Testing And Refinement Of The Edge Of The Study

Edge is one of the most fundamental and significant features of image. Edge detection is always one of the most classical studying projects of computer vision and image processing field. The first step of image analysis and understanding is edge detection. The goal of edge detection is to recover information about shapes and reflectance or transmittance in image. It is one of the fundamental steps in image processing, image analysis, image pattern recognition, computer vision, as well as in human vision. The correctness and reliability of its results affect directly the comprehension machine system made for objective world. This thesis summarizes the definition and algorithms of edge detection, together with its developments. In the thesis, we introduce several classical edge detection algorithms and edge thinning algorithms mostly, and then a new edge detection algorithm for color image and a new edge thinning algorithm are proposed.Edge detection is still a difficult problem for color image. In this paper, in order to carry out the edge detection on color image much better, a new edge detection algorithm is proposed. This algorithm is found by separating hue, saturation and value information of color image and analyzing the problems of gradient algorithm. It is based on computing the hue distance, saturation distance and value distance respectively, and then the sum of the three distances is taken as the final color distance. The feature of the algorithm is that the edge pixel is directly extracted in a local window. The experimental results show that this method can get true edge and the edge possesses good continuity.The traditional image edge detection can only get very wide edges usually, which makes it difficult for image processing in the future. In this article, we present a new edge thinning algorithm. By analyzing the image edge character and the insufficient of the classical edge detectors, we can thin the edge by Prewitt operator. The experimental results show that the method not only can preserve the origin edge information, but also give us acceptable thin edges.