Sketch Feature Extraction Through Learning Fuzzy Inference Rules with a Neural Network


The Transactions of the Korea Information Processing Society (1994 ~ 2000), Vol. 5, No. 4, pp. 1066-1073, Apr. 1998
10.3745/KIPSTE.1998.5.4.1066,   PDF Download:

Abstract

In this paper, we propose a new efficient operator named DBAH (difference between arithmetic mean and harmonic mean) and a technique for extracting sketch features through learning fuzzy inference rules with a neural network. The DBAH operator provide some advantages; sensitivity dependence on local intensities and insensitivity on small rates of intensity change in very dark regions. Also, the proposed fuzzy reasoning technique by a neural network has a good performance in extracting sketch features without human intervention.


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Cite this article
[IEEE Style]
C. S. Mok, "Sketch Feature Extraction Through Learning Fuzzy Inference Rules with a Neural Network," The Transactions of the Korea Information Processing Society (1994 ~ 2000), vol. 5, no. 4, pp. 1066-1073, 1998. DOI: 10.3745/KIPSTE.1998.5.4.1066.

[ACM Style]
Cho Sung Mok. 1998. Sketch Feature Extraction Through Learning Fuzzy Inference Rules with a Neural Network. The Transactions of the Korea Information Processing Society (1994 ~ 2000), 5, 4, (1998), 1066-1073. DOI: 10.3745/KIPSTE.1998.5.4.1066.