Face Image Analysis by Unsupervised Learning:
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...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Springer US
2001
|
| Schriftenreihe: | The Springer International Series in Engineering and Computer Science
612 |
| Schlagworte: | |
| Online-Zugang: | FHI01 BTU01 URL des Erstveröffentlichers |
| Zusammenfassung: | 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 |
| Beschreibung: | 1 Online-Ressource (XV, 173 p) |
| ISBN: | 9781461516378 |
| DOI: | 10.1007/978-1-4615-1637-8 |
Internformat
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Datensatz im Suchindex
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| author | Bartlett, Marian Stewart |
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| dewey-hundreds | 000 - Computer science, information, general works |
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| discipline | Informatik |
| doi_str_mv | 10.1007/978-1-4615-1637-8 |
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| id | DE-604.BV045148934 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:10:02Z |
| institution | BVB |
| isbn | 9781461516378 |
| language | English |
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| physical | 1 Online-Ressource (XV, 173 p) |
| psigel | ZDB-2-ENG ZDB-2-ENG_2000/2004 ZDB-2-ENG ZDB-2-ENG_2000/2004 ZDB-2-ENG ZDB-2-ENG_Archiv |
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| publisher | Springer US |
| record_format | marc |
| series2 | The Springer International Series in Engineering and Computer Science |
| spelling | Bartlett, Marian Stewart Verfasser aut Face Image Analysis by Unsupervised Learning by Marian Stewart Bartlett Boston, MA Springer US 2001 1 Online-Ressource (XV, 173 p) txt rdacontent c rdamedia cr rdacarrier The Springer International Series in Engineering and Computer Science 612 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 Computer Science User Interfaces and Human Computer Interaction Computer Imaging, Vision, Pattern Recognition and Graphics Artificial Intelligence (incl. Robotics) Statistics for Life Sciences, Medicine, Health Sciences Control, Robotics, Mechatronics Theory of Computation Computer science Computers User interfaces (Computer systems) Artificial intelligence Computer graphics Statistics Control engineering Robotics Mechatronics Gesicht (DE-588)4020687-7 gnd rswk-swf Mustererkennung (DE-588)4040936-3 gnd rswk-swf Gesicht (DE-588)4020687-7 s Mustererkennung (DE-588)4040936-3 s 1\p DE-604 Erscheint auch als Druck-Ausgabe 9781461356530 https://doi.org/10.1007/978-1-4615-1637-8 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Bartlett, Marian Stewart Face Image Analysis by Unsupervised Learning Computer Science User Interfaces and Human Computer Interaction Computer Imaging, Vision, Pattern Recognition and Graphics Artificial Intelligence (incl. Robotics) Statistics for Life Sciences, Medicine, Health Sciences Control, Robotics, Mechatronics Theory of Computation Computer science Computers User interfaces (Computer systems) Artificial intelligence Computer graphics Statistics Control engineering Robotics Mechatronics Gesicht (DE-588)4020687-7 gnd Mustererkennung (DE-588)4040936-3 gnd |
| subject_GND | (DE-588)4020687-7 (DE-588)4040936-3 |
| title | Face Image Analysis by Unsupervised Learning |
| title_auth | Face Image Analysis by Unsupervised Learning |
| title_exact_search | Face Image Analysis by Unsupervised Learning |
| title_full | Face Image Analysis by Unsupervised Learning by Marian Stewart Bartlett |
| title_fullStr | Face Image Analysis by Unsupervised Learning by Marian Stewart Bartlett |
| title_full_unstemmed | Face Image Analysis by Unsupervised Learning by Marian Stewart Bartlett |
| title_short | Face Image Analysis by Unsupervised Learning |
| title_sort | face image analysis by unsupervised learning |
| topic | Computer Science User Interfaces and Human Computer Interaction Computer Imaging, Vision, Pattern Recognition and Graphics Artificial Intelligence (incl. Robotics) Statistics for Life Sciences, Medicine, Health Sciences Control, Robotics, Mechatronics Theory of Computation Computer science Computers User interfaces (Computer systems) Artificial intelligence Computer graphics Statistics Control engineering Robotics Mechatronics Gesicht (DE-588)4020687-7 gnd Mustererkennung (DE-588)4040936-3 gnd |
| topic_facet | Computer Science User Interfaces and Human Computer Interaction Computer Imaging, Vision, Pattern Recognition and Graphics Artificial Intelligence (incl. Robotics) Statistics for Life Sciences, Medicine, Health Sciences Control, Robotics, Mechatronics Theory of Computation Computer science Computers User interfaces (Computer systems) Artificial intelligence Computer graphics Statistics Control engineering Robotics Mechatronics Gesicht Mustererkennung |
| url | https://doi.org/10.1007/978-1-4615-1637-8 |
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