Neural Edge Detector - A Good Mimic of Conventional One Yet Robuster against Noise

Authors:
Kenji Suzuki;Isao Horiba;Noboru Sugie
Affiliations:
-;-;-
Venue:
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
Year:
2001

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Abstract

This paper describes a new edge detector using a multilayer neural network, called a neural edge detector (NED), and its capacity for edge detection against noise. The NED is a supervised edge detector: the NED acquires the function of a desired edge detector through training. The experiments to acquire the functions of the conventional edge detectors were performed. The experimental results have demonstrated that the NED is a good mimic for the conventional edge detectors, moreover robuster against noise: the NED can detect the similar edges to those detected by the conventional edge detector; the NED is robuster against noise than the original one is.