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Item Details
| Title:
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SPARSE MODELING FOR IMAGE AND VISION PROCESSING
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| By: |
Julien Mairal, Francis Bach, Jean Ponce |
| Format: |
Paperback |
| List price:
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£63.00 |
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We currently do not stock this item, please contact the publisher directly for
further information.
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| ISBN 10: |
1680830082 |
| ISBN 13: |
9781680830088 |
| Publisher: |
NOW PUBLISHERS INC |
| Pub. date: |
19 December, 2014 |
| Series: |
Foundations and Trends in Computer Graphics and Vision |
| Pages: |
216 |
| Description: |
In recent years, a large amount of multi-disciplinary research has been conducted on sparse models and their applications. This monograph intends to offer a self-contained view of sparse modeling for visual recognition and image processing. |
| Synopsis: |
In recent years, a large amount of multi-disciplinary research has been conducted on sparse models and their applications. In statistics and machine learning, the sparsity principle is used to perform model selection-that is, automatically selecting a simple model among a large collection of them. In signal processing, sparse coding consists of representing data with linear combinations of a few dictionary elements. Subsequently, the corresponding tools have been widely adopted by several scientific communities such as neuroscience, bioinformatics, or computer vision. The goal of this monograph is to offer a self-contained view of sparse modeling for visual recognition and image processing. More specifically, it focuses on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts. |
| Publication: |
US |
| Imprint: |
now publishers Inc |
| Returns: |
Non-returnable |
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