Efficient Implementation of the Fuzzy c-Means Clustering Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Image Segmentation With Distributed Behavior-Based Agents
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition
Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition
Image segmentation using evolutionary computation
IEEE Transactions on Evolutionary Computation
Clustering with a genetically optimized approach
IEEE Transactions on Evolutionary Computation
Optimization of clustering criteria by reformulation
IEEE Transactions on Fuzzy Systems
A cellular coevolutionary algorithm for image segmentation
IEEE Transactions on Image Processing
An object detection and recognition system for weld bead extraction from digital radiographs
Computer Vision and Image Understanding
A swarm based approach to medical image analysis
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
Unsupervised Evolutionary Segmentation Algorithm Based on Texture Analysis
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
An object detection and recognition system for weld bead extraction from digital radiographs
Computer Vision and Image Understanding
Image space colonization algorithm
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
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This paper describes a new evolutionary algorithm for image segmentation. The evolution involves the colonization of a bidimensional world by a number of populations. The individuals, belonging to different populations, compete to occupy all the available space and adapt to the local environmental characteristics of the world. We present experiments with synthetic images, where we show the efficiency of the proposed method and compare it to other segmentation algorithm, and an application to medical images. Reported results indicate that the segmentation of noise images is effectively improved. Moreover, the proposed method can be applied to a wide variety of images.