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

Mesh saliency via spectral processing

Song, Ran, Liu, Yonghuai, Martin, Ralph R. and Rosin, Paul L. 2014. Mesh saliency via spectral processing. ACM Transactions on Graphics 33 (1) , -. 10.1145/2530691

[img]
Preview
PDF - Accepted Post-Print Version
Download (879kB) | Preview

Abstract

We propose a novel method for detecting mesh saliency, a perceptually-based measure of the importance of a local region on a 3D surface mesh. Our method incorporates global considerations by making use of spectral attributes of the mesh, unlike most existing methods which are typically based on local geometric cues. We first consider the properties of the log-Laplacian spectrum of the mesh. Those frequencies which show differences from expected behaviour capture saliency in the frequency domain. Information about these frequencies is considered in the spatial domain at multiple spatial scales to localise the salient features and give the final salient areas. The effectiveness and robustness of our approach are demonstrated by comparisons to previous approaches on a range of test models. The benefits of the proposed method are further evaluated in applications such as mesh simplification, mesh segmentation, and scan integration, where we show how incorporating mesh saliency can provide improved results.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Additional Information: Pdf uploaded in accordance with the publisher’s policy at http://www.sherpa.ac.uk/romeo/issn/0730-0301/ (accessed 31/07/2014) © ACM, 2014. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Transactions on Graphics, {VOL 33, ISSUE 1, 2014} http://doi.acm.org/10.1145/2530691
Publisher: Association for Computing Machinery (ACM)
ISSN: 0730-0301
Funders: Welsh Government
Date of First Compliant Deposit: 30 March 2016
Last Modified: 13 Mar 2020 15:33
URI: http://orca.cf.ac.uk/id/eprint/57386

Citation Data

Cited 83 times in Google Scholar. View in Google Scholar

Cited 70 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