Robust Edge Detection

Abstract

A robust edge-detection algorithm which performs equally under a wide variety of noisy situations and a broad range of edges is described. The algorithm is executed in three phases. In phase 1, the step and linear edges are detected from the noise-corrupted image using a statistical classification technique. In phase 2, all the thin-line edges (i.e. which are lines less than two pixels wide) are detected by a supplementary technique since these edges cannot be detected simultaneously with the other step and linear edges. In phase 3, the spurious edge elements are suppressed and the isolated missing edge elements are interpolated using a number of hypothesized edge-segments. Finally some experimental results are provided to illustrate the success of the algorithm.<>