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.
This work was supported in part by the Ministry of Education, Science, Sports and Culture of Japan, by the Kayamori Foundation of Informational Science Advancement, and by the Okawa Foundation for Information and Telecommunications.
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Suzuki, K., Horiba, I., Sugie, N. (2001). Neural Edge Detector - A Good Mimic of Conventional One Yet Robuster against Noise. In: Mira, J., Prieto, A. (eds) Bio-Inspired Applications of Connectionism. IWANN 2001. Lecture Notes in Computer Science, vol 2085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45723-2_36
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DOI: https://doi.org/10.1007/3-540-45723-2_36
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