Identificacion Principal del registro INGC-EBK-000757

Control Number Identifier AR-LpUFI

LAST MODIFICATION DATE 20160916110025

CONTROL FIELD cr nn 008mamaa

CONTROL FIELD 140407s2014 gw | s |||| 0|eng d

a International Standard Book Number 9783642548512

a Standard number or code 10.1007/978-3-642-54851-2

a Title Subspace Methods for Pattern Recognition in Intelligent Environment

h Medium [libro electrónico] /

c Statement of responsibility, etc edited by Yen-Wei Chen, Lakhmi C. Jain.

a Place of publication, distribution, etc Berlin, Heidelberg :

b Name of publisher, distributor, etc Springer Berlin Heidelberg :

b Name of publisher, distributor, etc Imprint: Springer,

c Date of publication, distribution, etc 2014.

a Extent xvi, 199 p. :

b Other physical details il.

a Series statement Studies in Computational Intelligence,

x International Standard Serial Number 1860-949X ;

v Volume number/sequential designation 552

a Formatted contents note 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.

a Summary, etc 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.

a Topical term or geographic name as entry element Applied mathematics.

a Topical term or geographic name as entry element Engineering mathematics.

a Topical term or geographic name as entry element Engineering.

a Topical term or geographic name as entry element Computational Methods of Engineering.

a Topical term or geographic name as entry element Artificial Intelligence (incl. Robotics).

a Topical term or geographic name as entry element Pattern Recognition.

a Personal name Chen, Yen-Wei,

e Relator term ed.

a Personal name C. Jain, Lakhmi,

e Relator term ed.

u Uniform Resource Identifier (R) http://dx.doi.org/10.1007/978-3-642-54851-2

c item type EBK

a descripción COM