Dynamic Granularity Matrix Space Based Adaptive Edge Detection Method for Structured Light Stripes

被引:8
作者
Yu, Changzhi [1 ]
Ji, Fang [1 ]
Jing, Xingjiu [1 ]
Liu, Mi [1 ]
机构
[1] China Acad Engn Phys, Inst Mech Mfg Technol, Mianyang 612999, Peoples R China
基金
中国国家自然科学基金;
关键词
Number:; 51705485; Acronym:; NSFC; Sponsor: National Natural Science Foundation of China; TB23; CAEP; Sponsor: China Academy of Engineering Physics;
D O I
10.1155/2019/1959671
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Structured light has been widely applied to 3D shape measurement with the capabilities of rapidness, high-accuracy, and noncontact. Because of uneven illumination and noise, it is difficult to distinguish the light stripes and background of the image, which reduces the measurement accuracy. In this paper, an adaptive Canny edge detection method with two phases is proposed for structured light stripes. Firstly, the idea of dynamic granularity is introduced and the dynamic granularity matrix space is established, in which image segmentation problems are described as the transformations and jumping of image at different granularity layers. The hierarchical structure and optimal granularity layer of image are obtained as the basis of adaptive edge detection. Secondly, a quantum-inspired group leader hybrid algorithm is adopted to calculate the optimal threshold from the optimal granularity layer, which is taken as the adaptive threshold for Canny operator. Finally, experimental tests and comparisons have been conducted to verify the effectiveness of the adaptive method proposed. The experiments show that the proposed method achieves high segmentation accuracy, improves the segmentation efficiency, and has strong robustness to noise.
引用
收藏
页数:14
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