Corner point detection algorithm based on contour

Abstract

In digital image processing, corner detection is widely applied to the fields of shape extraction and analysis, target registration and matching, image compression and the like as a part of image feature extraction, and has an inseparable relationship with measurement, identification and positioning of workpieces in an industrial field. In order to solve the problems of low positioning accuracy, long detection time and the like when angular point extraction is carried out on a workpiece in the industry at present, the invention provides a contour-based angular point detection algorithm, contour extraction is carried out on a preprocessed image by adopting an eight-neighborhood contour tracking method, the sharpness of contour points is calculated, candidate angular points are obtained through non-maximum suppression, and the candidate angular points are optimized by adopting an SUSAN circular template and a support area adaptive method. Experimental results show that the algorithm is high in anti-interference performance, accurate in positioning and high in speed.

Description

technical field [0001] The invention relates to an image corner extraction method, which can be used in industrial measurement and the like. It belongs to the technical field of image processing. Background technique [0002] The corner of the image is an important characteristic of the image. As an important part of the automatic detection of workpieces in the industrial field, the corner detection of the image (Corner Detection) has a direct impact on the ability and speed of corner extraction. The accuracy and efficiency of industrial automation inspection. [0003] The corner detection algorithms at this stage mainly include three types of corner detection algorithms based on grayscale images, binary images and contour curves. However, when corner points are extracted on contour curves, due to the less calculation and the accuracy of detection Higher, and industrial inspection has great requirements on speed while ensuring accuracy, so this type of algorithm is widely ...

AI Technical Summary

Problems solved by technology

[0004] The corner detection algorithm based on the contour sharpness first performs Gaussian smoothing on the contour curve at the optimal scale, then extracts the corner points through the approximate calculation of the angle between the support areas on both sides of the center point of the contour line, and finally performs non-maximum value suppression to obtain the final Corner points, the algorithm can better detect corner points and suppress false corner points, but Gaussian smoothing makes the algorithm complex and reduces the detection accuracy

[0005] The SUSAN algorithm uses a circular template to detect the corners of the image, and the corners can be judged directly by comparing the gray value of the pixel neighborhood. The advantage is that there is no need to calculate the gradient, no interpolation, and no dependence on the previous image preprocessing. The disadvantage is that the positioning accuracy is poor, it is sensitive to noise, and it may miss corner points

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