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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Communication dans un congrès
IVCNZ, Nov 2010, Queenstown, New Zealand. 2010
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https://hal-ujm.archives-ouvertes.fr/ujm-00578107
Contributeur : Eric Dinet <>
Soumis le : vendredi 18 mars 2011 - 13:30:10
Dernière modification le : mercredi 25 juillet 2018 - 14:05:31

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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. 2010. 〈ujm-00578107〉

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