2016
DOI: 10.1007/s11042-016-4155-y
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Abstract: Saliency prediction models provide a probabilistic map of relative likelihood of an image or video region to attract the attention of the human visual system. Over the past decade, many computational saliency prediction models have been proposed for 2D images and videos. Considering that the human visual system has evolved in a natural 3D environment, it is only natural to want to design visual attention models for 3D content. Existing monocular saliency models are not able to accurately predict the attentive … Show more

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Cited by 21 publications
(15 citation statements)
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“…Our eye tracking experiments showed that LBVS3D has close correlation with the human fixation data [38,41]. A block-diagram of LBVS3D (from [38]) is depicted in Fig. 2.…”
Section: A Learning Based Visual Saliency For 3d (Lbvs3d)mentioning
confidence: 59%
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“…Our eye tracking experiments showed that LBVS3D has close correlation with the human fixation data [38,41]. A block-diagram of LBVS3D (from [38]) is depicted in Fig. 2.…”
Section: A Learning Based Visual Saliency For 3d (Lbvs3d)mentioning
confidence: 59%
“…In our previous work [38], we designed a Learning Based Visual Saliency (LBVS3D) prediction model of attention for stereoscopic video. This model takes into consideration both low-level stimulus driven saliency features such as depth, motion, brightness, texture, and color, as well as high-level context dependent attributes such as presence of humans, text, vehicles, animals, and horizon.…”
Section: A Learning Based Visual Saliency For 3d (Lbvs3d)mentioning
confidence: 99%
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