Please use this identifier to cite or link to this item: http://dl.pgu.ac.ir/handle/Hannan/9919
Title: Subspace Methods for Pattern Recognition in Intelligent Environment
Authors: Chen, Yen-Wei
C J Ain, L Akhmi
Keywords: Engineering;Artificial intelligence;Optical pattern recognition;Engineering mathematics;Engineering;Appl Mathematics/Computational Methods of Engineering;Artificial Intelligence;Pattern Recognition;519 23;TA640-643
Issue Date: 2014
Publisher: Springer Berlin Heidelberg;Imprint Springer,
place: Berlin, Heidelberg
Series/Report no.: Studies in Computational Intelligence,;1860-949X ;;552.;Studies in Computational Intelligence,;1860-949X ;;552.
Abstract: This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. With the fast development of internet and computer technologies, the amount of available data is rapidly increasing in our daily life. How to extract core information or useful features is an important issue. Subspace methods are widely used for dimension reduction and feature extraction in pattern recognition. They transform a high-dimensional data to a lower-dimensional space (subspace), where most information is retained. The book covers a broad spectrum of subspace methods including linear, nonlinear and multilinear subspace learning methods and applications. The applications include face alignment, face recognition, medical image analysis, remote sensing image classification, traffic sign recognition, image clustering, super resolution, edge detection, multi-view facial image synthesis.
Description: Printed edition:;9783642548505.
Table Of Contents: Active Shape Model and Its Application to Face Alignment -- Condition Relaxation in Conditional Statistical Shape Models -- Independent Component Analysis and Its Application to Classification of High-Resolution Remote Sensing Images -- Subspace Construction from Artificially Generated Images for Traffic Sign Recognition -- Local Structure Preserving based Subspace Analysis Methods and Applications -- Sparse Representation for Image Super-Resolution -- Sampling and Recovery of Continuously-Defined Sparse Signals and Its Applications -- Tensor-Based Subspace Learning for Multi-Pose Face Synthesis.
URI: http://dl.pgu.ac.ir/handle/Hannan/9919
ISBN: 9783642548512
9783642548505 (print)
Format: [electronic resource] /
Type Of Material: Book
Appears in Collections:Mathematics

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