Description
Springer Statistical Learning And Pattern Analysis For Image And Video Processing 2010 Edition by Nanning Zheng Jianru Xue
Why are We Writing This Book? Visual data (graphical image video and visualized data) affect every aspect of modern society. The cheap collection storage and transmission of vast amounts of visual data have revolutionized the practice of science technology and business. Innovations from various disciplines have been developed and applied to the task of designing intelligent machines that can automatically detect and exploit useful regularities (patterns) in visual data. One such approach to machine intelligence is statistical learning and pattern analysis for visual data. Over the past two decades rapid advances have been made throughout the ?eld of visual pattern analysis. Some fundamental problems including perceptual gro- ingimagesegmentation stereomatching objectdetectionandrecognitionand- tion analysis and visual tracking have become hot research topics and test beds in multiple areas of specialization including mathematics neuron-biometry and c- nition. A great diversity of models and algorithms stemming from these disciplines has been proposed. To address the issues of ill-posed problems and uncertainties in visual pattern modeling and computing researchers have developed rich toolkits based on pattern analysis theory harmonic analysis and partial differential eq- tions geometry and group theory graph matching and graph grammars. Among these technologies involved in intelligent visual information processing statistical learning and pattern analysis is undoubtedly the most popular and imp- tant approach and it is also one of the most rapidly developing ?elds with many achievements in recent years. Above all it provides a unifying theoretical fra- work for intelligent visual information processing applications. Table of contents : 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.