基于改进灰色重心法的结构光条纹中心提取算法研究

IF 2.1 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jun Wang, Jingjing Wu, Xiang Jiao, Yue Ding
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引用次数: 0

摘要

摘要针对传统条形中心提取算法在速度和精度上无法满足结构光三维测量系统的要求,提出了一种基于改进质心算法的线结构光条形中心快速提取算法。该算法首先对结构光图像进行预处理,通过骨架提取得到条纹中心的近似位置;其次,利用灰度梯度法求解骨架上各像素点的法线方向。然后,利用重力法的加权灰色中心求解沿法线方向的条纹中心坐标;最后,利用最小二乘法拟合出光滑的条形中心线。实验结果表明,改进后的算法在速度、亚像素级精度和良好的结构光条纹中心提取效果上均有显著提高,并且改进算法的重复测量精度在0.01 mm以内,算法具有良好的重复性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on the center extraction algorithm of structured light fringe based on an improved gray gravity center method
Abstract In this study, we proposed a fast line-structured light stripe center extraction algorithm based on an improved barycenter algorithm to address the problem that the conventional strip center extraction algorithm cannot meet the requirements of a structured light 3D measurement system in terms of speed and accuracy. First, the algorithm performs pretreatment of the structured light image and obtains the approximate position of the stripe center through skeleton extraction. Next, the normal direction of each pixel on the skeleton is solved using the gray gradient method. Then, the weighted gray center of the gravity method is used to solve the stripe center coordinates along the normal direction. Finally, a smooth strip centerline is fitted using the least squares method. The experimental results show that the improved algorithm achieved significant improvement in speed, sub-pixel level accuracy, and a good structured light stripe center extraction effect, as well as the repeated measurement accuracy of the improved algorithm is within 0.01 mm, and the algorithm has good repeatability.
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来源期刊
Journal of Intelligent Systems
Journal of Intelligent Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
5.90
自引率
3.30%
发文量
77
审稿时长
51 weeks
期刊介绍: The Journal of Intelligent Systems aims to provide research and review papers, as well as Brief Communications at an interdisciplinary level, with the field of intelligent systems providing the focal point. This field includes areas like artificial intelligence, models and computational theories of human cognition, perception and motivation; brain models, artificial neural nets and neural computing. It covers contributions from the social, human and computer sciences to the analysis and application of information technology.
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