Abstract
This paper proposes the performance of a new algorithm for recognition vehicle’s system. This recognition system is based on extracted features on the performance of image’s curvelet transform & achieving standard deviation of curvelet coefficients matrix in different scales & various orientations. The curvelet transform is a multiscale transform with frame elements indexed by location, scale and orientation parameters, and have time-frequency localization properties of wavelets but also shows a very high degree of directionality and anisotropy.The used classifier in this paper is called k nearest-neighbor.In addition, the proposed recognition system is obtained by using different scales information as feature vector. So, we could clarify the most important scales in aspect of having useful information. The results of this test show, the right recognition rate of vehicle’s model in this recognition system, at the time of using the total scales information numbers 2,3&4 curvelet coefficients matrix is about 95%. We’ve gathered a data set that includes of 300 images from 5 different classes of vehicles. These 5 classes of vehicles include of: PEUGEOT 206, PEUGEOT 405, Pride, RENULT5 and Peykan. We’ve examined 230 pictures as our train data set and 70 pictures as our test data set.
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Kazemi, F.M., Pourreza, H.R., Moravejian, R., Kazemi, E.M. (2008). Vehicle Recognition Using Curvelet Transform and Thresholding. In: Sobh, T. (eds) Advances in Computer and Information Sciences and Engineering. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-8741-7_26
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DOI: https://doi.org/10.1007/978-1-4020-8741-7_26
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