Curve corner point detection method based on difference accumulated values and three-point chain code differences

Abstract

The invention relates to a curve corner point detection method based on difference accumulated values and three-point chain code differences, which is technically characterized by comprising the following steps of: firstly carrying out Freeman chain code encoding to the obtained curve, calculating the difference accumulated values of the chain codes point by point according to the Freeman chain code encoding sequence based on the obtained Freeman chain codes, and dividing all pixel points on the curve into determining points, non-corner points and doubtful points through the difference accumulated values of the three-point chain codes; then calculating the three-point chain code differences of the doubtful points, and judging the doubtful points through the three-point chain code differences to find out true corner points in the doubtful points; and finally merging the corner points found in the determining points and the doubtful points to obtain all corner points of the curve. In the method, the points with curvatures to be calculated are judged by adopting the three-point chain code differences to replace the curvatures, thereby further reducing operation workload and lowering operation complexity. The corner points detected in the method also contain sequence characteristics which provide convenient for utilizing the corner points to carry out image processing.

Description

technical field [0001] The invention relates to a curve corner point detection method based on difference accumulation value and three-point chain code difference, which can be used for corner point detection on curves and belongs to image feature extraction. Background technique [0002] In digital image processing and computer vision, corner points contain important information of target objects. Shape analysis, pattern recognition, image matching, data compression, and motion analysis can be performed using corner point information. Therefore, corner detection has gradually become a research focus in computer vision and image processing. The current corner point detection methods can be roughly divided into two categories: (1) Use a mask operator to process each pixel in the image and its neighboring pixels, and then select the corner point according to the processing result. (2) Carry out corner point detection according to the edge information of the object. The seco...

AI Technical Summary

Problems solved by technology

At present, conventional methods generally use the calculation of the curvature of each point to detect corner points or judge corner points. Because there are many points on the curve, this leads to a large amount of calculation for conventional methods and is difficult to implement.

At present, it is also proposed to exclude some non-corner points first, and then only calculate the curvature value of a part of the points to detect the corner points. This method can eliminate some non-corner points, but the number of points that can be excluded is very limited, and some even Pixels close to the original curve, while this method still needs to calculate the curvature, so the amount of calculation is still very large in actual use

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