Displaying similar documents to “Application of fuzzy techniques to the design of algorithms in computer vision.”

Fuzzy sets in computer vision: an overview.

Pilar Sobrevilla, Eduard Montseny (2003)

Mathware and Soft Computing

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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...

Evolution-fuzzy rule based system with parameterized consequences

Piotr Czekalski (2006)

International Journal of Applied Mathematics and Computer Science

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While using automated learning methods, the lack of accuracy and poor knowledge generalization are both typical problems for a rule-based system obtained on a given data set. This paper introduces a new method capable of generating an accurate rule-based fuzzy inference system with parameterized consequences using an automated, off-line learning process based on multi-phase evolutionary computing and a training data covering algorithm. The presented method consists of the following steps:...

A defuzzification based new algorithm for the design of Mamdani-type fuzzy controllers

Jean Jamil Saade (2000)

Mathware and Soft Computing

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This paper presents a new learning algorithm for the design of Mamdani- type or fully-linguistic fuzzy controllers based on available input-output data. It relies on the use of a previously introduced parametrized defuzzification strategy. The learning scheme is supported by an investigated property of the defuzzification method. In addition, the algorithm is tested by considering a typical non-linear function that has been adopted in a number of published research articles. The test...

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

Dwijesh Dutta Majumder (1985)

Aplikace matematiky

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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.

Evolutionary algorithms and fuzzy sets for discovering temporal rules

Stephen G. Matthews, Mario A. Gongora, Adrian A. Hopgood (2013)

International Journal of Applied Mathematics and Computer Science

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A novel method is presented for mining fuzzy association rules that have a temporal pattern. Our proposed method contributes towards discovering temporal patterns that could otherwise be lost from defining the membership functions before the mining process. The novelty of this research lies in exploring the composition of fuzzy and temporal association rules, and using a multi-objective evolutionary algorithm combined with iterative rule learning to mine many rules. Temporal patterns...

On classification with missing data using rough-neuro-fuzzy systems

Robert K. Nowicki (2010)

International Journal of Applied Mathematics and Computer Science

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The paper presents a new approach to fuzzy classification in the case of missing data. Rough-fuzzy sets are incorporated into logical type neuro-fuzzy structures and a rough-neuro-fuzzy classifier is derived. Theorems which allow determining the structure of the rough-neuro-fuzzy classifier are given. Several experiments illustrating the performance of the roughneuro-fuzzy classifier working in the case of missing features are described.

A methodology for developing knowledge-based systems.

Juan Luis Castro, José Jesús Castro-Sánchez, Antonio Espin, José Manuel Zurita (1998)

Mathware and Soft Computing

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This paper presents a methodology for developing fuzzy knowledge based systems (KBS), which permits a complete automatization. This methodology will be useful for approaching more complex problems that those in which machine learning from examples are successful.

Neuro-fuzzy modelling based on a deterministic annealing approach

Robert Czabański (2005)

International Journal of Applied Mathematics and Computer Science

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This paper introduces a new learning algorithm for artificial neural networks, based on a fuzzy inference system ANBLIR. It is a computationally effective neuro-fuzzy system with parametrized fuzzy sets in the consequent parts of fuzzy if-then rules, which uses a conjunctive as well as a logical interpretation of those rules. In the original approach, the estimation of unknown system parameters was made by means of a combination of both gradient and least-squares methods. The novelty...