Modelling Numerical and Spatial Uncertainty in GrayscaleImage Capture Using Fuzzy Set Theory | Zendy

Mike Nachtegae | Zendy; Peter Sussner | Zendy; Tom Mélange | Zendy; Etienne Kerre | Zendy

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Modelling Numerical and Spatial Uncertainty in GrayscaleImage Capture Using Fuzzy Set Theory

Author(s) -

Mike Nachtegae,

Peter Sussner,

Tom Mélange,

Etienne Kerre

Publication year - 2009

Publication title -

journal of advanced computational intelligence and intelligent informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.172

H-Index - 20

eISSN - 1343-0130

pISSN - 1883-8014

DOI - 10.20965/jaciii.2009.p0529

Subject(s) - grayscale , computer science , interval (graph theory) , fuzzy set , image (mathematics) , set (abstract data type) , fuzzy logic , object (grammar) , artificial intelligence , uncertainty analysis , data mining , possibility theory , position (finance) , algorithm , computer vision , mathematics , simulation , programming language , finance , combinatorics , economics

In this paper, we will discuss interval-valued and intuitionistic fuzzy sets as a model for grayscale images, taking into account the uncertainty regarding the measured grayscale values, which in some cases is also related to the uncertainty regarding the spatial position of an object in an image. We will demonstrate the practical potential of this image model by introducing an interval-valued morphological theory and by illustrating its application with some examples. The results show that the uncertainty that is present during the image capture not only can be modelled, but can also be propagated such that the information regarding the uncertainty is never lost.

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