Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

3D point of interest detection via spectral irregularity diffusion

Song, Ran, Liu, Yonghuai, Martin, Ralph Robert and Rosin, Paul L. 2013. 3D point of interest detection via spectral irregularity diffusion. The Visual Computer 29 (6-8) , pp. 695-705. 10.1007/s00371-013-0806-4

[img]
Preview
PDF - Accepted Post-Print Version
Download (643kB) | Preview

Abstract

This paper presents a method for detecting points of interest on 3D meshes. It comprises two major stages. In the first, we capture saliency in the spectral domain by detecting spectral irregularities of a mesh. Such saliency corresponds to the interesting portions of surface in the spatial domain. In the second stage, to transfer saliency information from the spectral domain to the spatial domain, we rely on spectral irregularity diffusion (SID) based on heat diffusion. SID captures not only the information about neighbourhoods of a given point in a multiscale manner, but also cues related to the global structure of a shape. It thus preserves information about both local and global saliency. We finally extract points of interest by looking for global and local maxima of the saliency map. We demonstrate the advantages of our proposed method using both visual and quantitative comparisons based on a publicly available benchmark.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Uncontrolled Keywords: Mesh saliency; Points of interest; Laplacian; Eigendecomposition
Additional Information: Pdf uploaded in accordance with publisher's policy at http://www.sherpa.ac.uk/romeo/issn/0178-2789/ (accessed 23/10/14) The final publication is available at Springer via http://dx.doi.org/10.1007/s00371-013-0806-4
Publisher: Springer
ISSN: 0178-2789
Date of First Compliant Deposit: 30 March 2016
Last Modified: 04 Jun 2017 08:08
URI: http://orca-mwe.cf.ac.uk/id/eprint/50740

Citation Data

Cited 2 times in Google Scholar. View in Google Scholar

Cited 14 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics