DMCA
SALIENCY DETECTION FOR STEREOSCOPIC IMAGES
BibTeX
@MISC{Fang_saliencydetection,
author = {Yuming Fang and Junle Wang and Manish Narwaria and Patrick Le Callet and Weisi Lin},
title = {SALIENCY DETECTION FOR STEREOSCOPIC IMAGES},
year = {}
}
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Abstract
Saliency detection techniques have been widely used in var-ious 2D multimedia processing applications. Currently, the emerging applications of stereoscopic display require new saliency detection models for stereoscopic images. Different from saliency detection for 2D images, depth features have to be taken into account in saliency detection for stereoscopic images. In this paper, we propose a new stereoscopic salien-cy detection framework based on the feature contrast of color, intensity, texture, and depth. Four types of features including color, luminance, texture, and depth are extracted from DC-T coefficients to represent the energy for image patches. A Gaussian model of the spatial distance between image patch-es is adopted for the consideration of local and global contrast calculation. A new fusion method is designed to combine the feature maps for computing the final saliency map for stereo-scopic images. Experimental results on a recent eye track-ing database show the superior performance of the proposed method over other existing ones in saliency estimation for 3D images. Index Terms — stereoscopic images, 3D Saliency Detec-tion, stereoscopic saliency detection, visual attention 1.
Keyphrases
saliency detection stereoscopic image saliency detection stereoscopic image saliency detec-tion new stereoscopic salien-cy detection framework multimedia processing application gaussian model saliency estimation dc-t coefficient stereoscopic saliency detection final saliency map image patch depth feature index term stereoscopic display superior performance image patch-es spatial distance stereo-scopic image global contrast calculation feature contrast new fusion method recent eye track-ing database saliency detection technique visual attention new saliency detection model experimental result
