A microhardness indentation point cloud segmentation method based on voxel cloud connectivity segmentation. (December 2021)

• Record Type: Journal Article
• Authors: Wei, Shi; Chengyao, Shi; Shilin, Chen; Kailin, Zhang
• Is Part Of: Measurement. Volume 18(2021)
• Publication Date: 2021-12
• Subjects: Voxel cloud connectivity segmentation (VCCS) -- Ultra-microhardness -- Indentation point cloud -- Segmentation algorithm -- Score model; Detectors -- Periodicals; Measurement -- Periodicals; 530.7
• DOI: 10.1016/j.measen.2021.100124
• Languages: English
• ISSNs: 2665-9174

Abstract

Abstract: In order to accurately extract the effective area of microhardness indentation obtained by laser scanning confocal microscope, the indentation point cloud segmentation method is studied based on over-segmentation using voxel cloud connectivity segmentation. Further, a microhardness indentation point cloud segmentation algorithm is proposed. The proposed method can determine the belonging area of the point cloud by combining multiple feature score models, and can achieve the segmentation and extraction of ultra-microhardness indentation data where the characteristics of indentation area are not obvious, or the data point is dense. The experimental results show that the method can extract the indentation area accurately, and the repeatability and consistency of extraction is good.