Computing refined skeletal features from medial point clouds

J. Kustra, A.C. Jalba, A.C. Telea

Research output: Contribution to journalArticleAcademicpeer-review

7 Citations (Scopus)
2 Downloads (Pure)

Abstract

Medial representations have been widely used for many shape analysis and processing tasks. Large and complex 3D shapes are, in this context, a challenging case. Recently, several methods have been proposed that extract point-based medial surfaces with high accuracy and computational scalability. However, the resulting medial clouds are of limited use for shape processing due to the difficulty of computing refined medial features from such clouds. In this paper, we show how to bridge the gap between having a raw medial cloud and enriching this cloud with feature points, medial-point classification, medial axis decomposition into sheets, robust regularization, and Y-network extraction. We further show how such properties can be used to support several shape processing sample applications including edge detection and shape segmentation, for a wide range of complex 3D shapes. Keywords: Feature extraction; Shape segmentation; Point cloud; Skeleton segmentation; Polygonal
Original languageEnglish
Pages (from-to)13-21
JournalPattern Recognition Letters
Volume76
DOIs
Publication statusPublished - 1 Jun 2016

Bibliographical note

Special Issue on Skeletonization and its Application

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