Enhanced image saliency model based on blur identification

Abstract : Detection of visual saliency is of great interest for a lot of computer vision applications in particular for content-based image retrieval. The work presented in this paper is devoted to develop an algorithm of saliency detection that performs adequately in predicting human fixations for stimuli containing blur and sharp regions. This work is based on an experimental study on the effect of blurriness on visual attention when observers see images with no prior knowledge in free viewing conditions. A ground-truth has been derived from this experimental study to test the saliency model we developed.
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Conference papers
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https://hal-ujm.archives-ouvertes.fr/ujm-00578107
Contributor : Eric Dinet <>
Submitted on : Friday, March 18, 2011 - 1:30:10 PM
Last modification on : Wednesday, July 25, 2018 - 2:05:31 PM

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  • HAL Id : ujm-00578107, version 1

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Rizwan Ahmed Khan, Hubert Konik, Eric Dinet. Enhanced image saliency model based on blur identification. IVCNZ, Nov 2010, Queenstown, New Zealand. ⟨ujm-00578107⟩

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