Content-Based Pseudoscopic View Detection

Stereoscopic images are generated from a pair of images (i.e., left and right images). In order to generate 3-D perception using the left and right images, it should be guaranteed that each image is perceived by the corresponding eye only. However, the depth perception becomes distorted when the left and the right eye views are interchanged, also known as a pseudoscopic problem. In this paper, we propose a novel method for detecting the pseudoscopic view by using disparity comparison in stereo images. Our approach originates from the idea that the disparities on a scene are categorized into three classes: zero disparity, positive disparity, and negative disparity, and that the foreground is usually located in front of the background. The proposed pseudoscopic view detection system consists of three sequential stages: 1) foreground/background segmentation, 2) feature points extraction, and 3) disparity comparison. We first segment the given image into two layers (i.e., foreground and background). Then, the feature points at each layer are extracted and matched to estimate the disparity characteristics of each layer. Finally, the existence of the pseudoscopic view can be investigated by using a disparity calibration model (DCM) presented in this paper and comparing the sign and magnitude of the average disparity of selected matching points set at each layer. Experimental results on various stereoscopic video sequences show that the proposed method is a useful and efficient approach in detecting the pseudoscopic view stereo images.