A stable line/circle feature detection method and device

Abstract

The invention provides a method for stably detecting straight line / circle features. First, setting a base point and an initial detection area and repositioning the detection area through template matching; adjusting the detection area by use of position supplementation and correction; calculating and obtaining estimated value of noise variance through extracting measuring directional data, calculating measuring line local noise scales within the scope of a projection line, and performing filtering to measuring line local scales; then calculating edge points of one-dimensional data; detecting noise points in the points through fitting inner points and noise point segmentation and screening; keeping the inner points on the straight line / circle, and finally, fitting out a straight line / circle by the detected inner points by use of the least square method. By use of the method, edge feature points can be stably extracted in a strong noise image and fitted into a straight line / circle. Detection area and ROI area are set through template matching so that the straight line / circle detection is carried out within the ROI area, and therefore, the method has good calculation real-time performance and will be of great importance to industrial machine vision measurement techniques.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to a line / circle feature detection method. Background technique [0002] Line / circle is an important feature in the image. Line / circle detection is one of the most important tools in computer vision and machine vision recognition. It has important applications in machine vision, such as dimension measurement, reference coordinate system establishment, grid Detection of line / circle features such as feature recognition, document form recognition, scale line detection, mark point recognition radius, diameter measurement, etc. [0003] In image processing, the commonly used methods focus on Hough transform and Radon transform, and various algorithms focus on solving the detection accuracy and speed problems (see non-patent literature 1: Jiqiang Song, Michael RLyu.A Hough Transform based Line Recognition Method Utilizing both ParametersSpace and Image Space, Pattern Recognitio...

AI Technical Summary

Problems solved by technology

Patent Document 4 (Chinese Patent Publication No. CN104331876A) and Patent Document 5 (Chinese Patent Publication No. CN 104408456A) disclose a method and device for line detection and image processing based on Hough transform; Patent Document 6 (Chinese Patent Publication No. CN102482027A) A circle detection method based on Hough transform is disclosed. The radius of the circle is estimated in advance, and the circle is detected by Hough transform in a small radius range. Although the algorithm can improve efficiency, the stability is not high; Patent Document 7 (Chinese Patent Publication No. CN104036514A) discloses a circle detection method based on histogram peak search, patent document 8 (Chinese Patent Publication No. CN1039032824A) discloses a multi-circle detection method based on least squares, how to obtain circle edge points in this patent does not Open; Patent Document 9 (Chinese Patent Publication No. CN103295227A) discloses a circle detection method based on gradient direction segmentation

[0004] In industrial applications, due to the variety of products, the complex and changeable factory environment, or the changes and reflections on the surface of the product, the quality of the product image is degraded, and the image contains a lot of unstable factors such as noise and shadow. None of the methods based on Hough transform, Radon transform or chain code can detect straight line / circle features stably. At the same time, this type of algorithm takes a long time and cannot be used for real-time detection in industrial machine vision.

In the actual machine vision system, by relying on the salient features in the image for positioning, and then using the geometric positional relationship between the line / circle detection area and the salient feature area to locate the line / circle detection area in the subsequent image, in the line / circle detection area In the method, the ROI area is used to detect the line / circle feature, and the sub-pixel edge point is obtained, and then the least square method is used to fit the line / circle. However, in the strong noise image, the wrong edge point is often detected by mistake. When performing feature fitting, it will affect the results and cause detection errors

Some algorithms use enumeration to exclude abnormal points (points not on the line / circle), but this algorithm is not efficient

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