A Method of Recognizing Flake Surfaces from Noisy Point Cloud for Measuring Stone Tools

Erdenebayar, Shurentsetseg; Murakami, Keita; Chiba, Fumito; Konno, Kouichi

doi:10.1109/nicoint50878.2020.00008

Cited by 2 publications

(1 citation statement)

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“…The input data utilized in this study are point clouds of stone tools measured using a 3D scanner. 7 The proposed algorithm is easy to implement because we adopt simple algorithms, such as PCA, voxelization, 3D flood flilling, and Region growing segmentation.…”

Section: Proposed Methodsmentioning

confidence: 99%

A study on automatic flake surface segmentation of stone tools by calculating shape features

Altansukh

You

Altantsetseg

et al. 2023

International Workshop on Advanced Imaging Technology (IWAIT) 2023

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Stone tools are valuable archaeological findings dating to prehistoric age. The ancient people hit a rock with a stone hammer to make stone tools, it also produces flakes. To analyze human activities, reassembling stone tools is effective. When these stone tools are assembled using a computer, the flake surfaces of each stone must be extracted. The segmentation process is necessary for reassembling. This study aims for the segmentation in order to extract flake surfaces, and it requires identifying the orientation of the point normal vector. To do this, the surface of a flake must be considered. If the external space of the object can be correctly defined, directing normal vector is sufficient. While the surface is defined by adopting the voxelization technique, a surface is determined by a voxel that contains points. In addition, external and internal voxels are identified using the 3D flood filling algorithm. We applied normal vector estimation to region-growing segmentation and confirmed its effectiveness experimentally.

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Section: Proposed Methodsmentioning

confidence: 99%