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

Abstract

Point-cloud-based techniques play a very significant role in the archaeological application for stone tools. Measured point data involve small noises, which are overlaps obtained through measurement by laser devices. Such noisy data make it difficult to extract highly accurate segmented flakes, which will be used for the refitted flake matching process, because potential feature points lying on the boundary edges are hardly extracted. To overcome this issue, this paper describes a method of recognizing flake surfaces with noisy point clouds. First, the resampling method is applied to remove the noise in the input data. Then, the surface variation is calculated with a various number of neighbors and the potential feature points are detected by analyzing its surface variation. After that, feature lines are extracted from the potential feature points. The feature lines represent boundary edges of the flake surfaces. Finally, flake surfaces are extracted by the feature-line-based segmentation method. The implementation of this work can recognize flake surfaces from noisy data.