| 基于密度聚类的光条中心线提取方法 |
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| 引用本文： | 梁宇龙,段发阶. 基于密度聚类的光条中心线提取方法[J]. 激光技术, 2020, 44(4): 459-465. DOI: 10.7510/jgjs.issn.1001-3806.2020.04.011 |
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| 作者姓名： | 梁宇龙 段发阶 |
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| 作者单位： | 天津大学 精密测试技术及仪器国家重点实验室, 天津 300072 |
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| 基金项目： | 国家自然科学基金;天津市自然科学基金;国家重点研发计划 |
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| 摘 要： | 为了解决线结构光3维测量中噪声光斑对提取精度的影响,采用了密度聚类灰度重心提取算法提取激光光条中心线。该方法由中心线预提取以及中心线最终提取两阶段组成,预提取阶段实现对激光与光斑两者中心线的同时提取,最终提取阶段采用基于连通性的密度聚类算法完整保留激光中心线并剔除噪声光斑。在仿真实验阶段,对大小为600pixel×600pixel、含有激光中心线的图像进行了加噪处理,并使用提取结果与真实中心线之间各点的均方根误差以及运行时间作为考察标准进行了实验研究。结果表明,该方法与传统灰度重心法相比,在高亮度各向异性光斑、高亮度小面积光斑、高亮度点噪声图像的均方根误差分别降低了12.59pixel,15.12pixel和83.36pixel,时间复杂度分别提高了0.383s,0.412s和0.416s。该方法与传统灰度重心法相比具有更高的提取精度、近似的时间复杂度,且对噪声光斑具有较好鲁棒性,可以在噪声光斑图像中完整提取出光条中心线。
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| 关 键 词： | 图像处理 中心提取 灰度重心法 密度聚类 结构光 |
| 收稿时间： | 2019-08-15 |
Light bar centerline extraction method based on density clustering |
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| LIANG Yulong,DUAN Fajie. Light bar centerline extraction method based on density clustering[J]. Laser Technology, 2020, 44(4): 459-465. DOI: 10.7510/jgjs.issn.1001-3806.2020.04.011 |
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| Authors: | LIANG Yulong DUAN Fajie |
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| Affiliation: | (State Key Laboratory of Precision Measurement Technology and Instrument, Tianjin University, Tianjin 300072, China) |
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| Abstract: | In order to solve the influence of noise spot on extraction accuracy in the online structured light three-dimensional measurement, the center line of laser stripe was extracted by density clustering gray centroid extraction algorithm. The method consists of two stages: The pre-extraction of center line and the final extraction of center line. The pre-extraction stage realizes the simultaneous extraction of the center line of laser and spot. In the final extraction stage, the connectivity-based density clustering algorithm is used to preserve the laser centerline and eliminate the noise spot. In the simulation experiment stage, the image with the size of 600pixel×600pixel and the laser center line was denoised. The root mean square error and the running time of each point between the extracted result and the real center line were used as the inspection criteria. The results show that the root mean square error of high brightness anisotropic spot, high brightness small area spot, and high brightness point noise image was respectively reduced by 12.59pixel, 15.12pixel, and 83.36pixel, and the time complexity was respectively increased by 0.383s, 0.412s, and 0.416s. Compared with the traditional gray centroid method, this method has higher extraction accuracy, approximate time complexity, and better robustness to noise spot. |
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| Keywords: | image processing center extraction gray centroid method density clustering structured light |
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