Titlebook: Statistical Learning and Pattern Analysis for Image and Video Processing; Nanning Zheng,Jianru Xue Book 2009 Springer-Verlag London 2009 M

mastoid-bone

Statistical Motion Analysis,fferent techniques for performing various motion tasks; these techniques can be subsumed within a statistical and geometrical framework. We choose to focus on statistical approaches to classifying motion patterns in an observed image sequence.

fluffy

可能性

Information Processing in Cognition Process and New Artificial Intelligent Systems,es that have emerged in the intersecting fields of cognitive science and information science. To this end, a new scheme for associative memory and a new architecture for an AI system with attractors of chaos are addressed.

Glycogen

Book 2009storage, 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 regulari

指数

人类的发源

Unsupervised Learning for Visual Pattern Analysis,ty are two important topics in unsupervised learning. Clustering relates to the grouping of similar objects in visual perception, while dimensionality reduction is essential for the compact representation of visual patterns. In this chapter, we focus on clustering techniques, offering first a theore

Osteons

fatty-streak

胰脏

Functional Approximation, the properties of the wavelet transform rather than on the entire encoder. These advances include enhancements to the construction of an adaptive wavelet transform that results in fewer wavelet coefficients and improvements in motion-compensated temporal filtering that achieve temporal scalability

kindred

Supervised Learning for Visual Pattern Classification,e., the support vector machine (SVM) and the boosting algorithm, are briefly introduced. SVMs and boosting algorithms are two hot topics of recent research in supervised learning. SVMs improve the generalization of the learning machine by implementing the rule of structural risk minimization (SRM).