In this paper, the problem of edge detection is addressed using the first order statistics and automatic thresholding technique. The general idea of edge detection using the simple edge detectors such as gradient operators or second derivative operators is extended to the statistic domain. The statistical features are used to describe the relationship between the current pixel and its neighboring, then, the thresholding technique is employed to determine the edge of gray level image. The proposed method improves the accuracy of the edge detection and suppresses the impact of the noise on the results, while the edge has a good consistency. The proposed method is validated by performing a comparative study with respect to other existing techniques. The experimental segmentation results, on standard and textured images, highlight the effectiveness of the proposed method.
Key words: Thresholding, statistical features, first order statistics, noise, segmentation, edge detection, defect detection.
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