Pre-attentive detection of depth saliency using stereo vision

A quick estimation of depth is required by artificial vision systems for their self survival and navigation through the environment. Following the selection strategy of biological vision, known as visual attention, can help in accelerating extraction of depth for important and relevant portions of given scenes. Recent studies on depth perception in biological vision indicate that disparity is computed using object detection in the brain. The proposed method uses concepts from these studies and determines the shift that objects go through in the stereo frames using data regarding their borders. This enables efficient creation of depth saliency map for artificial visual attention. Results of the proposed model have shown success in selecting those locations from stereo scenes that are salient for human perception in terms of depth.