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Stereo matching is important in the area of computer vision and photogrammetry. We present a stereo matching algorithm to refine depth map by using stereo image pair. The reference image is segmented by using hill-climbing algorithm and Scale Invariant Feature Transform (SIFT) feature descriptor with Sum of Absolute Difference (SAD) local stereo matching is performed. Next, we extract a set of disparity...
Stereo matching is an active research area in computer vision for decades. Most of the existing stereo matching algorithms assume that the corresponding pixels have the same intensity or color in both images. But in real world situations, image color values are often affected by various radiometric factors such as exposure and lighting variations. This paper introduces a robust stereo matching algorithm...
Stereo matching is an active research area in computer vision for decades. This paper introduces a new disparity map estimation algorithm based on image segments. The reference image is segmented using hill-climbing algorithm. The initial disparity map is estimated Scale Invariant Feature Transform (SIFT) feature points matching between two stereo images in each segment by Sum of Absolute Difference...
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