New Method Based on Multi-Threshold of Edges Detection in Digital Images

Edges characterize object boundaries in image and are therefore useful for segmentation, registration, feature extraction, and identification of objects in a scene. Edges detection is used to classify, interpret and analyze the digital images in a various fields of applications such as robots, the sensitive applications in military, optical character recognition, infrared gait recognition, automatic target recognition, detection of video changes, real-time video surveillance, medical images, and scientific research images. There are different methods of edges detection in digital image. Each one of these methods is suited to a particular type of images. But most of these methods have some defects in the resulting quality. Decreasing of computation time is needed in most applications related to life time, especially with large size of images, which require more time for processing. Threshold is one of the powerful methods used for edge detection of image. In this paper, We propose a new method based on different Multi-Threshold values using Shannon entropy to solve the problem of the traditional methods. It is minimize the computation time. In addition to the high quality of output of edge image. Another benefit comes from easy implementation of this method.