Improved VEDA and Unwanted Edge Removal approaches for License Plate Detection

Edge detection is probably the most widely used operations in image analysis, and there are probably more algorithms within the literature for enhancing and detecting edges than any other detection approaches. This is due to the fact that edges establish the outline relevant to an object. An edge will be the boundary between a desire and to discover the background, and indicates the boundary between overlapping objects. Recently, license plate edge detection technique is a vital part of vision navigation, which is the key method of intelligent vehicle assistance. The detection outcome is seriously affected through quality of noise and image. This means that in the event the edges in an image can be identified accurately, all of the objects can easily be located and basic properties such as area, perimeter, and shape can easily be measured. License plate edge detection is assist to analyze the direction of the plate extension and the specific location of obstacles, dimension and speed of obstacles among the road. Within the existing work, several typical edge detection operators like Prewitt, Sobel operators and in digital image processing are theoretically assessed, and are generally made use of for license edge detection. By evaluating the existing simulation results of license edge detection, the higher quality road test results might be gained when using Sobel and Prewitt operator[1].Existing work doesn’t support if the input image is noisy image. In our proposed work we implemented Adaptive median filter for edge detection algorithms in order to eliminate the noisy. In our proposed work we also introduce improved edge detection ULEA and VEDA methods which gives the higher quality result in the presence of noise. The effectiveness of the improved process is demonstrated experimentally. The current work mainly concentrates on the study of different edge detection techniques and analysis of there relative performances.