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Affine adaptation of local image features using the Hessian Matrix

Lakemond, Ruan, Fookes, Clinton B., & Sridharan, Sridha (2009) Affine adaptation of local image features using the Hessian Matrix. In IEEE International Conference On Advanced Video and Signal Based Surveillance, 2-4 September 2009, Genoa, Italy. (In Press)

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Abstract

Local feature detectors that make use of derivative based saliency functions to locate points of interest typically require adaptation processes after initial detection in order to achieve scale and affine covariance. Affine adaptation methods have previously been proposed that make use of the second moment matrix to iteratively estimate the affine shape of local image regions. This paper shows that it is possible to use the Hessian matrix to estimate local affine shape in a similar fashion to the second moment matrix. The Hessian matrix requires significantly less computation effort to compute than the second moment matrix, allowing more efficient affine adaptation. It may also be more convenient to use the Hessian matrix, for example, when the Determinant of Hessian detector is used. Experimental evaluation shows that the Hessian matrix is very effective in increasing the efficiency of blob detectors such as the Determinant of Hessian detector, but less effective in combination with the Harris corner detector.

Impact and interest:

6 citations in Scopus
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ID Code: 20506
Item Type: Conference Paper
Additional URLs:
Keywords: Affine Adaptation, Local Image Features, Hessian Matrix, Scale and Affine Covariance, Harris Detector
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Image Processing (080106)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Institutes > Information Security Institute
Past > Schools > School of Engineering Systems
Copyright Owner: Copyright 2009 IEEE
Deposited On: 19 May 2009 07:55
Last Modified: 29 Feb 2012 23:54

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