Sparse Representation for Computer Vision and Pattern Recognition

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 di...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Proceedings of the IEEE Ročník 98; číslo 6; s. 1031 - 1044
Hlavní autoři: Wright, John, Ma, Yi, Mairal, Julien, Sapiro, Guillermo, Huang, Thomas S., Yan, Shuicheng
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.06.2010
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithm design and analysis
Algorithms
Application software
Asia
Compressed sensing
Computer vision
Data mining
Dictionaries
Face recognition
Mathematical analysis
Pattern recognition
Representations
Semantics
Signal processing algorithms
Signal representations
State of the art
ISSN:0018-9219, 1558-2256
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-1
ObjectType-Feature-2
content type line 23
ISSN:0018-9219
1558-2256
DOI:10.1109/JPROC.2010.2044470