摘要
在立体视觉检测系统中,特征匹配技术至关重要,其用于识别和对齐不同图像间的相似特征,实现图像对比、物体识别、三维重建等多项任务。特征匹配的质量直接影响整个立体视觉检测系统的精度,特征点提取是特征匹配的基础,这些点的质量直接决定了匹配的准确性和算法的鲁棒性。ORB算法因具有高效性,被广泛应用于特征匹配任务,但在处理复杂场景时,特征点在数量和分布均匀性方面存在不足。对此,提出了一种改进的基于关键点密度的自适应抽样方法,通过结合图像的局部对比度和梯度信息,优化ORB算法中关键点的分布,以实现整个图像中关键点的均匀选取,提高特征点提取性能。利用Middlebury立体视觉数据集进行的实验结果表明,改进后的算法相比传统方法,在关键点数量和分布均匀性上有显著提升,同时保持了接近原ORB算法的运行效率。此项研究不仅针对ORB算法在复杂场景处理中的不足提供了有效的解决方案,也为计算机视觉领域特征点提取和匹配的优化开辟了新的途径。
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.
作者
景有鲜
朱庆生
JING Youxian;ZHU Qingsheng(CAS Nanjing Astronomical Instruments Research Center,Nanjing 210042,China;College of Astronomy and Space Science,University of Science and Technology of China,Hefei 230026,China)
出处
《计算机科学》
CSCD
北大核心
2024年第S02期255-259,共5页
Computer Science
基金
国家自然科学基金(12141304)。
