
Sparse Coding And Its Applications In Computer Vision
World Scientific Publishing Co Pte Ltd
Published on 14. December 2015
Book
Hardback
240 pages
978-981-4725-04-0 (ISBN)
Description
This book provides a broader introduction to the theories and applications of sparse coding techniques in computer vision research. It introduces sparse coding in the context of representation learning, illustrates the fundamental concepts, and summarizes the most active research directions. A variety of applications of sparse coding are discussed, ranging from low-level image processing tasks such as super-resolution and de-blurring to high-level semantic understanding tasks such as image recognition, clustering and fusion.The book is suitable to be used as an introductory overview to this field, with its theoretical part being both easy and precious enough for quick understanding. It is also of great value to experienced researchers as it offers new perspective to the underlying mechanism of sparse coding, and points out potential future directions for different applications.
More details
Language
English
Place of publication
Singapore
Singapore
Target group
College/higher education
Professional and scholarly
Dimensions
Height: 235 mm
Width: 157 mm
Thickness: 18 mm
Weight
504 gr
ISBN-13
978-981-4725-04-0 (9789814725040)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Author
Adobe Systems, Inc, Usa
Snapchat, Inc, Usa
Amazon.com, Inc, Usa
Univ Of Illinois At Urbana-champaign, Usa
Univ Of Illinois At Urbana-champaign, Usa
Univ Of Illinois At Urbana-champaign, Usa
Univ Of Illinois At Urbana-champaign, Usa
Content
Sparse Priors; Sparse Solvers; Dictionary Learning Algorithms; Image Super-Resolution with Coupled Dictionaries; Image De-Blurring with Sparse Regularization; Sensor Fusion with Joint Structured Sparsity; Clustering with L1-Graph; Discriminative Representation Learning; Hyper-Spectral Image Classification;