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We proposed fuzzy inference schemes to address the changes of the lighting environment problems: the illumination of the images captured from camera installed on a moving vehicle also varies from frame to frame. First, the input image is checked with a fuzzy inference method to evaluate the illu mination conditions in order to apply appropriate preprocess ing operations to get a better result. To overcome the effects caused by vehicle speed and changes in direction, a fuzzy infe rence method was again used to select an adapted detection window to increase the throughput rate. The Adaboost classifi er was employed to detect the road sign candidates from an image and the support vector machine technique was employed to recognize the content of the detected road sign. The manda tory and warning road traffic signs are the processing targets in this research. The proposed system can detect and recognize road signs correctly from the captured image, and not only overcome problems such as low illumination, viewpoint rota tion, partial occlusion and rich red color around the road sign, but also reach a high recognition rate and processing perfor mance.

Hard cover, 752 pagesPublisher InTech

This paper investigates and simulates a Coloured Stochastic Petri Nets model for depth evaluation intrusion detection. Network attack behaviors are very complexity sometimes, it is difficult to capture all of them. In this paper, we could realize what them happened with analyzing and simulating an intrusion. The experimental results demonstrated that the CSPN model approach was an efficient and helpful to evaluate an intrusion detection system.