Neuro-fuzzy (NF) systems are very suitable tools to deal with uncertainty encountered in the process of extracting useful information from images. We present a novel adaptive neuro-fuzzy inference system (ANFIS) for edge detection in digital images. The internal parameters of the proposed ANFIS edge detector are optimized by training using very simple artificial images. The edges are directly determined by ANFIS network. The proposed ANFIS edge detector is tested on popular images having different image properties and also compared with popular edge detectors from the literature. Experimental results show that the proposed ANFIS edge detector exhibits much better performance than the competing operators and may efficiently be used for the detection of edges in digital images.
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