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

Eduard MontsenyPilar Sobrevilla — 1998

Mathware and Soft Computing

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...

Fuzzy sets in computer vision: an overview.

Pilar SobrevillaEduard Montseny — 2003

Mathware and Soft Computing

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...

Use of fuzzy techniques for detection of multiple sclerosis small lesions.

F. Xavier AymerichPilar SobrevillaJaume GiliEduard Montseny — 1998

Mathware and Soft Computing

This work shows an application of algorithms in which fuzzy techniques are used. It is focused on the automation of image analysis for use with a non-invasive technique, as magnetic resonance, in multiple sclerosis patients, and specifically in detection of the smallest lesions. The typical uncertainty in the definition of these lesions lead us to consider that a fuzzy approach is a good solution to the problem. The design of the algorithm is based on the definition of a rule set, which...

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