Feature point matching is a key step in computer vision and has important applications in many fields. Through the study of current image feature point matching methods, a matching method based on the combination of grayscale and geometric feature quantities of feature points is extracted. This method first uses the Harris algorithm to extract feature points; then uses the epipolar constraint to reduce the search range; and finally uses the grayscale of the feature points to achieve feature point matching. This method uses epipolar constraints to overcome the disadvantage of large computational complexity of feature point matching using grayscale and improves the matching speed. Experiments show that it is an accurate and fast feature point matching method.
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