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Fast adaptive threshold for the Canny edge detector

The Canny edge detector is widely used in computer vision to locate sharp intensity changes and find object boundaries in an image. The Canny edge detector removes the weak edges by hysteresis threshold and has difficulty to find upper and low thresholds with unimodal gradient magnitude distributions. In this paper, an algorithm, based on finding edge region and background region in the gradient magnitude histogram plot, is proposed which is capable of performing hysteresis threshold fast and adaptively. Its effectiveness is demonstrated on a variety of images, showing its successful application to Canny edge detector. The results of the fast adaptive threshold Canny edge detector we presented are better than the results of Canny edge detector with fixed threshold. Further tests are carried out on all sorts of real data using our method to select thresholds and get the good results. These demonstrate that the proposed algorithm is capable of performing hysteresis threshold fast and adaptively, often better than fixed upper and low threshold Canny edge detector that are run for comparison.