Statistical Learning and Pattern Analysis for Image and Video Processing [electronic resource] / by Nanning Zheng, Jianru Xue.

Pattern Analysis and Statistical Learning -- Unsupervised Learning for Visual Pattern Analysis -- Component Analysis -- Manifold Learning -- Functional Approximation -- Supervised Learning for Visual Pattern Classification -- Statistical Motion Analysis -- Bayesian Tracking of Visual Objects -- Probabilistic Data Fusion for Robust Visual Tracking -- Multitarget Tracking in Video-Part I -- Multi-Target Tracking in Video – Part II -- Information Processing in Cognition Process and New Artificial Intelligent Systems.

The inexpensive collection, storage, and transmission of vast amounts of visual data has revolutionized science, technology, and business. Innovations from various disciplines have aided in the design of intelligent machines able to detect and exploit useful patterns in data. One such approach is statistical learning for pattern analysis. Among the various technologies involved in intelligent visual information processing, statistical learning and pattern analysis is undoubtedly the most popular and important approach, and is the area which has undergone the most rapid development in recent years. Above all, it provides a unifying theoretical framework for applications of visual pattern analysis. This unique textbook/reference provides a comprehensive overview of theories, methodologies, and recent developments in the field of statistical learning and statistical analysis for visual pattern modeling and computing. The book describes the solid theoretical foundation, provides a complete summary of the latest advances, and presents typical issues to be considered in making a real system for visual information processing. Features: • Provides a broad survey of recent advances in statistical learning and pattern analysis with respect to the two principal problems of representation and computation in visual computing • Presents the fundamentals of statistical pattern recognition and statistical learning via the general framework of a statistical pattern recognition system • Discusses pattern representation and classification, as well as concepts involved in supervised learning, semi-statistical learning, and unsupervised learning • Introduces the supervised learning of visual patterns in images, with a focus on supervised statistical pattern analysis, feature extraction and selection, and classifier design • Covers visual pattern analysis in video, including methodologies for building intelligent video analysis systems, critical aspects of motion analysis, and multi-target tracking formulation for video • Includes an in-depth discussion of information processing in the cognitive process, embracing a new scheme of association memory and a new architecture for an artificial intelligent system with attractors of chaos This complete guide to developing intelligent visual information processing systems is rich in examples, and will provide researchers and graduate students in computer vision and pattern recognition with a self-contained, invaluable and useful resource on the topic.