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Activity Number:
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309
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Type:
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Invited
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Date/Time:
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Tuesday, July 31, 2007 : 2:00 PM to 3:50 PM
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Sponsor:
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Technometrics
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| Abstract - #307955 |
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Title:
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Statistical Principles in Image Modeling
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Author(s):
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Ying Nian Wu*+ and Jinhui Li and Ziqiang Liu and Song-Chun Zhu
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Companies:
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University of California, Los Angeles and University of California, Los Angeles and University of California, Los Angeles and University of California, Los Angeles
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Address:
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Department of Statistics, Los Angeles, CA, 90095,
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Keywords:
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Sparse coding ; Markov random fields ; Meaningful alignment
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Abstract:
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Images of natural scenes contain rich variety of visual patterns. In order to learn and recognize these patterns from natural images, it is necessary to construct statistical models for these patterns. In this article, we review three statistical principles for modeling image patterns, namely, the sparse coding principle, the minimax entropy principle, and the meaningful alignment principle. We examine these three principles and their relationships in the context of modeling images as compositions of Gabor wavelets. We show that these three principles correspond to three regimes of composition patterns of Gabor wavelets, and these three regimes are connected by the change of scale or resolution.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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