Stereoscopic Visual Attention Model for 3D Video

Zhang, Yun; Jiang, Gangyi; Yu, Mei; Chen, Ken

doi:10.1007/978-3-642-11301-7_33

Cited by 88 publications

(43 citation statements)

References 11 publications

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“…This type of models (e.g. [29], [30] and [31]) does not contain any depth-map-based feature-extraction processes. Apart from detecting the salient areas by using 2D visual features, these models share a same step in which depth information is used as the weighting factor of the 2D saliency.…”

Section: Dw Depth Information Operation Validationmentioning

confidence: 99%

Computational Model of Stereoscopic 3D Visual Saliency

Wang

daSilva

Le-Callet

et al. 2013

IEEE Trans. on Image Process.

119

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Abstract-Many computational models of visual attention performing well in predicting salient areas of 2D images have been proposed in the literature. The emerging applications of stereoscopic 3D display bring additional depth information affecting the human viewing behavior, and require extensions of the efforts made in 2D visual modeling. In this paper, we propose a new computational model of visual attention for stereoscopic 3D still image. Apart from detecting salient areas based on 2D visual features, the proposed model takes depth as an additional visual dimension. The measure of depth saliency is derived from the eye movement data obtained from an eye-tracking experiment using synthetic stimuli. Two different ways of integrating depth information in the modeling of 3D visual attention are then proposed and examined. For the performance evaluation of 3D visual attention models, we have created an eye-tracking database which contains stereoscopic images of natural content and is publicly available along with this paper. The proposed model gives a good performance, compared to that of state-of-the-art 2D models on 2D images. The results also suggest that a better performance is obtained when depth information is taken into account through the creation of a depth saliency map rather than when it is integrated by a weighting method.

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Section: Dw Depth Information Operation Validationmentioning

confidence: 99%

Computational Model of Stereoscopic 3D Visual Saliency

Wang

daSilva

Le-Callet

et al. 2013

IEEE Trans. on Image Process.

119

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“…However, when color contrast is not distinct enough, depth cue can serve as the complement to weight the color contrast under the assumption that a closer region is usually more attractive for human visual attention [12]. For each region i R , its color contrast is evaluated by comparing to all the other regions in the image with a depth weighting factor as follows: (1), the parameter  controls the strength of spatial weighting, and is set to 0.4, a value larger than  , for a moderate spatial weighting effect, due that color variations are usually larger than depth variations in most neighboring regions.…”

Section: Depth Weighted Color Contrastmentioning

confidence: 99%

Salient region detection for stereoscopic images

Fan

Sun

2014

2014 19th International Conference on Digital Signal Processing

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In this paper, we propose an effective saliency model, which combines region-level depth, color and spatial information, to detect salient regions in stereoscopic images. Based on region segmentation results of stereoscopic images, depth contrast, depth weighted color contrast, and spatial compactness of color distribution are measured for each region, and combined to generate the region-level saliency map. Experimental results on a public stereoscopic image dataset with ground truths of salient objects demonstrate that the proposed saliency model outperforms the state-of-the-art saliency models.

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“…Zhang et al [53] proposed a stereoscopic visual attention model for 3D video based on multiple perceptual stimuli. Chamaret et al [54] designed a Region of Interest (ROI) extraction method for adaptive 3D rendering.…”

Section: D Visual Saliency Mapmentioning

confidence: 99%