IEEE Trans Image Process - Computational model of stereoscopic 3D visual saliency.

Tópicos

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Resumo

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 an additional depth of 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 images. 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.

Resumo Limpo

mani comput model visual attent perform well predict salient area d imag propos literatur emerg applic stereoscop d display bring addit depth inform affect human view behavior requir extens effort made d visual model paper propos new comput model visual attent stereoscop d still imag apart detect salient area base d visual featur propos model take depth addit visual dimens measur depth salienc deriv eye movement data obtain eyetrack experi use synthet stimuli two differ way integr depth inform model d visual attent propos examin perform evalu d visual attent model creat eyetrack databas contain stereoscop imag natur content public avail along paper propos model give good perform compar stateoftheart d model d imag result also suggest better perform obtain depth inform taken account creation depth salienc map rather integr weight method

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