Loading...
Image understanding using sparse representations /
Image understanding has been playing an increasingly crucial role in several inverse problems and computer vision. Sparse models form an important component in image understanding, since they emulate the activity of neural receptors in the primary visual cortex of the human brain. Sparse methods hav...
| Main Authors: | , , , |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
San Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) :
Morgan & Claypool,
2014.
|
| Series: | Synthesis digital library of engineering and computer science.
Synthesis lectures on image, video, and multimedia processing ; # 15. |
| Subjects: | |
| Online Access: | Abstract with links to full text |
| Summary: | Image understanding has been playing an increasingly crucial role in several inverse problems and computer vision. Sparse models form an important component in image understanding, since they emulate the activity of neural receptors in the primary visual cortex of the human brain. Sparse methods have been utilized in several learning problems because of their ability to provide parsimonious, interpretable, and efficient models. Exploiting the sparsity of natural signals has led to advances in several application areas including image compression, denoising, inpainting, compressed sensing, blind source separation, super-resolution, and classification. |
|---|---|
| Item Description: | Part of: Synthesis digital library of engineering and computer science. Series from website. |
| Physical Description: | 1 PDF (xi, 106 pages) : illustrations. Also available in print. |
| Format: | Mode of access: World Wide Web. System requirements: Adobe Acrobat Reader. |
| Bibliography: | Includes bibliographical references (pages 91-104). |
| ISBN: | 9781627053600 |
| ISSN: | 1559-8144 ; |
| Access: | Abstract freely available; full-text restricted to subscribers or individual document purchasers. |