Study Of Fault Detection Methods Based On Image Processing

Fault detection and diagnosis by imaging technology has become a common approach in non-destructive testing applications. As in many other image processing applications, gray level thresholding methods are widely used to locate the defect position. Because of the variety of defect signatures and that of imaging models, it is hard to design a universal thresholding algorithm. In this paper, a new thresholding method based on multiple classifier fusion is proposed. It uses fuzzy integral to integrate the outputs of multiple thresholding algorithms. The proposed method differs from the traditional classifier fusion methods in that its fusion decision not only depending on the individual classifier's output, but also incorporating the uncertainty of the classifier's ability to make decision. The classifier's decision making ability is represented by fuzzy measurement, which can be interpreted as the importance of the individual classifier's local decision to the final decision. The optimal correspondence between subjective expectation and objective evidence is achieved by fusing the features from multiple sources. Evaluated by a set of hand segmented non-destructive images, the averaging validation index shows that the proposed method takes advantages over individual thresholding algorithm, also over majority voting and averaging fusion methods.The cognition and recognition of many landscapes and images are extremely intuitionistic and oversimplified for human eyes, but it is still another thing for the computers. In this paper, a novel image segmentation algorithm based on the biology vision lateral inhibition model is proposed. Each pixel in the image is excited by the brightness information of itself, inhibited by that of the pixels around it, and inhibits them reversely at the same time. The lateral inhibition network makes the excitation and the inhibition of every pixel in the image multiply weights respectively and sums up them as the new intensity of that pixel. The new image is filtered and an inhibited image is obtained. Because of the variety of the image content, the weighting parameters of the lateral network play a vital role in the segmentation process. In this paper, evolutionary strategy is used to search the optimal weighting parameters of the lateral inhibition model. The optimization object function is a multiple criteria combination of the image histogram, clustering and entropy information. The experimental results show that the image segmentation method based on the biology vision model is effective and efficient.