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Subspace methods for pattern recognition in intelligent environment [electronic resource]

Subspace methods for pattern recognition in intelligent environment [electronic resource]

Material type
E-Book(소장)
Personal Author
Chen, Yen-Wei. Jain, L. C.
Title Statement
Subspace methods for pattern recognition in intelligent environment [electronic resource] / Yen-Wei Chen, Lakhmi C. Jain, editors.
Publication, Distribution, etc
Berlin;   Heidelberg :   Springer Berlin Heidelberg :   Imprint: Springer,   2014.  
Physical Medium
1 online resource (xvi, 199 p.) : ill.
Series Statement
Studies in computational intelligence,1860-949X ; 552
ISBN
9783642548512
요약
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.
General Note
Title from e-Book title page.  
Content Notes
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.
Bibliography, Etc. Note
Includes bibliographical references and index.
이용가능한 다른형태자료
Issued also as a book.  
Subject Added Entry-Topical Term
Pattern recognition systems --Mathematical models. Computer vision.
Short cut
URL
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020 ▼a 9783642548512
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050 4 ▼a TA329-348
082 0 4 ▼a 006.4 ▼2 23
084 ▼a 006.4 ▼2 DDCK
090 ▼a 006.4
245 0 0 ▼a Subspace methods for pattern recognition in intelligent environment ▼h [electronic resource] / ▼c Yen-Wei Chen, Lakhmi C. Jain, editors.
260 ▼a Berlin; ▼a Heidelberg : ▼b Springer Berlin Heidelberg : ▼b Imprint: Springer, ▼c 2014.
300 ▼a 1 online resource (xvi, 199 p.) : ▼b ill.
490 1 ▼a Studies in computational intelligence, ▼x 1860-949X ; ▼v 552
500 ▼a Title from e-Book title page.
504 ▼a Includes bibliographical references and index.
505 0 ▼a 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.
520 ▼a 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.
530 ▼a Issued also as a book.
538 ▼a Mode of access: World Wide Web.
650 0 ▼a Pattern recognition systems ▼x Mathematical models.
650 0 ▼a Computer vision.
700 1 ▼a Chen, Yen-Wei.
700 1 ▼a Jain, L. C.
830 0 ▼a Studies in computational intelligence; ▼v 552.
856 4 0 ▼u https://oca.korea.ac.kr/link.n2s?url=http://dx.doi.org/10.1007/978-3-642-54851-2
945 ▼a KLPA
991 ▼a E-Book(소장)

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Main Library/e-Book Collection/ Call Number CR 006.4 Accession No. E14034765 Availability Processing Due Date Make a Reservation Service M

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