| 書目名稱 | Face Image Analysis by Unsupervised Learning |
| 編輯 | Marian Stewart Bartlett |
| 視頻video | http://file.papertrans.cn/341/340194/340194.mp4 |
| 叢書名稱 | The Springer International Series in Engineering and Computer Science |
| 圖書封面 |  |
| 描述 | .Face Image Analysis by Unsupervised Learning. exploresadaptive approaches to image analysis. It draws upon principles ofunsupervised learning and information theory to adapt processing tothe immediate task environment. In contrast to more traditionalapproaches to image analysis in which relevant structure is determinedin advance and extracted using hand-engineered techniques, .FaceImage Analysis by. .Unsupervised Learning. explores methods thathave roots in biological vision and/or learn about the image structuredirectly from the image ensemble. Particular attention is paid tounsupervised learning techniques for encoding the statisticaldependencies in the image ensemble. .The first part of this volume reviews unsupervised learning,information theory, independent component analysis, and their relationto biological vision. Next, a face image representation usingindependent component analysis (ICA) is developed, which is anunsupervised learning technique based on optimal information transferbetween neurons. The ICA representation is compared to a number ofother face representations including eigenfaces and Gabor wavelets ontasks of identity recognition and expression analysis. Finall |
| 出版日期 | Book 2001 |
| 關(guān)鍵詞 | coding; cognition; image analysis; information; information theory; learning; presentation; supervised lear |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-1-4615-1637-8 |
| isbn_softcover | 978-1-4613-5653-0 |
| isbn_ebook | 978-1-4615-1637-8Series ISSN 0893-3405 |
| issn_series | 0893-3405 |
| copyright | Springer Science+Business Media New York 2001 |
|Archiver|手機(jī)版|小黑屋|
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