Face Image Analysis by Unsupervised Learning
Marian Stewart Bartlett
dec. de 2012 · The Springer International Series in Engineering and Computer Science Libro 612 · Springer Science & Business Media
Libro electrónico
173
Páxinas
reportAs valoracións e as recensións non están verificadas Máis información
Acerca deste libro electrónico
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.
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.
Valora este libro electrónico
Dános a túa opinión.
Información de lectura
Smartphones e tabletas
Instala a aplicación Google Play Libros para Android e iPad/iPhone. Sincronízase automaticamente coa túa conta e permíteche ler contido en liña ou sen conexión desde calquera lugar.
Portátiles e ordenadores de escritorio
Podes escoitar os audiolibros comprados en Google Play a través do navegador web do ordenador.
Lectores de libros electrónicos e outros dispositivos
Para ler contido en dispositivos de tinta electrónica, como os lectores de libros electrónicos Kobo, é necesario descargar un ficheiro e transferilo ao dispositivo. Sigue as instrucións detalladas do Centro de Axuda para transferir ficheiros a lectores electrónicos admitidos.
