Abstract
This paper is a combined method for stereo matching. Between the stereo images have difference perspective each other. The difference of stereo images is called disparity. This information measures the difference of reference and target stereo images. Then we can estimate the correspondence points on the target image from reference image. The proposed method is a combination of SIFT and cost aggregation. One of popular feature matching, SIFT and traditional cost aggregation method, BP(Belief propagation) are evaluated stereo matching points. In this work, the Middlebury stereo dataset helps to apply proposed work.
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