Statistical learning and pattern analysis for image and video processing
著者
書誌事項
Statistical learning and pattern analysis for image and video processing
（Advances in pattern recognition）
Springer, c2009
- : hbk.
大学図書館所蔵 全4件
内容説明・目次
内容説明
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- ing,imagesegmentation, stereomatching, objectdetectionandrecognition,and- 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.
目次
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.
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