Fast adaptive corner combined center detecting method based on geometric features

Abstract

The invention provides a fast adaptive corner combined center detecting method based on geometric features, and belongs to the technical field of image processing algorithms. The invention treats themarker by using the threshold binaryzation with different thresholds twice; then performs corner detection on the marker image by using the improved SUSAN algorithm to coarsely locate the center and the radius; performs Canny operator on the circular image after binarization to detect the edge, and radius-constrains the edge to obtain a refined and continuous edge; finally, processes the edge detected at this time, and obtains center coordinates of good precision by combining a mode of multi-processing and weighted averaging. The invention avoids the gradient calculation, reduces the interference caused by the noise, improves the rate of the algorithm, has good precision and running speed, is simple and easy to implement, and meets the real-time performance.

Description

technical field [0001] The invention relates to a method for detecting the center of a circle based on a geometric feature-based fast self-adaptive corner point combination, and belongs to the technical field of image processing algorithms. Background technique [0002] Generally, the accuracy of the center coordinates of a circular marker point is determined by the accuracy of the circle's edge detection. The paper "Circular target sub-pixel center positioning based on non-maximum suppression" (Journal of Instrumentation, Wang Jing, 201207) uses the gradient information in the image combined with the method of non-maximum suppression to obtain the ideal edge of continuous refinement, Realized the positioning of the pixel-level edge; combined with the 5×5 Zernike moment to perform sub-pixel-level positioning on the edge, and finally used the least squares method to fit twice to obtain the center coordinates of the circular marker points, and compared the simulation images an...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that although the coordinates of the circular marker points with higher accuracy can be obtained by refining the edge, the isolated noise points cannot be eliminated, and the image gradient information obtained in practice is often affected by noise. Changes, which affect the accuracy of the positioning of the center of the marker point

The disadvantage of this method is: using the Canny operator to extract edge information, the obtained edge can only be at the pixel level, while the effect of the ellipse fitting method is limited, and it is prone to edge discontinuity in practice

The disadvantages of this method are: experiments have been carried out on ideal images and noise images and the good accuracy and real-time performance of the method have been verified. However, there are a large number of iterative solutions to nonlinear equations in this method, and a large number of iterative processes seriously affect time efficiency of the algorithm

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