Image Detection Based On Edge Detection And Segmentation Techniques

Edges of an image reflect the information of the image mostly. They contain the basic characteristics. Edge detection is a vital part of many image processing and pattern recognition systems. Typical areas of applications are image segmentation, stereo vision, and identification of objects as in automatic target recognition. It is one of the most important parts in image processing.The traditional edge detection methods can be divided into two categories:the time domain and the frequency domain. These methods are not very effective to the image edges extraction with noise. In this paper, an edge detection method based on self-learning and statistics is improved. Simulation result shows our approach can highly improve the connectivity and the accuracy of the image boundaries quickly and easily.Ideally, the methods discussed in the previous section should yield pixels lying only on edges. Edge connection is an important post-process technique of image edge extraction and image segmentation. Based on fuzzy logic, an effective algorithm of edge connection is proposed. Simulation result shows our approach can highly improve the connectivity of the extracted image boundaries without distortion. This work lays foundations of target detection, tracking, and recognition for photoelectric tracker and imaging radar. Therefore, the techniques discussed above have highly practical value.