Abstract
Our goal is to enhance matching speed which is important for image engineering. Second Partial Derivative operator in Harris corner detector is directly used to compute the similarity between corners. Then initial matches are obtained. The algorithm is contrasted with normalized cross-correlation and method based on horizontal and vertical gradient. Its computational complexity is reduced and matching speed is improved effectively because this method only adopts the addition and subtraction operations. Experiments on several real images test the matching speed, the matching precision and matching rate of the algorithm. The results demonstrate that the algorithm not only have higher speed but also get higher matching precision and correct matching rate. Even though the stereo image pairs have brightness differences, it still performs rather well.
Highlights
Feature matching is to establish the correspondence between feature points of left and right image in a stereo image pair
The results demonstrate that the algorithm have higher speed and get higher matching precision and correct matching rate
There are several ways based on gray information to complete the initial matching, normalized cross correlation (NCC), sum of squared difference (SSD), and sum of absolute difference (SAD)
Summary
Feature matching is to establish the correspondence between feature points of left and right image in a stereo image pair. It is an important step in computer vision applications, such as stereo vision, motion tracking, and identification. During this process, matching speed is very important for real-time application of image processing. There are several ways based on gray information to complete the initial matching, normalized cross correlation (NCC), sum of squared difference (SSD), and sum of absolute difference (SAD). The most popular measure of similarity is the normalized cross correlation
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