A Three-Module Strategy for Edge Detection

This paper presents a three-module strategy for edge detection. The first and the last module involve well-known methods: the first module is a parallel process computing local edge strength and direction while the last module is a sequential process following edges. The originality of the overall method resides in the intermediate module, seen as a generalization of the nonmaximum deletion algorithm. The role of this module is twofold: it enables one to postpone some deletion to the last module where contextual information is available and it transmits the local edge direction in order to guide the contour following. In addition, a new postprocessing called learning edges is proposed as a refinement of the method. The binary edge images extracted from various gray-level images illustrate the power of the pro-