Spectral moving removal of non-isolated surface outlier clusters
In this paper, we propose a new algorithm for the fast removal of non-isolated surface outlier clusters. It consists of three basic components: (a) an intrinsic metric for detecting outliers on the basis of minimum variance principle; (b) bi-means clustering of a normalized histogram; (c) surface propagation for a geometric coherence check. The unique contributions of our approach include (a) a new idea of identifying non-isolated outlier clusters and linking the local spectral property to a global outlier removal process; (b) a modified data clustering scheme with a geometric coherence check. In comparison with existing algorithms, our algorithm is evaluated in terms of the quality and computational cost of outlier removal. Numerical experiments indicate the effectiveness of our approach in the aspects of convergence, accuracy, time and space efficiency.
point cloud spectral clustering surface noise measurement outlier reverse engineering metrology inspection