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| November 3
| November 3
| Abhishek Sharma
| Abhishek Sharma
| Cross-modal classification and retrieval: Techniques and challenges
| Cross-Modal Classification and Retrieval: Techniques and Challenges
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|-
| November 10
| November 10
Visual tracking is a classical computer vision task. However, the ubiquity of modern sensors makes it more difficult due to the large amount of data available for processing. The emerging theory of compressive sensing has the potential to address this problem in that it promises the ability to reduce the amount of data collected without sacrificing the amount of information within. In this talk, I will review recent research research toward the adaptation of some computer vision algorithms commonly used in visual tracking such that they can operate in the lower-dimensional compressive domain. Specifically, background subtraction and particle filtering will be discussed.
Visual tracking is a classical computer vision task. However, the ubiquity of modern sensors makes it more difficult due to the large amount of data available for processing. The emerging theory of compressive sensing has the potential to address this problem in that it promises the ability to reduce the amount of data collected without sacrificing the amount of information within. In this talk, I will review recent research research toward the adaptation of some computer vision algorithms commonly used in visual tracking such that they can operate in the lower-dimensional compressive domain. Specifically, background subtraction and particle filtering will be discussed.
===Cross-modal classification and retrieval: Techniques and challenges===
===Cross-Modal Classification and Retrieval: Techniques and Challenges===
Speaker: [http://www.cs.umd.edu/~bhokaal/ Abhishek Sharma] -- Date: November 3, 2011
Speaker: [http://www.cs.umd.edu/~bhokaal/ Abhishek Sharma] -- Date: November 3, 2011