Abstract
Matching Algorithms are explored by many experts, lots of good methods are used for photography, computer vision, 3D reconstruction etc. Such as SIFT and some other improved algorithms that are robust to translation, rotation, illumination, angle of view, and scale different. However, it is a bottleneck for their application in some in time system because of their high time cost. A parallel framework for automatic matching for high resolution imagery with large size is introduced, the most time-consuming parts while matching are devised by parallel in a server with many cores, for example, the feature extracting, matching, and the quick quasi-dense matching on the epipolar resampled images. Experiments show that, it works efficiently and robustly.
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