In this paper, object-based disparity estimation scheme using adaptive disparity-based segmentation is proposed and its performance is analyzed in terms of PSNR through comparison to that of the conventional disparity estimation algorithms. In the proposed algorithm, firstly we can get segmented objects by region growing from input stereoscopic image pair, which is region growing is a procedure that groups pixels or sub-region into larger regions, and then, the feature-based disparity estimation method in which Canny mask operator is used for detecting the edge information from the input stereo pair are used for extracting the feature value. And, the matching window size for reconstruction of stereoscopic image is adaptively selected depending on the magnitude of the feature value of the input stereo pair by comparing with the predetermined threshold value. That is, coarse matching is carried out in the region having a small feature value while dense matching is carried out in the region having a large feature value. Accordingly, in this paper, this new approach can not only reduce mismatching possibility of the disparity vector mostly happened in the conventional dense disparity estimation with a small matching window size, but also reduce the blocking effect occurred in the disparity estimation with a large matching window size and region growing methods often give very good segmentations that correspond well to the observed edges.
From some experimental results, it is found that the proposed algorithm improves PSNR of the reconstructed image about 2.36~3.74 dB on the average than that of the conventional algorithms.