Abstract
In order to accurately extract corners from the image with high texture complexity, the paper analyzed the traditional corner detection algorithm based on gray value of image. Although Harris corner detection algorithm has higher accuracy, but there also exists the following problems: extracting false corners, the information of the corners is missing and computation time is a bit long. So an improved corner detection algorithm combined Harris with SUSAN corner detection algorithm is proposed, the new algorithm first use the Harris to detect corners of image, then use the SUSAN to eliminate the false corners. By comparing the test results show that the new algorithm to extract corners very effective, and better than the Harris algorithm in the performance of corner detection.
Highlights
The corner points in images contain large amounts of information, which can reflect the local features of image, and provide important information for further processing
Calculate the autocorrelation matrix M, judge which one is feature point according to its eigenvalue
When circular mask is completely on the background or target, we get the largest USAN area; when the mask approaches the edge of the target, the USAN area decreases gradually; when the mask nucleus is on the edge of the target, USAN area is very small; when the mask nucleus in the corner point, USAN area is smallest, This is SUSAN corner detector
Summary
The corner points in images contain large amounts of information, which can reflect the local features of image, and provide important information for further processing. The corner detection methods exist for achieving this are many, mainly they can be divided into two categories: one is based on the edges[1], the other is based on the grayscale changes[2]. The most widely used operators in the grayscale-based corner detection algorithm are SUSAN detector and Harris detector. The two methods are both having advantages and disadvantages, and applying to appropriate environments and conditions of its own. This paper is trying to make some improvements aiming at the weaknesses of Harris detector, making it better in extracting the corner points in images
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