Comparison of spatial domain optimal trade-off maximum average correlation height (OT-MACH) filter with scale invariant feature transform (SIFT) using images with poor contrast and large illumination gradient

Gardezi, A, Qureshi, T, Alkandri, A, Young, R C D, Birch, P M and Chatwin, C R (2015) Comparison of spatial domain optimal trade-off maximum average correlation height (OT-MACH) filter with scale invariant feature transform (SIFT) using images with poor contrast and large illumination gradient. Published in: Casasent, David and Alam, Mohammad S, (eds.) Proceedings of SPIE Optical Pattern Recognition XXVI; Baltimore, Maryland, United States; 22–23 April 2015. 9477 a6. SPIE ISSN 0277-786X ISBN 9781628415933

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Abstract

A spatial domain optimal trade-off Maximum Average Correlation Height (OT-MACH) filter has been previously developed and shown to have advantages over frequency domain implementations in that it can be made locally adaptive to spatial variations in the input image background clutter and normalised for local intensity changes. In this paper we compare the performance of the spatial domain (SPOT-MACH) filter to the widely applied data driven technique known as the Scale Invariant Feature Transform (SIFT). The SPOT-MACH filter is shown to provide more robust recognition performance than the SIFT technique for demanding images such as scenes in which there are large illumination gradients. The SIFT method depends on reliable local edge-based feature detection over large regions of the image plane which is compromised in some of the demanding images we examined for this work. The disadvantage of the SPOTMACH filter is its numerically intensive nature since it is template based and is implemented in the spatial domain. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

Item Type: Conference Proceedings
Additional Information: Copyright (2015) Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Keywords: MACH filter, SIFT filter, correlation filter, invariant pattern recognition
Schools and Departments: School of Engineering and Informatics > Engineering and Design
Research Centres and Groups: Industrial Informatics and Signal Processing Research Group
Subjects: Q Science
T Technology
Related URLs:
Depositing User: Chris Chatwin
Date Deposited: 17 Jul 2015 07:51
Last Modified: 26 Jun 2017 13:40
URI: http://sro.sussex.ac.uk/id/eprint/55399

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