Boundary Detection by Constrained Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constrained Restoration and the Recovery of Discontinuities
IEEE Transactions on Pattern Analysis and Machine Intelligence
AntNet: distributed stigmergetic control for communications networks
Journal of Artificial Intelligence Research
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Implementing linear models in genetic programming
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
'Identifying the structure of nonlinear dynamic systems using multiobjective genetic programming
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Ant Colony Optimization for Image Regularization Based on a Nonstationary Markov Modeling
IEEE Transactions on Image Processing
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In global classifications using Markov Random Field (MRF) modeling, the neighborhood form is generally considered as independent of its location in the image. Such an approach may lead to classification errors for pixels located at the segment borders. The solution proposed here consists in relaxing the assumption of fixed-form neighborhood. Here we propose to use the Ant Colony Optimization (ACO) and to exploit its ability of self-organization. Modeling upon the behavior of social insects for computing strategies, the ACO ants collect information through the image, from one pixel to the others. The choice of the path is a function of the pixel label, favoring paths within a same image segment. We show that this corresponds to an automatic adaptation of the neighborhood to the segment form. Performance of this new approach is illustrated on a simulated image and on actual remote sensing images SPOT4/HRV.