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Item Details
| Title:
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MACHINE LEARNING IN COMPUTER VISION
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| By: |
Nicu Sebe, Ira J. Cohen, Ashutosh Garg |
| Format: |
Hardback |
| List price:
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£70.50 |
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We believe that this item is permanently unavailable, and so we cannot source
it.
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| ISBN 10: |
1402032749 |
| ISBN 13: |
9781402032745 |
| Publisher: |
SPRINGER-VERLAG NEW YORK INC. |
| Pub. date: |
3 June, 2005 |
| Series: |
Computational Imaging and Vision v. 29 |
| Pages: |
258 |
| Description: |
Addresses the use of important machine learning techniques into computer vision applications. This book deals with these aspects from a different perspective: that the key element in the computer revolution is the use of machine learning to capture the variations in visual appearance, rather than having the designer of the model accomplish this. |
| Synopsis: |
It started withimageprocessing inthesixties. Back then, it took ages to digitize a Landsat image and then process it with a mainframe computer. P- cessing was inspired on theachievements of signal processing and was still very much oriented towards programming. In the seventies, image analysis spun off combining image measurement with statistical pattern recognition. Slowly, computational methods detached themselves from the sensor and the goal to become more generally applicable. In theeighties, model-drivencomputervision originated when arti?cial- telligence and geometric modelling came together with image analysis com- nents. The emphasis was on precise analysiswithlittleorno interaction, still very much an art evaluated by visual appeal. The main bottleneck was in the amount of data using an average of 5 to 50 pictures to illustrate the point. At the beginning of the nineties, vision became available to many with the advent of suf?ciently fast PCs. The Internet revealed the interest of the g- eral public im images, eventually introducingcontent-basedimageretrieval.Combining independent (informal) archives, as the web is, urges for inter- tive evaluation of approximate results andhence weak algorithms and their combination in weak classi?ers. |
| Illustrations: |
biography |
| Publication: |
US |
| Imprint: |
Springer-Verlag New York Inc. |
| Returns: |
Non-returnable |
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