Vehicle license plate super-resolution using soft learning prior
Multimedia Tools and Applications
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This paper proposes the performance of a new algorithm for vehicles recognition 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. This paper presents the application of three different types of classifiers to the vehicle recognition. They include of Support vector machine (one versus one), k nearest-neighbor and Support vector machine (one versus all). 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 performed numerical experiments for vehicles recognition have shown the superiority of curvelet and standard deviation preprocessing, which are associated with the Support vector machine structure (one versus one). The results of this test show, the right recognition rate of vehicle's model in this recognition system, at the time of using total scales information numbers 3&4 curvelet coefficients matrix is about 99%. 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, RENAULT55 and Peykan. We've examined 230 pictures as our train data set and 70 pictures as our test data set.