Every computer vision level crawl with uncertainty, what makes its management a significant problem to be considered and solved when trying for automated systems for scene analysis and interpretation. This is why fuzzy set theory and fuzzy logic is making many inroads into the handling of uncertainty in various aspects of image processing and computer vision.
The growth within the use of fuzzy set theory in computer vision is keeping pace with the use of more complex algorithms addressed to solve problems arisen from image vagueness management.
Due to the natural linguistic capabilities of high-level computer vision, it is a very appropriate place for applying fuzzy sets. Moreover, scene description, i.e., the language-based representation of regions and their relationships, for either humans or higher automated reasoning provides an excellent opportunity. With this overview we want to address the various aspects of image processing and analysis problems where the theory of fuzzy sets has so far been applied. On the other hand, we will discuss the possibility of making fusion of the merits of fuzzy set theory, neural networks theory and genetic algorithms for improved performance. Finally a list of representative references is also provided.
@article{urn:eudml:doc:39249,
title = {Fuzzy sets in computer vision: an overview.},
journal = {Mathware and Soft Computing},
volume = {10},
year = {2003},
pages = {71-83},
zbl = {1086.68622},
language = {en},
url = {http://dml.mathdoc.fr/item/urn:eudml:doc:39249}
}
Sobrevilla, Pilar; Montseny, Eduard. Fuzzy sets in computer vision: an overview.. Mathware and Soft Computing, Tome 10 (2003) pp. 71-83. http://gdmltest.u-ga.fr/item/urn:eudml:doc:39249/