Producing Fuzzy Inclusion and Entropy Measures

Bogiatzis, Athanasios C.; Papadopoulos, Basil K.

doi:10.1007/978-3-319-18275-9_3

Cited by 2 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [2,91], we studied the production of possible fuzzy subsethood and entropy measures according to Young's axioms and theorem. Let X be our universal set (a finite set), ∅ its complement, F(X) its power set and A, B members of X with corresponding membership grades m A (x) and m B (x).…”

Section: Fuzzy Subsethood and Entropy Measuresmentioning

confidence: 99%

Global Image Thresholding Adaptive Neuro-Fuzzy Inference System Trained with Fuzzy Inclusion and Entropy Measures

Bogiatzis

Papadopoulos

2019

Symmetry

View full text Add to dashboard Cite

Thresholding algorithms segment an image into two parts (foreground and background) by producing a binary version of our initial input. It is a complex procedure (due to the distinctive characteristics of each image) which often constitutes the initial step of other image processing or computer vision applications. Global techniques calculate a single threshold for the whole image while local techniques calculate a different threshold for each pixel based on specific attributes of its local area. In some of our previous work, we introduced some specific fuzzy inclusion and entropy measures which we efficiently managed to use on both global and local thresholding. The general method which we presented was an open and adaptable procedure, it was free of sensitivity or bias parameters and it involved image classification, mathematical functions, a fuzzy symmetrical triangular number and some criteria of choosing between two possible thresholds. Here, we continue this research and try to avoid all these by automatically connecting our measures with the wanted threshold using some Artificial Neural Network (ANN). Using an ANN in image segmentation is not uncommon especially in the domain of medical images. However, our proposition involves the use of an Adaptive Neuro-Fuzzy Inference System (ANFIS) which means that all we need is a proper database. It is a simple and immediate method which could provide researchers with an alternative approach to the thresholding problem considering that they probably have at their disposal some appropriate and specialized data.

show abstract

Section: Fuzzy Subsethood and Entropy Measuresmentioning

confidence: 99%