Application of fuzzy techniques to the design of algorithms in computer vision.

Montseny, Eduard, and Sobrevilla, Pilar. "Application of fuzzy techniques to the design of algorithms in computer vision.." Mathware and Soft Computing 5.2-3 (1998): 223-230. <http://eudml.org/doc/39137>.

@article{Montseny1998,
abstract = {In this paper a method for the design of algorithms is presented which use fuzzy techniques in order to achieve a better vagueness treatment. A base of rules will be developed in order to design the algorithms. Data fuzzification problem is solved by using probability density functions and probability distribution functions, whereas data analysis is set out associating, to each one of the analysis rules, a fuzzy set which will be obtained by applying an aggregation function which will be defined by using an OWA operator.The proposed design provides a solution to the data value fuzzification problem, which is a quite well solved problem for applied control algorithms, but, up to now, displayed great difficulties for vision ones.Moreover, the proposed data analysis method provides a solution for non intrinsic problems from vision algorithms.},
author = {Montseny, Eduard, Sobrevilla, Pilar},
journal = {Mathware and Soft Computing},
keywords = {Algoritmos de ordenador; Visión artificial; Procesamiento de imágenes; Información difusa; Algebra de operadores; computer vision; image processing; fuzzy techniques; aggregation functions; membership functions; OWA operators},
language = {eng},
number = {2-3},
pages = {223-230},
title = {Application of fuzzy techniques to the design of algorithms in computer vision.},
url = {http://eudml.org/doc/39137},
volume = {5},
year = {1998},
}

TY - JOUR
AU - Montseny, Eduard
AU - Sobrevilla, Pilar
TI - Application of fuzzy techniques to the design of algorithms in computer vision.
JO - Mathware and Soft Computing
PY - 1998
VL - 5
IS - 2-3
SP - 223
EP - 230
AB - In this paper a method for the design of algorithms is presented which use fuzzy techniques in order to achieve a better vagueness treatment. A base of rules will be developed in order to design the algorithms. Data fuzzification problem is solved by using probability density functions and probability distribution functions, whereas data analysis is set out associating, to each one of the analysis rules, a fuzzy set which will be obtained by applying an aggregation function which will be defined by using an OWA operator.The proposed design provides a solution to the data value fuzzification problem, which is a quite well solved problem for applied control algorithms, but, up to now, displayed great difficulties for vision ones.Moreover, the proposed data analysis method provides a solution for non intrinsic problems from vision algorithms.
LA - eng
KW - Algoritmos de ordenador; Visión artificial; Procesamiento de imágenes; Información difusa; Algebra de operadores; computer vision; image processing; fuzzy techniques; aggregation functions; membership functions; OWA operators
UR - http://eudml.org/doc/39137
ER -