Video sequence segmentation using genetic algorithms
Pattern Recognition Letters
Multiresolution-based watersheds for efficient image segmentation
Pattern Recognition Letters
Robust Multiscale Affine 2D-Image Registration through Evolutionary Strategies
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Stereo Correspondence Using GA-Based Segmentation
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
Scene Interpretation Using Semantic Nets and Evolutionary Computation
Real-World Applications of Evolutionary Computing, EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoROB, and EvoFlight
Design and Analysis of an Efficient Evolutionary Image Segmentation Algorithm
Journal of VLSI Signal Processing Systems
Automatic target detection by optimal morphological filters
Journal of Computer Science and Technology
A Parallel Genetic Algorithm for Physical Mapping of Chromosomes
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Image segmentation by three-level thresholding based on maximum fuzzy entropy and genetic algorithm
Pattern Recognition Letters
Markov random field modeled range image segmentation
Pattern Recognition Letters
Automatic video segmentation using genetic algorithms
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
The strongest schema learning GA and its application to multilevel thresholding
Image and Vision Computing
Edge-based Segmentation Using Robust Evolutionary Algorithm Applied to Medical Images
Journal of Signal Processing Systems
Solving the multiple competitive facilities location and design problem on the plane
Evolutionary Computation
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
MR Brain Image Segmentation Using A Multi-seed Based Automatic Clustering Technique
Fundamenta Informaticae
Genetic algorithms for video segmentation
Pattern Recognition
Natural and remote sensing image segmentation using memetic computing
IEEE Computational Intelligence Magazine
Chemical-reaction-inspired metaheuristic for optimization
IEEE Transactions on Evolutionary Computation
Use of GA based approach for engineering design through WWW
WSEAS Transactions on Computers
A multiobjective approach to MR brain image segmentation
Applied Soft Computing
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Image space colonization algorithm
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
A new evolutionary algorithm for image segmentation
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
MR Brain Image Segmentation Using A Multi-seed Based Automatic Clustering Technique
Fundamenta Informaticae
Image Segmentation Based on Bacterial Foraging and FCM Algorithm
International Journal of Swarm Intelligence Research
GeneSIS: A GA-based fuzzy segmentation algorithm for remote sensing images
Knowledge-Based Systems
Moving object detection using Markov Random Field and Distributed Differential Evolution
Applied Soft Computing
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Image segmentation denotes a process by which a raw input image is partitioned into nonoverlapping regions such that each region is homogeneous and the union of any two adjacent regions is heterogeneous. A segmented image is considered to be the highest domain-independent abstraction of an input image. The image segmentation problem is treated as one of combinatorial optimization. A cost function which incorporates both edge information and region gray-scale uniformity is defined. The cost function is shown to be multivariate with several local minima. The genetic algorithm, a stochastic optimization technique based on evolutionary computation, is explored in the context of image segmentation. A class of hybrid evolutionary optimization algorithms based on a combination of the genetic algorithm and stochastic annealing algorithms such as simulated annealing, microcanonical annealing, and the random cost algorithm is shown to exhibit superior performance as compared with the canonical genetic algorithm. Experimental results on gray-scale images are presented