Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/32224
Title: A multi-scale bilateral structure tensor based corner detector
Authors: Zhang, L
Zhang, L 
Zhang, D 
Issue Date: 2010
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2010, v. 5995 LNCS, no. PART 2, p. 618-627
Abstract: In this paper, a novel multi-scale nonlinear structure tensor based corner detection algorithm is proposed to improve effectively the classical Harris corner detector. By considering both the spatial and gradient distances of neighboring pixels, a nonlinear bilateral structure tensor is constructed to examine the image local pattern. It can be seen that the linear structure tensor used in the original Harris corner detector is a special case of the proposed bilateral one by considering only the spatial distance. Moreover, a multi-scale filtering scheme is developed to tell the trivial structures from true corners based on their different characteristics in multiple scales. The comparison between the proposed approach and four representative and state-of-the-art corner detectors shows that our method has much better performance in terms of both detection rate and localization accuracy.
Keywords: Bilateral structure tensor
Corner detector
Harris
Publisher: Springer
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
ISBN: 3642123031
9783642123030
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-642-12304-7_58
Description: 9th Asian Conference on Computer Vision, ACCV 2009, Xi'an, 23-27 September 2009
Appears in Collections:Conference Paper

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

17
Last Week
0
Last month
0
Citations as of Sep 7, 2020

WEB OF SCIENCETM
Citations

4
Last Week
0
Last month
0
Citations as of Sep 17, 2020

Page view(s)

137
Last Week
0
Last month
Citations as of Sep 20, 2020

Google ScholarTM

Check

Altmetric


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