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231224s2014 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2014.2337758
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|a eng
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| 100 |
1 |
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|a Sun, Xiaoshuai
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Toward statistical modeling of saccadic eye-movement and visual saliency
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|c 2014
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| 336 |
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 29.09.2015
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|a Date Revised 30.09.2014
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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| 520 |
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|a In this paper, we present a unified statistical framework for modeling both saccadic eye movements and visual saliency. By analyzing the statistical properties of human eye fixations on natural images, we found that human attention is sparsely distributed and usually deployed to locations with abundant structural information. This observations inspired us to model saccadic behavior and visual saliency based on super-Gaussian component (SGC) analysis. Our model sequentially obtains SGC using projection pursuit, and generates eye movements by selecting the location with maximum SGC response. Besides human saccadic behavior simulation, we also demonstrated our superior effectiveness and robustness over state-of-the-arts by carrying out dense experiments on synthetic patterns and human eye fixation benchmarks. Multiple key issues in saliency modeling research, such as individual differences, the effects of scale and blur, are explored in this paper. Based on extensive qualitative and quantitative experimental results, we show promising potentials of statistical approaches for human behavior research
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| 650 |
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|a Journal Article
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4 |
|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Yao, Hongxun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ji, Rongrong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liu, Xian-Ming
|e verfasserin
|4 aut
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| 773 |
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8 |
|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 23(2014), 11 vom: 29. Nov., Seite 4649-62
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