Robust Consensus Based Edge Detection

Abstract

We present a new robust algorithm for edge detection. The algorithm detects both roof and step type edges. A pixel is declared as an edge pixel if there is a consensus between different processes that try to determine if the pixel lies on a discontinuity. We use robust estimation methods to estimate local fits to windows in the pixel′s neighborhood and accumulate votes from each fit. The use of robust estimators enables us to transform any window possibly containing a discontinuity to a binary window containing a step edge in the location of the discontinuity. We then employ conventional methods to detect this step edge. We show experimental results on simulated edges and synthetic images with varying Gaussian and random noise levels and analyze the probability of detection. The algorithm is also applied to several real intensity and range images and it is shown to perform well. A comparison with the Canny edge detector is given when applicable.