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A workpiece image registration method based on contour curvature
Published:2024-10-30 author:JIN Yanjun, LIU Haiping, NI Shuangjing, et al. Browse: 111 Check PDF documents
A workpiece image registration method based on contour curvature


JIN Yanjun, LIU Haiping, NI Shuangjing, ZHU Xihao

(Zhejiang Institute of Mechanical & Electrical Engineering Co.,Ltd., Hangzhou 310051, China)


Abstract: With the development of machine vision technology, the workpiece image registration is widely used in the applications of machine grasping and defect detection. However, the different viewpoints of the workpiece are easy to cause the difficulty of image matching, and it becomes an urgent problem to be solved and studied. Considering the unique contour shape of the workpiece and the matching problem caused by multiple viewpoints, a workpiece image registration method based on contour curvature was proposed. Firstly, in contour smoothing processing, to address the issue of poor performance in polygon fitting Douglas-Pucker (DP) algorithm, the sliding window averaging method was used to further process the contours, and the smoothness of the contours was effectively improved. Then, in the resampling of contour curves, the time serialization was performed on the set of sampling points, and an effective resampling method was designed using the sampling interval to realize arbitrary values of resampling points and keep the density of contour points. When establishing a fully affine curvature library, a two-step matching strategy was designed to effectively improve matching accuracy and efficiency. Finally, for the curvature curve matching algorithm, shape context (SC) was introduced into dynamic time warping (DTW) distance calculation to solve the problems of DTW algorithm. The research results show that this method can effectively perform affine transformations on template workpiece images, achieving a match with the target workpiece images. Meanwhile, in the three comparative experiments of the matching algorithm, the transformed images obtained through the SCDTW matching algorithm has only 4.8%, 2.65%, and 3.92% differences from the target workpiece images. This is significantly less compared to the differences of 6.83%, 4.03%, 6.34% for DTW, and 7.45%, 6.39%, 7.31% for normalized cross correlation (NCC). This smaller discrepancy indicates that the algorithm has better registration effects and can more effectively calculate the similarity between two curves. Consequently, it can obtain the optimal curvature curve sample and achieve accurate workpiece image registration.

Key words:  image processing; contour extraction; contour smooth method; resampling of contour curve; image registration of workpiece; contour curvature; fully affine model; dynamic time warping (DTW)
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