Sparse Representation, Modeling and Learning in Visual Recognition

Theory, Algorithms and Applications

de

Éditeur :

Springer


Collection :

Advances in Computer Vision and Pattern Recognition

Paru le : 2015-05-25



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Description
This unique text/reference presents a comprehensive review of the state of the art in sparse representations, modeling and learning. The book examines both the theoretical foundations and details of algorithm implementation, highlighting the practical application of compressed sensing research in visual recognition and computer vision. Topics and features: describes sparse recovery approaches, robust and efficient sparse representation, and large-scale visual recognition; covers feature representation and learning, sparsity induced similarity, and sparse representation and learning-based classifiers; discusses low-rank matrix approximation, graphical models in compressed sensing, collaborative representation-based classification, and high-dimensional nonlinear learning; includes appendices outlining additional computer programming resources, and explaining the essential mathematics required to understand the book.
Pages
257 pages
Collection
Advances in Computer Vision and Pattern Recognition
Parution
2015-05-25
Marque
Springer
EAN papier
9781447167136
EAN PDF
9781447167143

Informations sur l'ebook
Nombre pages copiables
2
Nombre pages imprimables
25
Taille du fichier
8212 Ko
Prix
94,94 €
EAN EPUB
9781447167143

Informations sur l'ebook
Nombre pages copiables
2
Nombre pages imprimables
25
Taille du fichier
5069 Ko
Prix
94,94 €