Titlebook: Applying Soft Computing in Defining Spatial Relations; Pascal Matsakis,Les M. Sztandera Book 2002 Springer-Verlag Berlin Heidelberg 2002 C

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Mathematical Morphology and Spatial Relationships: Quantitative, Semi-Quantitative and Symbolic Setqualitative one, for reasoning in a logical framework about space. This is made possible thanks to the strong algebraic structure of mathematical morphology, that finds equivalents in set theoretical terms, fuzzy operations and logical expressions.

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,In the Face of Francis I’s Foreign policy,fuzzy spatial relations in modeling movement behavior primarily associated with foraging is demonstrated. It is shown that spatially explicit ecological modeling is a complex domain rich in the potential for intelligent applications using fuzzy spatial relations

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https://doi.org/10.1007/978-3-030-79860-4iness of approximate linguistic terms. Then a fuzzy set model of three approximate linguistic terms, ‘a little bit,’ ‘somewhat,’ and ‘nearly completely,’ is presented. A discussion of possible further research is followed by a summary at the end of the chapter.

ORBIT

Fuzzifying Spatial Relations, formalisms have been developed that deal with space on the basis of relations between objects. Although most approaches provide some algorithms to reason about such relations, they usually do not make any attempt to address questions like how to handle imprecision in spatial relations or how to com

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