Abstract: In stereo vision inspection system,feature matching technology is crucial for identifying and aligning similar features between different images,and realizing many tasks such as image comparison,object recognition,and 3D reconstruction.The qua-lity of feature matching directly affects the accuracy of the whole stereo vision detection system.Feature point extraction is the basis of feature matching,and the quality of these points directly determines the accuracy of matching and the robustness of the algorithm.The ORB algorithm is widely used in the feature matching task because of its high efficiency,but there are deficiencies in terms of the number and uniformity of the distribution of the feature points when dealing with complex scenes.In this paper,an improved adaptive sampling method based on the density of keypoints is proposed to optimize the distribution of keypoints in the ORB algorithm by combining the local contrast and gradient information of the image,so as to achieve the uniform selection of keypoints in the whole image and to improve the performance of feature point extraction.Experimental results on the Middlebury stereo vision dataset show that the improved algorithm significantly improves the number of keypoints and the uniformity of distribution compared to the traditional method,while maintaining an operational efficiency close to that of the original ORB algorithm.This study not only provides an effective solution to the shortcomings of the ORB algorithm in complex scene processing,but also opens up a new way for the optimization of feature point extraction and matching in the field of computer vision.

Key words: Stereovision, Feature extraction, ORB algorithm, Keypoint density, Adaptive sampling