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  • © 2014

Subspace Methods for Pattern Recognition in Intelligent Environment

  • Latest research on the theoretical foundations and applications of subspace methods for pattern recognition using intelligent techniques

Part of the book series: Studies in Computational Intelligence (SCI, volume 552)

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  • ISBN: 978-3-642-54851-2
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Table of contents (8 chapters)

  1. Front Matter

    Pages 1-14
  2. Condition Relaxation in Conditional Statistical Shape Models

    • Elco Oost, Sho Tomoshige, Akinobu Shimizu
    Pages 33-56
  3. Subspace Construction from Artificially Generated Images for Traffic Sign Recognition

    • Hiroyuki Ishida, Ichiro Ide, Hiroshi Murase
    Pages 83-104
  4. Sparse Representation for Image Super-Resolution

    • Xian-Hua Han, Yen-Wei Chen
    Pages 123-150
  5. Tensor-Based Subspace Learning for Multi-pose Face Synthesis

    • Xu Qiao, Takanori Igarashi, Yen-Wei Chen
    Pages 171-195
  6. Back Matter

    Pages 197-199

About this book

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.

Keywords

  • Computational Intelligence
  • Knowledge-Based Techniques
  • Pattern Recognition
  • Subspace Methods

Editors and Affiliations

  • Ritsumeikan University College of Science & Engineering, Kusuatsu, Shiga, Japan

    Yen-Wei Chen

  • Faculty of Education, Science, Technology & Mathematics, University of Canberra, Canberra, Australia

    Lakhmi C. Jain

Bibliographic Information

Buying options

eBook USD 99.00
Price excludes VAT (USA)
  • ISBN: 978-3-642-54851-2
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book USD 169.99
Price excludes VAT (USA)