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Fusion based analysis of ophthalmologic image data

Jiří Jan, Radim Kolář, Libor Kubečka, Jan Odstrčilík, Jiří Gazárek (2011)

Kybernetika

The paper presents an overview of image analysis activities of the Brno DAR group in the medical application area of retinal imaging. Particularly, illumination correction and SNR enhancement by registered averaging as preprocessing steps are briefly described; further mono- and multimodal registration methods developed for specific types of ophthalmological images, and methods for segmentation of optical disc, retinal vessel tree and autofluorescence areas are presented. Finally, the designed methods...

Fuzzy Mathematical Programming approach for Solving Fuzzy Linear Fractional Programming Problem

Chinnadurai Veeramani, Muthukumar Sumathi (2014)

RAIRO - Operations Research - Recherche Opérationnelle

In this paper, a solution procedure is proposed to solve fuzzy linear fractional programming (FLFP) problem where cost of the objective function, the resources and the technological coefficients are triangular fuzzy numbers. Here, the FLFP problem is transformed into an equivalent deterministic multi-objective linear fractional programming (MOLFP) problem. By using Fuzzy Mathematical programming approach transformed MOLFP problem is reduced single objective linear programming (LP) problem. The proposed...

Fuzzy relation equation under a class of triangular norms: A survey and new results.

Antonio Di Nola, Witold Pedrycz, Salvatore Sessa, Wang Pei Zhuang (1984)

Stochastica

By substituting the classical lattice operator min of the unit real interval with a triangular norm of Schweizer and Sklar, the usual fuzzy relational equations theory of Sanchez can be generalized to wider theory of fuzzy equations. Considering a remarkable class of triangular norms, for such type of equations defined on finite sets, we characterize the upper an lower solutions.We also characterize the solutions posessing a minimal fuzziness measure of Yager valued with respect to a triangular...

Fuzzy relation equations under LSC and USC t-norms and their Boolean solutions.

Antonio Di Nola, Witold Pedrycz, Salvatore Sessa (1987)

Stochastica

This paper follows a companion paper (Stochastica 8 (1984), 99-145) in which we gave the state of the art of the theory of fuzzy relation equations under a special class of triangular norms. Here we continue this theory establishing new results under lower and upper semicontinuous triangular norms and surveying on the main theoretical results appeared in foregoing papers. Max-t fuzzy equations with Boolean solutions are recalled and studied. Many examples clarify the results established.

Fuzzy sets as set classes.

Ton Sales (1982)

Stochastica

Fuzzy sets have been studied in various forms. We now offer a presentation of fuzzy sets whereby they are conceived as representatives of a whole class of sets (that are themselves subsets of the universe of objects on which the fuzzy set is defined).

Fuzzy sets in pattern recognition, image analysis and automatic speech recognition

Dwijesh Dutta Majumder (1985)

Aplikace matematiky

Fuzzy set theory, a recent generalization of classical set theory, has attracted the attention of researchers working in various areas including pattern recognition, which has had a seminal influence in the development of this new theory. This paper attempts to discuss some of the methodologies that have been suggested for pattern recognition, and techniques for image processing and speech recognition.

Fuzzy transforms in image compression and fusion

Irina Perfilieva (2007)

Acta Mathematica Universitatis Ostraviensis

An overview of direct and inverse fuzzy transforms of three types is given and applications to data processing are considered. The construction and some important properties of fuzzy transforms are presented on the theoretical level. Three applications of F -transform to data processing have been chosen: compressional and reconstruction of data, removing noise and data fusion. All of them successively exploit the filtering property of the inverse fuzzy transform.

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