The fuzzy c-partition entropy technique for threshold selection is one of the best image thresholding techniques, but its complexity increases with the number of thresholds. In this paper, the selection of thresholds (fuzzy parameters) was seen as an optimization problem and solved using particle swarm optimization (PSO), differential evolution (DE), genetic (GA) algorithms. The proposed fast approaches have been tested on many images. For example, the processing time of four-level thresholding using PSO, DE and GA is reduced to less than 0.4s. PSO, DE and GA show equal performance when the number of thresholds is small. When the number of thresholds is greater, the PSO algorithm performs better than GA and DE in terms of precision and robustness. But the GA algorithm is the most efficient with respect to the execution time.

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This paper is related to the simulation, in Matlab environment, of a robot manipulator controlled by both type-1 and interval type-2 fuzzy controllers, in which a modification in Karnik-Mendel algorithm has been proposed. To calculate the output of interval type-2 fuzzy system there is a main step called type-reduced; this operation is based on Karnik-Mendel algorithm, which uses arithmetic mean to calculate the control output. In this work, we propose to change the arithmetic mean by harmonic one. The performances of modified interval type-2 controller and type-1 fuzzy controller with and without noises are compared in terms of integral of squared error. The proposed modification in type reduction of Karnik-Mendel algorithm for interval type-2 fuzzy set shows best performance. Indeed, the amount of error in case of modified interval type-2 fuzzy controller is less two times than type-1 fuzzy controller.

Abstract-With the development of acquisition image techniques, more data coming from different sources of image become available. Multi-modality image fusion seeks to combine information from different images to obtain more inferences than can be derived from a single modality. The main aim of this work is to improve cerebral IRM real images segmentation by fusion of modalities (T1, T2 and DP) using estimation et maximizatio Approach (EM). The evaluation of adopted approaches was compared using four criteria which are: the standard deviation (STD), entropy of information (IE), the coefficient of correlation (CC) and the space frequency (SF). The experimental results on MRI brain real images prove that the adopted scenarios of fusion approaches are more accurate and robust than the standard EM approach