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Hessian-based affine adaptation of salient local image features

Lakemond, Ruan, Sridharan, Sridha, & Fookes, Clinton B. (2012) Hessian-based affine adaptation of salient local image features. Journal of Mathematical Imaging and Vision, 44(2), pp. 150-167.

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Abstract

Affine covariant local image features are a powerful tool for many applications, including matching and calibrating wide baseline images. Local feature extractors that use a saliency map to locate features require adaptation processes in order to extract affine covariant features. The most effective extractors make use of the second moment matrix (SMM) to iteratively estimate the affine shape of local image regions. This paper shows that the Hessian matrix can be used to estimate local affine shape in a similar fashion to the SMM. The Hessian matrix requires significantly less computation effort than the SMM, allowing more efficient affine adaptation. Experimental results indicate that using the Hessian matrix in conjunction with a feature extractor that selects features in regions with high second order gradients delivers equivalent quality correspondences in less than 17% of the processing time, compared to the same extractor using the SMM.

Impact and interest:

2 citations in Scopus
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0 citations in Web of Science®

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ID Code: 50951
Item Type: Journal Article
Keywords: Local image features, Affine adaption, Wide baseline matching, Shape estimation
DOI: 10.1007/s10851-011-0317-8
ISSN: 0924-9907
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Divisions: Current > Schools > School of Chemistry, Physics & Mechanical Engineering
Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2012 Springer
Copyright Statement: The original publication is available at SpringerLink http://www.springerlink.com
Deposited On: 19 Jun 2012 08:31
Last Modified: 20 Jun 2013 17:42

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