FREE delivery to all EXCLUSIVE BOOKS stores nationwide. FREE delivery to your door on all orders over R450. Excludes all international deliveries.

Face Image Analysis by Unsupervised Learning

Marian Stewart Bartlett
    Product form
      FORMAT: Paperback / softback

      R 3,020.27 Price and availability exclusive to website

      YOU COULD EARN 3,020.27 FUTURE RETAIL DISCOUNTS.
      ESTIMATED DELIVERY: Approx. 10 - 15 Business Days
      BUY NOW PAY LATER
      From R 503.37 per month!
      3x monthly payments of R 1,006.75 with
      4x fortnightly payments of R 755.06 with
      Face Image Analysis by Unsupervised Learning explores adaptive approaches to image analysis. It draws upon principles of unsupervised learning and information theory to adapt processing to the immediate task environment. In contrast to more traditional approaches to image analysis in which relevant structure is determined in advance and extracted using hand-engineered techniques, Face Image Analysis by Unsupervised Learning explores methods that have roots in biological vision and/or learn about the image structure directly from the image ensemble. Particular attention is paid to unsupervised learning techniques for encoding the statistical dependencies in the image ensemble. The first part of this volume reviews unsupervised learning, information theory, independent component analysis, and their relation to biological vision. Next, a face image representation using independent component analysis (ICA) is developed, which is an unsupervised learning technique based on optimal information transfer between neurons. The ICA representation is compared to a number of other face representations including eigenfaces and Gabor wavelets on tasks of identity recognition and expression analysis. Finally, methods for learning features that are robust to changes in viewpoint and lighting are presented. These studies provide evidence that encoding input dependencies through unsupervised learning is an effective strategy for face recognition. Face Image Analysis by Unsupervised Learning is suitable as a secondary text for a graduate-level course, and as a reference for researchers and practitioners in industry.
      Format: Paperback / softback CONTRIBUTORS: Marian Stewart Bartlett EAN: 9781461356530 COUNTRY: United States PAGES: WEIGHT: 302 g HEIGHT: 235 cm
      PUBLISHED BY: Springer-Verlag New York Inc. DATE PUBLISHED: 2012-10-26 CITY: GENRE: COMPUTERS / Artificial Intelligence / General, COMPUTERS / Artificial Intelligence / Computer Vision & Pattern Recognition, COMPUTERS / User Interfaces, MEDICAL / Biostatistics, TECHNOLOGY & ENGINEERING / Automation WIDTH: 155 cm SPINE:

      Book Themes:

      Probability and statistics, Automatic control engineering, Mathematical theory of computation, Artificial intelligence, Image processing, Human–computer interaction

      Customer Reviews

      Be the first to write a review
      0%
      (0)
      0%
      (0)
      0%
      (0)
      0%
      (0)
      0%
      (0)
      Face Image Analysis by Unsupervised Learning explores adaptive approaches to image analysis. It draws upon principles of unsupervised learning and information theory to adapt processing to the immediate task environment. In contrast to more traditional approaches to image analysis in which relevant structure is determined in advance and extracted using hand-engineered techniques, Face Image Analysis by Unsupervised Learning explores methods that have roots in biological vision and/or learn about the image structure directly from the image ensemble. Particular attention is paid to unsupervised learning techniques for encoding the statistical dependencies in the image ensemble. The first part of this volume reviews unsupervised learning, information theory, independent component analysis, and their relation to biological vision. Next, a face image representation using independent component analysis (ICA) is developed, which is an unsupervised learning technique based on optimal information transfer between neurons. The ICA representation is compared to a number of other face representations including eigenfaces and Gabor wavelets on tasks of identity recognition and expression analysis. Finally, methods for learning features that are robust to changes in viewpoint and lighting are presented. These studies provide evidence that encoding input dependencies through unsupervised learning is an effective strategy for face recognition. Face Image Analysis by Unsupervised Learning is suitable as a secondary text for a graduate-level course, and as a reference for researchers and practitioners in industry.
      Format: Paperback / softback CONTRIBUTORS: Marian Stewart Bartlett EAN: 9781461356530 COUNTRY: United States PAGES: WEIGHT: 302 g HEIGHT: 235 cm
      PUBLISHED BY: Springer-Verlag New York Inc. DATE PUBLISHED: 2012-10-26 CITY: GENRE: COMPUTERS / Artificial Intelligence / General, COMPUTERS / Artificial Intelligence / Computer Vision & Pattern Recognition, COMPUTERS / User Interfaces, MEDICAL / Biostatistics, TECHNOLOGY & ENGINEERING / Automation WIDTH: 155 cm SPINE:

      Book Themes:

      Probability and statistics, Automatic control engineering, Mathematical theory of computation, Artificial intelligence, Image processing, Human–computer interaction

      Customer Reviews

      Be the first to write a review
      0%
      (0)
      0%
      (0)
      0%
      (0)
      0%
      (0)
      0%
      (0)

      Recently viewed products

      Login

      Forgot your password?

      Don't have an account yet?
      Create account