000 03412nam a22005295i 4500
001 978-3-642-54851-2
003 DE-He213
005 20200420220229.0
007 cr nn 008mamaa
008 140407s2014 gw | s |||| 0|eng d
020 _a9783642548512
_9978-3-642-54851-2
024 7 _a10.1007/978-3-642-54851-2
_2doi
050 4 _aTA329-348
050 4 _aTA640-643
072 7 _aTBJ
_2bicssc
072 7 _aMAT003000
_2bisacsh
082 0 4 _a519
_223
245 1 0 _aSubspace Methods for Pattern Recognition in Intelligent Environment
_h[electronic resource] /
_cedited by Yen-Wei Chen, Lakhmi C. Jain.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2014.
300 _aXVI, 199 p. 99 illus., 52 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-949X ;
_v552
505 0 _aActive 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.
520 _aThis 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.
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aPattern recognition.
650 0 _aApplied mathematics.
650 0 _aEngineering mathematics.
650 1 4 _aEngineering.
650 2 4 _aAppl.Mathematics/Computational Methods of Engineering.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aPattern Recognition.
700 1 _aChen, Yen-Wei.
_eeditor.
700 1 _aC. Jain, Lakhmi.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642548505
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v552
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-54851-2
912 _aZDB-2-ENG
942 _cEBK
999 _c52362
_d52362