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Abstract-Traffic Sign Detection and Recognition (TSDR) has many features help the driver in improving the safety and comfort, today it is widely used in the automotive manufacturing sector, a robust detection and recognition system a good solution for driver assistance systems, it can warn the driver and control or prohibit certain actions which significantly increase driving safety and comfort. This paper presents a study to design, implement and test a method of detection and recognition of road signs based on computer vision. The approach adopted in this work consists of two main modules: a detection module which is based on color segmentation and edge detection to identify areas of the scene may contain road signs and a recognition module based on the multilayer perceptrons whose role is to match the patterns detected with road signs corresponding visual information. The development of these two modules is performed using the C/C++ language and the OpenCV library. The tests are performed on a set of real images of traffic to show the performance of the system being developed.

In order to realize an identification system of traffic road signs in real time, the authors have developed a performed detection method of these signs in real time. This method, presented in this paper, is implemented in two modules. The first is a pre-processing module based on video stream processing technics and the second, based on the polygonal approximation digital curves, identifies areas that may contain road signs using the particularity of their colors and contours. All algorithms implemented in this work are developed under the programming language C / C ++ using OpenCV library. The results of tests, realized on real videos of the traffic signs, show the performance improvement of the method developed in this work in terms of rate and speed of detection.

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Nowadays, digital images compression requires more and more significant attention of researchers. Even when high data rates are available, image compression is necessary in order to reduce the memory used, as well the transmission cost. An ideal image compression system must yield high-quality compressed image with high compression ratio. In this article, a neural network is implemented for image compression using the feature of wavelet transform. The idea is that a back-propagation neural network can be trained to relate the image contents to its ideal compression method between two different wavelet transforms: orthogonal (Haar) and biorthogonal (bior4.4).