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首页> 外文会议>Visualization, imaging, and image processing >VEHICLE MAKE AND MODEL IDENTIFICTION IN CONTOURLET DOMAIN USING LOCALISED DIRECTIONAL FEATURE MAPS
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VEHICLE MAKE AND MODEL IDENTIFICTION IN CONTOURLET DOMAIN USING LOCALISED DIRECTIONAL FEATURE MAPS

机译：基于局部方向特征图的Contourlet域中的车辆制造和模型识别

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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.
机译：随着安全重要性的日益提高，可能广泛使用车辆制造和模型识别（MMR）系统来为自动车牌识别（ANPR）系统提供补充功能。结果，在文献中已经提出了许多车辆MMR算法。与此并行的是，基于多分辨率的特征分析技术的实用性导致了高效的对象分类算法，这引起了研究界的密切关注。为此，最近引入了可以提供有效的定向多分辨率图像表示的Contourlet变换。文献中已经尝试在车辆MMR中使用Curvelet变换。在本文中，我们提出了一种新的Contourlet变换域中的局部特征检测方法，与先前提出的基于Contourlet的车辆MMR方法相比，该方法将特征非局部化，从而能够将分类率提高多达4％。在次优分类中。进一步地，我们表明，由于排他性地使用了对由车辆组成的图像最重要的定向子带，因此该算法可以以较低的计算复杂度实现更高的分类精度。

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