Research on Workpiece Corner Detection Method based on Combination Method Image Feature Points

To improve the workpiece localization accuracy, the paper proposes an improved image alignment optimization method by random sample consensus (RANSAC) and selecting some point pairs with high confidence from them, and then calculating the affine transformation matrix separately using the combination method to filter the optimal affine transformation matrix algorithm. The image is preprocessed. First, the grayscale image is filtered with a median to remove the noise, and the scale-invariant feature transform (SIFT) detection algorithm is used to obtain the image feature points and perform feature point matching; the more decisive matching points are coarsely selected by the distance threshold between the matched feature point pairs, and the more decisive matching points are chosen from Several teams of issues are selected from the more decisive matching points. The base image transformation matrix is calculated respectively, specific methods filter the better matrix, and the better rotation angle is calculated. The accuracy and stability of the algorithm are verified through experiments, and the experimental results show that the error of the rotation angle of the workpiece is within 0.1° and the algorithm's running time is within 1 ms.