Discontinuity detection and thresholding -- A stochastic approach (Q2365843)

We study discontinuity detection and thresholding using a stochastic approach. We discuss a general form of detectors, which includes a number of well known detectors as special cases. Thresholding is indispensable to eliminate spurious responses from the detection process. We proposes a Weighted Thresholding, which is designed to cope with a variety of anomalies. Our analysis and experimental results on real images show that intelligent thresholding methods can make a significant difference for discontinuity detection.