A statistical approach to multi-scale edge detection

We propose a statistical approach to combining edge cues at multiple scales using data driven probability distributions. These distributions are learnt on the Sowerby and South Florida datasets which include the ground truth positions of edges. We evaluate our results using Chernoff information and conditional entropy. Our results demonstrate the effectiveness of multi-scale processing and validate previous heuristics such as coarse-to-fine edge tracking. Keywords—Bayesian inference, edge detection, multi-scale processing, empirical evaluation.