I. Zafar, B.S. Acar, and E.A. Edirisinghe (UK)
Vehicle MMR, Contourlet Transform, Feature Matching,multiresolution image analysis
With the increased importance of security, the possible use of vehicle Make & Model Recognition (MMR) systems to provide complimentary functionality to Automatic Number Plate Recognition (ANPR) systems has been widely accepted. As a result a number of vehicle MMR algorithms 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 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 at significantly lower computational complexity due to the exclusive use of directional sub-bands most significant to images consisting of vehicles.
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