Leveraging Stereopsis for Saliency Analysis
Yuzhen Niu1, Yujie Geng1,2, Xueqing Li2, and Feng Liu1
1Computer Science Department, Portland State University
2Computer Science Department, Shandong University

                   input stereo image (left and right)

disparity map                                                        stereo saliency
The input image is used here with the permission from Flickr user "Wagman".
Abstract
Stereopsis provides an additional depth cue and plays an important role in the human vision system. This paper explores stereopsis for saliency analysis and presents two approaches to stereo saliency detection from stereoscopic images. The first approach computes stereo saliency based on the global disparity contrast in the input image. The second approach leverages domain knowledge in stereoscopic photography. A good stereoscopic image takes care of its disparity distribution to avoid 3D fatigue. Particularly, salient content tends to be positioned in the stereoscopic comfort zone to alleviate the vergence-accommodation conflict. Accordingly, our method computes stereo saliency of an image region based on the distance between its perceived location and the comfort zone. Moreover, we consider objects popping out from the screen salient as these objects tend to catch a viewer’s attention. We build a stereo saliency analysis benchmark dataset that contains 1000 stereoscopic images with salient object masks. Our experiments on this dataset show that stereo saliency provides a useful complement to existing visual saliency analysis and our method can successfully detect salient content from images that are difficult for monocular saliency analysis methods.
Paper
Yuzhen Niu, Yujie Geng, Xueqing Li, and Feng Liu. Leveraging Stereopsis for Saliency Analysis
IEEE CVPR 2012. PDF  

Related Paper

Long Mai, Yuzhen Niu, and Feng Liu. Saliency Aggregation: A Data-driven Approach
IEEE CVPR 2013. Project PDF

Long Mai and Feng Liu. Comparing Salient Object Detection Results without Ground Truth
ECCV 2014.
Stereo saliency benchmark 
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