Research On Image Edge Detection And Its Application

Digital image processing is increasingly being used for a wide range of applications and infiltrating to all areas of society. The edge feature of digital image is an important feature of the image. Digital image edge detection technology is the basis of image processing, computer vision, pattern recognition, which is widely used in image segmentation, motion detection, object tracking, face recognition and other fields. Thus, image edge detection is one of the hot research areas in image processing. Improving the accuracy of edge detection and exploring its practical applications are the important research contents of edge detection. The study is combined with three projects, "Research on Multi-spectral Image Registration","Automatic Fault Detection in Trouble of Moving Freight Car Detection System" and "Human Motion Capture System Based on Stereo Vision". The basic theory of edge detection method is researched in this paper. Edge detection technologies are applied in above three projects. The content studied of this thesis mainly includes the following several respects:Firstly, we systematically summarize the research achievements and the curruent situation of research related to image edge detection technology at home and abroad, and categorizes the edge detection technology and describes the classic gray image and color image edge detection methods. This paper introduces the applications of edge detection technology in the field of image processing, pattern recognition and computer vision, and prospects the development trend of image edge detection technology.Next, the traditional Canny edge detector is introduced in detail, the drawbacks on Canny detector are analysised in this thesis. An adaptive Canny edge detector based on histogram concavity analysis is proposed, it uses switching median filter to remove the impulsive noise before Gaussian filtering, and selects the optimal dual-threshold through histogram concavity analysis. The improved detector can efficiently remove the impulsive noise and automatically select the optimal dual-threshold.Then, in order to this extend the gray SUSAN edge detection method to the color image. A method of SUSAN edge detection method is presented to detect the edges of color image. This new method first convert the the RGB image into CIELab color space, and then use the color difference to calculate the size of Univalue Segment Assimilating Nucleus (USAN) area. The final edges are extracted by thresholding. This new method can effectively detect edges of color images compared with traditional methods, the detected edges more consistent with the human visual characteristics.Moreover, the theory of partial differential equation and its application in image processing are reviewed first, an improved Canny color edge detection method based on fast vectorial total variation (VTV) minimization denoising model and color difference Sobel operator is proposed, it uses fast vectorial total variation (VTV) minimization model to remove noise while preserving the image edges. Then calculates the color difference and direction in CIELAB color space, which is used for non-maximal suppression. Finally, the improved method extracts the edges by the double-threshold method. This detector can efficently detects the edges of color image. As to impulsive noise in the color image, we designed an improved Canny edge detector against impulsive noise based on CIELAB space.Finally, this thesis describes the basic principle and composition of the trouble of moving freight car detection system. According to several common faults of the TFDS system, it offers the classifying, analyzing in detail. Image edge detection technology is applied successively to the trouble of moving freight car detection system. An automatic detection method for loss of freight car center plate bolts based on edge location and match is presented.The algorithm firstly uses sobel edge detection to extract the ROI (region of interest) of center plate bolts.Then locates all possible faults in the ROI using feature point detection and verify the candidate fault by edge matching.The experimental results show that:The proposed algorithm can rapidly detect the trouble of freight car center plate bolts.