9781461356530 - face image analysis by unsupervised learning: 612 di bartlett, marian stewart stewart (10 risultati)
Lingua: Inglese
Editore: Springer, 2012
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Lingua: Inglese
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Condizione: New. pp. 192.
Lingua: Inglese
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Serie: Libro 169 di 260 - The Springer International Series in Engineering and Computer Science
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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 tradit…ional 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.
Lingua: Inglese
Editore: Springer, 2012
Serie: Libro 169 di 260 - The Springer International Series in Engineering and Computer Science
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Editore: Springer US, Springer New York Okt 2012, 2012
Serie: Libro 169 di 260 - The Springer International Series in Engineering and Computer Science
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 contras…t 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. 192 pp. Englisch.
Lingua: Inglese
Editore: Springer US, 2012
Serie: Libro 169 di 260 - The Springer International Series in Engineering and Computer Science
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 environm…ent. In contrast to more trad.
Lingua: Inglese
Editore: Springer, 2012
Serie: Libro 169 di 260 - The Springer International Series in Engineering and Computer Science
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Condizione: New. Print on Demand pp. 192 49:B&W 6.14 x 9.21 in or 234 x 156 mm (Royal 8vo) Perfect Bound on White w/Gloss Lam.
Lingua: Inglese
Editore: Springer, 2012
Serie: Libro 169 di 260 - The Springer International Series in Engineering and Computer Science
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Condizione: New. PRINT ON DEMAND pp. 192.
Lingua: Inglese
Editore: Springer, Springer Okt 2012, 2012
Serie: Libro 169 di 260 - The Springer International Series in Engineering and Computer Science
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 192 pp. Englisch.
