A visual attention model for stereoscopic 3D images using monocular cues

Iatsun, Iana; Larabi, Mohamed–Chaker; Fernández-Maloigne, Christine

doi:10.1016/j.image.2015.05.009

Cited by 7 publications

(2 citation statements)

References 47 publications

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“…2D + DSM considered the center-surrounded mechanisms. We then compared our proposed model with the stereoscopic 3D saliency model proposed by [45]. We should note that the stereo model in [45] has already taken the center bias into consideration.…”

Section: Comparison Of Our Proposed Methods With Other Methodsmentioning

confidence: 99%

“…We then compared our proposed model with the stereoscopic 3D saliency model proposed by [45]. We should note that the stereo model in [45] has already taken the center bias into consideration. From Table 3, we can see that the performance is not improved significantly using the depth information as a weighted value (2D × depth (Chamaret)) in AUC and CC.…”

Section: Comparison Of Our Proposed Methods With Other Methodsmentioning

confidence: 99%

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Stereoscopic visual saliency prediction based on stereo contrast and stereo focus

Cheng

Zhang

et al. 2017

J Image Video Proc.

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In this paper, we exploit two characteristics of stereoscopic vision: the pop-out effect and the comfort zone. We propose a visual saliency prediction model for stereoscopic images based on stereo contrast and stereo focus models. The stereo contrast model measures stereo saliency based on the color/depth contrast and the pop-out effect. The stereo focus model describes the degree of focus based on monocular focus and the comfort zone. After obtaining the values of the stereo contrast and stereo focus models in parallel, an enhancement based on clustering is performed on both values. We then apply a multi-scale fusion to form the respective maps of the two models. Last, we use a Bayesian integration scheme to integrate the two maps (the stereo contrast and stereo focus maps) into the stereo saliency map. Experimental results on two eye-tracking databases show that our proposed method outperforms the state-of-the-art saliency models.

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Section: Comparison Of Our Proposed Methods With Other Methodsmentioning

confidence: 99%

Section: Comparison Of Our Proposed Methods With Other Methodsmentioning

confidence: 99%