In this paper a new variation of Hough Transform is proposed. It can be used to detect shapes or contours in an image, with better accuracy, especially in noisy images. The parameter space of Hough Transform is split into fuzzy cells which are de ned as fuzzy numbers. This fuzzy split provides the advantage to use the uncertainty of the contour point location which is increased when noisy image...

This paper presents a novel method for handwritten character recognition using wavelet packet transform and fuzzy logic. This method exploits the time-frequency localization and compression capability of wavelet packet transform, using the best basis algorithm to enhance the accuracy of recognition at the pixel level and the computational capability of fuzzy logic with linguistic variables, whi...

In some previous works the authors showed the advantages in coding and decoding images in the YUV space by using fuzzy relation equations. Indeed the images in the Y band were less compressed than in the U, V bands and a better Peak Signal to Noise Ratio was obtained with respect to that deduced by coding and decoding the same images in the RGB space. In another foregoing paper we used the fuzz...

In this paper1, we introduce a fuzzy automaton for computing the similarity between pairs of strings and a genetic method for adjusting its parameters. The fuzzy automaton models the edit operations needed to transform any string into another one. The selection of appropriate fuzzy operations and fuzzy membership values for the transitions leads to improve the system performance for a particula...

This paper proposes a hybrid approach based on local linear neuro fuzzy (LLNF) model and fuzzy transform (F-transform), termed FT-LLNF, for prediction of energy consumption. LLNF models are powerful in modelling and forecasting highly nonlinear and complex time series. Starting from an optimal linear least square model, they add nonlinear neurons to the initial model as long as the model's accu...

This paper is concerned with the fuzzy support vector classification, in which both of the type of the output training point and the value of the final fuzzy classification function are triangle fuzzy number. First, the fuzzy classification problem is formulated as a fuzzy chance constrained programming. Then, we transform this programming into its equivalence quadratic programming. Final, a fu...

The paper presents second author’s step toward building quite new theory of fuzzy signals. Before, in the first step author defined fuzzy Fourier transform. Here, an approach to description of fuzzy correlation functions is proposed. New definitions, called later fuzzy correlation and defuzzified correlation are proposed for fuzzy signals. The definitions are based on new concepts of the class ...

In this paper, the concept of canonical representation is proposed to find fuzzy roots of fuzzy polynomial equations. We transform fuzzy polynomial equations to system of crisp polynomial equations, this transformation is perform by using canonical representation based on three parameters Value, Ambiguity and Fuzziness.