.png){kind=icon}
Return
Sparse Representation for Computer Vision and Pattern Recognition
.png){kind=icon}
OA
AI
DOI：10.1109/JPROC.2010.2044470.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Techniques from sparse signal representation are beginning to see significant impact in computer vision, often on nontraditional applications where the goal is not just to obtain a compact high-fidelity representation of the observed signal, but also to extract semantic information. The choice of dictionary plays a key role in bridging this gap: unconventional dictionaries consisting of, or learned from, the training samples themselves provide the key to obtaining state-of-the-art results and to attaching semantic meaning to sparse signal representations. Understanding the good performance of such unconventional dictionaries in turn demands new algorithmic and analytical techniques. This review paper highlights a few representative examples of how the interaction between sparse signal representation and computer vision can enrich both fields, and raises a number of open questions for further study.
Keywords:
Compressed sensing
computer vision
pattern recognition
signal representations
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
25.9
Papers:
9.9K
Citations:
4.5W
Organization
Cited Papers
ShareSaveShareSave
ShareSaveX-ray Structure of the R69D Phosphatidylinositol-Specific Phospholipase C Enzyme: Insight into the Role of Calcium and Surrounding Amino Acids in Active Site Geometry and Catalysis.png)
BiochemistryOAAI
BiochemistryIF0
OAAIShareSaveShareSaveShareSave
View more