Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/112598
Title: Finding the Skeleton of 2D Shape and Contours: Implementation of Hamilton-Jacobi Skeleton
Authors: He, Yuchen
Kang, Sung Ha
Alvarez, L 
UNESCO Clasification: 220990 Tratamiento digital. Imágenes
Keywords: 2D shape
Skeleton
Thinning algorithm
Distance transform
Issue Date: 2021
Journal: Image Processing On Line 
Abstract: This paper presents the details of the flux-ordered thinning algorithm, which we refer to as the Hamilton-Jacobi Skeleton (HJS). It computes the skeleton of any binary 2D shape. It is based on the observation that the skeleton points have low average outward flux of the gradient of the distance transform. The algorithm starts by computing the distance function and approximating the flux values for all pixels inside the shape. Then a procedure called homotopy preserving thinning iteratively removes points with high flux while preserving the homotopy of the shape. In this paper, we implement the distance transform using a fast sweeping algorithm. We present numerical experiments to show the performance of HJS applied to various shapes. We point out that HJS serves as a multi-scale shape representation, a homotopy classifier, and a deficiency detector for binary 2D shapes. We also quantitatively evaluate the shape reconstructed from the medial axis obtained by HJS.
URI: http://hdl.handle.net/10553/112598
ISSN: 2105-1232
DOI: 10.5201/ipol.2021.296
Source: Image Processing On Line [ISSN 2105-1232], n. 11, p. 18-36
Appears in Collections:Artículos
Adobe PDF (1,12 MB)
Show full item record

Page view(s)

32
checked on May 15, 2022

Download(s)

14
checked on May 15, 2022

Google ScholarTM

Check

Altmetric


Share



Export metadata



Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.