Descripción del título
Subspace Methods for Patter...
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
Monografía
monografia Rebiun36416388 https://catalogo.rebiun.org/rebiun/record/Rebiun36416388 m o d | cr nn 008mamaa 140407s2014 gw s 00 0 eng d 9783642548512 9783642548505 9783642548529 9783662501900 10.1007/978-3-642-54851-2 doi UMA.RE eng TBJ bicssc MAT003000 bisacsh 519 23 Subspace Methods for Pattern Recognition in Intelligent Environment Recurso electrónico] edited by Yen-Wei Chen, Lakhmi C. Jain Berlin, Heidelberg Springer Berlin Heidelberg 2014 Berlin, Heidelberg Berlin, Heidelberg Springer Berlin Heidelberg Berlin, Heidelberg Springer Berlin Heidelberg Imprint: Springer 2014 Berlin, Heidelberg Berlin, Heidelberg Springer Berlin Heidelberg Imprint: Springer XVI, 199 p. 99 il., 52 il. col XVI, 199 p. 99 il., 52 il. col Text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Studies in Computational Intelligence 552 Bibliographic Level Mode of Issuance: Monograph Includes bibliographical references 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 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 English Engineering mathematics Artificial intelligence Optical pattern recognition Mathematical and Computational Engineering. Artificial Intelligence. Pattern Recognition. Engineering mathematics Artificial intelligence Optical pattern recognition Mathematical and Computational Engineering Artificial Intelligence Pattern Recognition Chen, Yen-Wei ed. lit C. Jain, Lakhmi ed. lit 3-642-54850-4 Studies in Computational Intelligence 552