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Feature matching method for uncorrected fisheye lens image

Traditional matching algorithms cannot be directly applied to the fisheye image matching for large distortion existing in fisheye image. Therefore, a matching algorithm based on uncorrected fisheye images is proposed. This algorithm adopts a local feature description method which combines MSER detector with CSLBP descriptor to obtain the image feature. First, the two uncorrected fisheye images captured by binocular vision system are described by the principle of epipolar constraint. Then the region detection is done with MSER and the ellipse fitting is used to the obtained regions. The MSER regions are described by CSLBP subsequently. Finally, in order to exclude the mismatching points of initial match, random sample consensus (RANSAC) algorithm has been adopted to achieve exact match. Experiments show that the method has a good effect on the uncorrected fisheye image matching.