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Human Activity and Behavior Analysis relates to the field of vision and sensor-based human action or activity and behavior analysis and recognition. The book includes a series of methodologies, surveys, relevant datasets, challenging applications, ideas, and future prospects.The book discusses topics such as action recognition, action understanding, gait analysis, gesture recognition, behavior analysis, emotion and affective computing, and related areas. This volume focuses on relevant activities in three main subject areas: Healthcare and Emotion, Mental Health, and Nurse Care Records.The editors are experts in these arenas and the contributing authors are drawn from high-impact research groups around the world. This book will be of great interest to academics, students, and professionals working and researching in the field of human activity and behavior analysis.

This book offers a comprehensive introduction to seven commonly used image understanding techniques in modern information technology. Readers of various levels can find suitable techniques to solve their practical problems and discover the latest development in these specific domains.The techniques covered include camera model and calibration, stereo vision, generalized matching, scene analysis and semantic interpretation, multi-sensor image information fusion, content-based visual information retrieval, and understanding spatial-temporal behavior. The book provides aspects from the essential concepts overview and basic principles to detailed introduction, explanation of the current methods and their practical techniques. It also presents discussions on the research trends and latest results in conjunction with new development of technical methods. This is an excellent read for those who do not have a subject background in image technology but need to use these techniques to complete specific tasks. These essential information will also be useful for their further study in the relevant fields.

Focusing on how visual information is represented, stored and extracted in the human brain, this book uses cognitive neural modeling in order to show how visual information is represented and memorized in the brain. Breaking through traditional visual information processing methods, the author combines our understanding of perception and memory from the human brain with computer vision technology, and provides a new approach for image recognition and classification. While biological visual cognition models and human brain memory models are established, applications such as pest recognition and carrot detection are also involved in this book.Given the range of topics covered, this book is a valuable resource for students, researchers and practitioners interested in the rapidly evolving field of neurocomputing, computer vision and machine learning.

Introduction to Visual Effects: A Computational Approach is the first single introduction to the computational and mathematical aspects of visual effects, incorporating both computer vision and graphics. The book also provides the readers with the source code to a library, enabling them to follow the chapters directly and build up a complete visual effects platform. The book covers the basic approaches to camera pose estimation, global illumination, and image-based lighting, and includes chapters on the virtual camera, optimization and computer vision, path tracing and many more.Key features include: Introduction to projective geometry, image-based lighting (IBL), global illumination solved by the Monte Carlo method (Pathtracing), an explanation of a set of optimization methods, and the techniques used for calibrating one, two, and many cameras, including how to use the RANSAC algorithm in order to make the process robust, and providing code to be implemented using the Gnu Scientific Library. C/C++ code using the OpenCV library, to be used in the process of tracking points on a movie (an important step for the matchmove process), and in the construction of modeling tools for visual effects. A simple model of the Bidirectional Reflectance Distribution Function (BRDF) of surfaces and the differential rendering method, allowing the reader to generate consistent shadows, supported by a code that can be used in combination with a software like Luminance HDR.

This book emphasizes various image shape feature extraction methods which are necessary for image shape recognition and classification. Focussing on a shape feature extraction technique used in content-based image retrieval (CBIR), it explains different applications of image shape features in the field of content-based image retrieval. Showcasing useful applications and illustrating examples in many interdisciplinary fields, the present book is aimed at researchers and graduate students in electrical engineering, data science, computer science, medicine, and machine learning including medical physics and information technology.