Fast SUSAN edge detector by adapting step-size

Abstract

The SUSAN algorithm is a popular edge detector for its high localization precision, noise-robustness and good connectivity at junctions. Straightforward implementation of SUSAN edge detector is performed by sliding a circular mask pixel by pixel on an image. For each pixel in the raster scan order, neighbor pixels in the circular mask around it are taken and used to compute the USAN area, according to which whether the current pixel is an edge point or not is judged. However, redundant computation exists in the direct implementation of SUSAN edge detector because of the pixel-by-pixel sliding way of the circular mask, which makes it somewhat inefficient. In this paper, we propose a fast implementation of SUSAN edge detector to decrease the redundancy. Instead of sliding the mask by one pixel every time, we propose an adaptive type of raster scan referred to as adaptive sliding mask which adjusts the sliding step-size adaptively according to USAN area. Experiments show that the proposed method can speed up the SUSAN edge detector effectively without spoiling its detection performance.