Fuzzy reasoning scheme for edge detection using local edge information based on Renyi´s entropy

In this paper we present a robust approach to edge detection using fuzzy reasoning. In order to implement the fuzzy inference system a novel entropic local edge information measure is introduced based on Renyi´s a-order entropy. The calculation of the proposed measure is carried out using a parametric classification scheme based on local statistics. By suitable tuning its parameter, the local edge information measure is capable of preserving edge information while exhibiting high immunity to noise. The proposed fuzzy system is able to successfully detect edges in images corrupted by noise. The effectiveness and the robustness of the new method are demonstrated by applying our algorithm to noisy images. Performance assessment is based on comparison of the results derived using the proposed method with those obtained using various existing edge-detection algorithms.