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Title: Localised contourlet features in vehicle make and model recognition
Authors: Zafar, Iffat
Edirisinghe, Eran A.
Acar, B. Serpil
Keywords: Vehicle MMR
Contourlet transform
Feature matching
Multi-resolution image analysis
Issue Date: 2009
Publisher: © 2009 SPIE
Citation: ZAFAR, I., EDIRISINGHE, E.A. and ACAR, B.S. Localised contourlet features in vehicle make and model recognition. IN: Niel, K.S. and Fofi, D.(eds) Image Processing: Machine Vision Applications II, Proc. of SPIE-IS&T Electronic Imaging, 7251, 725105, 10pp.
Abstract: Automatic vehicle Make and Model Recognition (MMR) systems provide useful performance enhancements to vehicle recognitions systems that are solely based on Automatic Number Plate Recognition (ANPR) systems. Several vehicle MMR systems have been proposed in literature. In parallel to this, the usefulness of multi-resolution based feature analysis techniques leading to efficient object classification algorithms have received close attention from the research community. To this effect, Contourlet transforms that can provide an efficient directional multi-resolution image representation has recently been introduced. Already an attempt has been made in literature to use Curvelet/Contourlet transforms in vehicle MMR. In this paper we propose a novel localized feature detection method in Contourlet transform domain that is capable of increasing the classification rates up to 4%, as compared to the previously proposed Contourlet based vehicle MMR approach in which the features are non-localized and thus results in sub-optimal classification. Further we show that the proposed algorithm can achieve the increased classification accuracy of 96% at significantly lower computational complexity due to the use of Two Dimensional Linear Discriminant Analysis (2DLDA) for dimensionality reduction by preserving the features with high between-class variance and low inter-class variance.
Description: Copyright 2009 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic electronic or print 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. This paper can also be found at: http://dx.doi.org/10.1117/12.805878
Version: Published
DOI: 10.1117/12.805878
URI: https://dspace.lboro.ac.uk/2134/6498
Appears in Collections:Conference Papers and Presentations (Computer Science)

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