NONLINEAR MODEL-BASED IMAGE/VIDEO PROCESSING AND ANALYSIS

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【簡介】 A comprehensive survey of techniques and applications in image andvideo processing and analysis A widely varied selection of experts provides extensive coverage ofnonlinear model-based techniques in image and video processing andanalysis. This volume not only details new techniques in stillimage and digital video but also discusses applications in computervision, multimedia, and visual information retrieval systems. Allnonlinear, model-based techniques are detailed, and a complete andup-to-date accounting of the most important and effectivealgorithms is included for every application and techniquediscussed. The applications described also include such real-world uses asmultimedia information retrieval, automatic surveillance, liptracking, and much more. Analytical techniques such as Boolean andstack filters, rational functions, mathematical morphologytechniques, and adaptive order statistic filtering receive detailedtreatment as they apply to specific uses such as image compression,segmentation, old motion picture restoration, image/videointerpolation, and noise smoothing. Chapters include: * Optimal Design of Boolean and Stack Filters and Their Applicationin Image Processing * Image Processing Using Rational Functions * Mathematical Morphology and Motion Picture Restoration * Adaptive Order Statistic Filtering of Still Images and ImageSequences * Video Segmentation Based on Multiple Features for Interactive andAutomatic Multimedia Applications * Invariant Features in Pattern Recognition-Fundamentals andApplications * Image Models for Facial Feature Tracking Students, engineers, and scientists will appreciate both the widerange of expert opinion in this comprehensive volume and thedetailed coverage of the algorithms and mathematical models used instate-of-the-art image and video processing. 【目錄】

MODEL-BASED REINFORCEMENT LEARNING: FROM DATA TO ACTIONS WITH A PYTHON-BASED TOOLBOX

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DESCRIPTION Model-Based Reinforcement Learning Explore a comprehensive and practical approach to reinforcement learning Reinforcement learning is an essential paradigm of machine learning, wherein an intelligent agent performs actions that ensure optimal behavior from devices. While this paradigm of machine learning has gained tremendous success and popularity in recent years, previous scholarship has focused either on theory—optimal control and dynamic programming – or on algorithms—most of which are simulation-based. Model-Based Reinforcement Learning provides a model-based framework to bridge these two aspects, thereby creating a holistic treatment of the topic of model-based online learning control. In doing so, the authors seek to develop a model-based framework for data-driven control that bridges the topics of systems identification from data, model-based reinforcement learning, and optimal control, as well as the applications of each. This new technique for assessing classical results will allow for a more efficient reinforcement learning system. At its heart, this book is focused on providing an end-to-end framework—from design to application—of a more tractable model-based reinforcement learning technique. Model-Based Reinforcement Learning readers will also find: A useful textbook to use in graduate courses on data-driven and learning-based control that emphasizes modeling and control of dynamical systems from data Detailed comparisons of the impact of different techniques, such as basic linear quadratic controller, learning-based model predictive control, model-free reinforcement learning, and structured online learning Applications and case studies on ground vehicles with nonholonomic dynamics and another on quadrator helicopters An online, Python-based toolbox that accompanies the contents covered in the book, as well as the necessary code and data Model-Based Reinforcement Learning is a useful reference for senior undergraduate students, graduate students, research assistants, professors, process control engineers, and roboticists.