A multi-features based corner detection method
Teng Jie, Jian Li, Xiangjing An, Hangen He · 2016
To improve the accuracy of corner's detection in the traditional black and white chessboard, a new method based on multi-features is proposed. Three distinct local features of the corners have been analyzed, they are structural response, symmetric response and edge response. By selectively applying these features, initial selection and later screening of potential corners have been done. Non-maximum suppression (NMS) has been used to generate original potential corner candidates, which could be scored by the combination of feature responses mentioned above. With all scores reasonably thresholded, false corners could be removed. Meanwhile, sub-pixel level of corner coordinates is achieved using the orthogonality of potential corners and adjacent pixels. Experimentally, final results prove the effectiveness and robustness of the proposed method with high sub-pixel accuracy.