Fuzzy inferring mathematical morphology and optical implementation

Liren Liu

doi:10.1117/1.600975

1 October 1996 Fuzzy inferring mathematical morphology and optical implementation

Liren Liu

Optical Engineering, Vol. 35, Issue 10, (October 1996). https://doi.org/10.1117/1.600975

Fuzzification is introduced into gray-scale mathematical morphology by using two-input one-output fuzzy rule-based inference systems. The fuzzy inferring dilation or erosion is defined from the approximate reasoning of the two consequences of a dilation or an erosion and an extended rank-order operation. The fuzzy inference systems with numbers of rules and fuzzy membership functions are further reduced to a simple fuzzy system formulated by only an exponential two-input oneoutput function. Such a one-function fuzzy inference system is able to approach complex fuzzy inference systems by using two specified parameters within it—a proportion to characterize the fuzzy degree and an exponent to depict the nonlinearity in the inferring. The proposed fuzzy inferring morphological operators tend to keep the object details comparable to the structuring element and to smooth the conventional morphological operations. Based on digital area coding of a gray-scale image, incoherently optical correlation for neighboring connection, and optical thresholding for rank-order operations, a fuzzy inference system can be realized optically in parallel.

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Liren Liu "Fuzzy inferring mathematical morphology and optical implementation," Optical Engineering 35(10), (1 October 1996). https://doi.org/10.1117/1.600975

Published: 1 October 1996

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KEYWORDS

Fuzzy logic

Fuzzy systems

Mathematical morphology

Rule based systems

Optical engineering

Image filtering

Binary data

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