Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Real-Valued Pattern Classification Based on Extended Associative Memory
ENC '04 Proceedings of the Fifth Mexican International Conference in Computer Science
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Robust fuzzy clustering-based image segmentation
Applied Soft Computing
Robust Image Segmentation Algorithm Using Fuzzy Clustering Based on Kernel-Induced Distance Measure
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 01
Evolutionary image segmentation based on multiobjective clustering
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
An Evolutionary Approach to Multiobjective Clustering
IEEE Transactions on Evolutionary Computation
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In this paper, the authors have proposed an algorithm of segmenting grayscale image using associative memories approach. The algorithm is divided in three steps. In the first step, a set of regions (classes), where each one groups to a certain number of pixel values, is obtained. In the second step, the associative memories training phase is applied to the information obtained from first phase and an associative network, that contains the centroids group of each of the regions in which the image will be segmented, is obtained. Finally, using the associative memories classification phase, the centroid to which each pixel belongs is obtained and the image segmentation process is completed.