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Cooperative Stereo-Zoom Matching for Disparity Computation

This paper investigates a stereo matching approach incorporated with the zooming information. Conventional stereo vision algorithms take one pair of images for correspondence matching, while our proposed method adopts two zoom lens cameras to acquire multiple stereo image pairs with zoom changes. These image sequences are able to provide more accurate results for stereo matching algorithms. The new framework makes the rectified images compliant with the zoom characteristics by the definition of the relationship between the left and right images. Our approach can be integrated with existing stereo matching algorithms under some requirements and adjustments. In the experiments, we test the proposed framework on 2014 Middlebury benchmark dataset and our own zoom image dataset. The results have demonstrated the improvement of disparity computation of the our technique.