Car license plate recognition is addressed in this paper. Given the development of intelligent transportation systems, it is absolutely essential to implement a strong license plate recognition system. Efforts were made to put forward a novel reliable method for car license plate recognition in Iran. Each license plate recognition system comprises three main parts. The first part is the license plate detection stage. The blue color feature of the license plate margin along with Scale-Invariant Feature Transform (SIFT) algorithm were used for this purpose. The accuracy of the presented method over the database was approximately 90% in less than a second. License plate morphological features were utilized upon character segmentation. Using these features, areas with sizes close to that of the characters of a license plate may be searched. The accuracy of this method was almost 95%. A probabilistic neural network together with a Support Vector Machine (SVM) was employed at the character recognition stage. For this stage, an accuracy of nearly 97% in 55 milliseconds for each license plate was achieved. تفاصيل المقالة

In this paper, a new method is presented for the detection of defects in random textures. In the training stage, the feature vectors of the normal textures’ images are extracted by using the optimal response of Gabor wavelet filters, and their probability density is estimated by means of the Gaussian Mixture Model (GMM). In the testing stage, similar to the previous stage,at first, the feature vectors corresponding to local neighborhoods of each pixel of the image under inspection are extracted. Then, by computing the likelihood of the test image’s feature vectors’ belonging to the parameters of the GMM, they are compared with a threshold value. Finally, the defective regions are localized in a defect map. The proposed algorithm was evaluated on a set of grayscale ceramic tile images with random textures. The simulations indicate that in comparison with the previous methods, the proposed algorithm enjoys an acceptable computational volume and accuracy in the detection of texture defects. تفاصيل المقالة