Chessboard angular point sub-pixel extraction method based on Harris operator

Abstract

The invention relates to a chessboard angular point sub-pixel extraction method based on the Harris operator, and belongs to the technical field of micro-machine assembling and micro-vision detecting. The chessboard angular point sub-pixel extraction method comprises the steps that the Harris operator is used for conducing angular point detection on a chessboard; a processing result of the Harris operator is optimized and cluster points in angular points detected in the prior step are eliminated; noise points are eliminated by the utilization of the symmetric method, angular point secondary optimization is conducted in order to find out pixel-level angular points; based on secondary surface fitting, the precision of the pixel-level angular points is improved to be at the sub-pixel level. By means of the chessboard angular point sub-pixel extraction method, the angular points of the black and white chessboard can be detected precisely, fake angular points appearing in an existing algorithm cannot appear, and detecting precision reaches 0.1 pixel.

Description

technical field [0001] The invention relates to a method for extracting sub-pixels of checkerboard corner points based on a Harris operator, and belongs to the technical fields of micromechanical assembly and microscopic visual inspection. Background technique [0002] At present, the microscopic vision system plays a vital role in the assembly process of micro-small structural parts. The higher the camera calibration accuracy of the microscopic vision system is, the higher the assembly accuracy of the assembly system will be. The checkerboard template has been widely used in the camera calibration process. The common practice is to realize the camera calibration by establishing the relationship between the image coordinates of the feature points in the checkerboard template and the corner points of the black and white area of ​​the checkerboard and the corresponding world coordinates. , whether the checkerboard corner detection is accurate or not directly determines the acc...

AI Technical Summary

Problems solved by technology

A small threshold will lead to corner clustering, and a large threshold will lose part of the corner information;

[0006] (3) The size of the neighborhood when finding the local maximum will also affect the number and tolerance of corner points extracted

[0007] Directly use the Harris operator to process the image, and there will be many false corners and corner clustering in the result, and there are many noise points that are obviously not corner points. This result is not good for corner point extraction.

Images