» » Information Theory in Computer Vision and Pattern Recognition

epub Information Theory in Computer Vision and Pattern Recognition download

by Francisco Escolano Ruiz,Pablo Suau Pérez,Boyán Ivanov Bonev,Alan L. Yuille

  • ISBN: 1848822960
  • Author: Francisco Escolano Ruiz,Pablo Suau Pérez,Boyán Ivanov Bonev,Alan L. Yuille
  • ePub ver: 1470 kb
  • Fb2 ver: 1470 kb
  • Rating: 4.1 of 5
  • Language: English
  • Pages: 364
  • Publisher: Springer; 2009 edition (July 24, 2009)
  • Formats: lit mbr docx lrf
  • Category: IT
  • Subcategory: Computer Science
epub Information Theory in Computer Vision and Pattern Recognition download

Information theory has proved to be effective for solving many computer vision and pattern recognition (CVPR) .

Information theory has proved to be effective for solving many computer vision and pattern recognition (CVPR) problems (such as image matching, clustering and segmentation, saliency detection, feature selection, optimal classifier design and many others). This book explores and introduces the latter elements through an incremental complexity approach at the same time where CVPR problems are formulated and the most representative algorithms are presented.

Автор: Francisco Escolano; Pablo Suau; Boyan Bonev Название: Information Theory in. .

The book is divided into five parts: basic methods in pattern recognition, basic methods in computer vision and image processing, recognition applications, life science and human identification, and systems and technology.

Foreword by Alan Yuille Information Theory (IT) can be highly . Foreword by Alan Yuille. Book · January 2009 with 181 Reads. Information theory has found widespread use in modern computer vision, and provides one of the most powerful current paradigms in the field.

Foreword by Alan Yuille Information Theory (IT) can be highly effective for formulating and designing algorithmic solutions to many problems in Computer Vision and Pattern Recognition (CVPR)  . Cite this publication. University of Alicante.

Information theory has proved to be effective for solving many computer vision and pattern recognition (CVPR) problems .

Information theory has proved to be effective for solving many computer vision and pattern recognition (CVPR) problems (such as image matching.

Authors: Escolano Ruiz, Francisco, Suau Pérez, Pablo, Bonev, Boyán Ivanov. Contains a Foreword by Professor Alan Yuille. To date, though, there has been no text that focusses on the needs of the vision or pattern recognition practitioner who wishes to find a concise reference to the subject. This text elegantly fills this gap in the literature.

Francisco Escolano (author), Pablo Suau (author), Boyan Bonev (author), Alan L. Yuille (foreword). Information theory has proved to be effective for solving many computer vision and pattern recognition (CVPR) problems (such as image matching, clustering and segmentation, saliency detection, feature selection, optimal classifier design and many others).

Start by marking Information Theory in Computer Vision and Pattern .

Start by marking Information Theory in Computer Vision and Pattern Recognition as Want to Read: Want to Read savin. ant to Read.

Finding books BookSee BookSee - Download books for free. Information Theory in Computer Vision and Pattern Recognition. Francisco Escolano Ruiz, Pablo Suau Pérez, Boyán Ivanov Bonev, Alan L. Yuille.

Francisco Escolano, Pablo Suau, Boyán Bonev.

Information theory has proved to be effective for solving many computer visionand pattern recognition (CVPR) problems (such as image matching, clustering and segmentation, saliency detection, feature selection, optimal classifier design and many others). Nowadays, researchers are widely bringing information theory elements to the CVPR arena. Among these elements there are measures (entropy, mutual information…), principles (maximum entropy, minimax entropy…) and theories (rate distortion theory, method of types…).

This book explores and introduces the latter elements through an incremental complexity approach at the same time where CVPR problems are formulated and the most representative algorithms are presented. Interesting connections between information theory principles when applied to different problems are highlighted, seeking a comprehensive research roadmap. The result is a novel tool both for CVPR and machine learning researchers, and contributes to a cross-fertilization of both areas.


Related to Information Theory in Computer Vision and Pattern Recognition: