Using monocular depth cues for modeling stereoscopic 3D saliency

Iana Iatsun (1) , Mohamed-Chaker Larabi (1) , Christine Fernandez-Maloigne (1)

1 XLIM-SIC - SIC

Saliency is one of the most important features in human visual perception. It is widely used nowadays for perceptually optimizing image processing algorithms. Several models have been proposed for 2D images and only few attempts can be observed for 3D ones. In this paper, we propose a stereoscopic 3D saliency model relying on 2D saliency features jointly with depth obtained from monocular cues. On the one hand, the use of 2D saliency features is justified psychophysically by the similarity observed between 2D and 3D attention maps. On the other hand, 3D perception is significantly based on monocular cues. The validation of our model using state-of-the-art procedures including Kullback-Leibler divergence (KLD), area under the curve (AUC) and correlation coefficient (CC) in comparison with attention maps showed very good performance.

Domaines: Traitement du signal et de l'image [eess.SP]

Iana Iatsun, Mohamed-Chaker Larabi, Christine Fernandez-Maloigne. Using monocular depth cues for modeling stereoscopic 3D saliency. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2014, Florence, Italy. pp.589 - 593, ⟨10.1109/ICASSP.2014.6853664⟩. ⟨hal-01155652⟩