Robust edge detection

Edge detection is an important issue in computer vision and image understanding systems. Most conventional techniques have assumed Gaussian noise, and their performance could decrease with the departure of noise distribution from normality. In this paper, we present an edge detection approach using robust statistics. The edge structure is first detected by a robust one-way design model, and then localized by a robust contrast test. Finally, hysteresis thresholding is applied to yield the output edge map. To evaluate its performance, experiments were carried out on synthetic and real images corrupted with both Gaussian noise and a mixture of Gaussian and impulsive noise. The results show that the performance of the proposed edge detector is stable and reliable under severe impulsive noise conditions.