Checker corner detection method

Abstract

A checker corner detection method uses a Harris corner detection algorithm to detect a checker image, thus obtaining candidate corners; a coordinate of the candidate corner is very precise and at a sub pixel level; taking each candidate corner as a center so as to obtain a square symmetrical template; using the square symmetrical template to process the candidate corners, and removing false corners, thus obtaining check corners. The checker corner detection method analyzes gray value distributed regularity in checker image corner neighbourhood, uses the square symmetrical template taking the corner as the center, and employs the square symmetrical template to process the candidate corners and removes the false corners so as to obtain the check corners; the method is short in calculation time, and high in detection precision.

Description

technical field [0001] The invention relates to the field of image technology, in particular to a checkerboard corner detection method. Background technique [0002] In an image, a corner point is a point where the brightness of the two-dimensional image changes sharply or a point with a maximum curvature on the edge curve of the image. It is an important local feature. The information content of the corner point is very high, which can provide sufficient constraints for image processing. , reduce the amount of calculation, greatly improve the speed of calculation, which is conducive to the reliable matching of images, making real-time processing possible, and in various image features, the corners have the advantages of rotation invariance and no change with lighting conditions, so in the target Corner points are of great significance in recognition, image matching, and image reconstruction. [0003] Among them, checkerboard corners are widely used in camera calibration as...

AI Technical Summary

Problems solved by technology

The advantage is that it avoids the dependence on the edge. The disadvantage is that sometimes there is a large offset between the local maximum point of the product of gradient and curvature and the real position of the corner point, resulting in low positioning accuracy, and sometimes some corners on the checkerboard are not originally corners. The interference point of the point is mistaken for the corner point or some actual corner points are missed; the third type is the corner point detection algorithm based on wavelet transform. The disadvantage of this type of method is that the performance of the algorithm is only when the feature scale of the object Better, and these scale information also need prior knowledge

In addition, the calibration function of OPEN CV, which is widely used at present, also has missed or multi-checked checkerboard corners.

This will undoubtedly have a great impact on the accuracy of camera calibration results.

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