern times, and has made possible the great successes of streaming and social media and digital cinema. Their book, Intelligent Image and Video Compression covers all the salient topics ranging over visual perception, information theory, bandpass transform theory, motion estimation and prediction, lossy and lossless compression, and of course the compression standards from MPEG (ranging from H.261 through the most modern H.266, or VVC) and the open standards VP9 and AV-1. The book is replete with clear explanations and figures, including color where appropriate, making it quite accessible and valuable to the advanced student as well as the expert practitioner. The book offers an excellent glossary and as a bonus, a set of tutorial problems. Highly recommended! --Al Bovik An approach that combines algorithmic rigor with practical implementation using numerous worked examples Explains how video compression methods exploit statistical redundancies, natural correlations, and knowledge of human perception to improve performance Uses contemporary video coding standards (AVC, HEVC and VVC) as a vehicle for explaining block-based compression Provides broad coverage of important topics such as visual quality assessment and video streaming

This book initially reviews the major feature representation and extraction methods and effective learning and recognition approaches, which have broad applications in the context of intelligent image search and video retrieval. It subsequently presents novel methods, such as improved soft assignment coding, Inheritable Color Space (InCS) and the Generalized InCS framework, the sparse kernel manifold learner method, the efficient Support Vector Machine (eSVM), and the Scale-Invariant Feature Transform (SIFT) features in multiple color spaces. Lastly, the book presents clothing analysis for subject identification and retrieval, and performance evaluation methods of video analytics for traffic monitoring. Digital images and videos are proliferating at an amazing speed in the fields of science, engineering and technology, media and entertainment. With the huge accumulation of such data, keyword searches and manual annotation schemes may no longer be able to meet the practical demand for retrieving relevant content from images and videos, a challenge this book addresses.This book initially reviews the major feature representation and extraction methods and effective learning and recognition approaches, which have broad applications in the context of intelligent image search and video retrieval. It subsequently presents novel methods, such as improved soft assignment coding, Inheritable Color Space (InCS) and the Generalized InCS framework, the sparse kernel manifold learner method, the efficient Support Vector Machine (eSVM), and the Scale-Invariant Feature Transform (SIFT) features in multiple color spaces. Lastly, the book presents clothing analysis for subject identification and retrieval, and performance evaluation methods of video analytics for traffic monitoring. Digital images and videos are proliferating at an amazing speed in the fields of science, engineering and technology, media and entertainment. With the huge accumulation of such data, keyword searches and manual annotation schemes may no longer be able to meet the practical demand for retrieving relevant content from images and videos, a challenge this book addresses.

"This book provides the reader with a concrete understanding of basic principles and pitfalls for 3-D capturing, highlighting stereoscopic imaging systems including holography"--

This book presents cutting-edge research on various ways to bridge the semantic gap in image and video analysis. The respective chapters address different stages of image processing, revealing that the first step is a future extraction, the second is a segmentation process, the third is object recognition, and the fourth and last involve the semantic interpretation of the image. The semantic gap is a challenging area of research, and describes the difference between low-level features extracted from the image and the high-level semantic meanings that people can derive from the image. The result greatly depends on lower level vision techniques, such as feature selection, segmentation, object recognition, and so on. The use of deep models has freed humans from manually selecting and extracting the set of features. Deep learning does this automatically, developing more abstract features at the successive levels. The book offers a valuable resource for researchers, practitioners, students and professors in Computer Engineering, Computer Science and related fields whose work involves images, video analysis, image interpretation and so on.

This book presents an introduction to new and important research in the images processing and analysis area. It is hoped that this book will be useful for scientists and students involved in many aspects of image analysis. The book does not attempt to cover all of the aspects of Computer Vision, but the chapters do present some state of the art examples.