Adaptive Image Segmentation With Distributed Behavior-Based Agents
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-based fuzzy connected image segmentation: theory, algorithms, and validation
Computer Vision and Image Understanding - Special issue on analysis of volumetric image
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Spatial models for fuzzy clustering
Computer Vision and Image Understanding
Deformable Organisms for Automatic Medical Image Analysis
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
IEEE Transactions on Image Processing
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Segmentation is one of the most difficult tasks in digital image processing. This paper presents a novel segmentation algorithm, which uses a biologically inspired paradigm known as artificial ant colonies. Considering the features of artificial ant colonies, we present an extended model applied in image segmentation. Each ant in our model is endowed with the ability of memorizing a reference object, which will be refreshed when a new target is found. A fuzzy connectedness measure is adopted to evaluate the similarity between the target and the reference object. The behavior of one ant is affected by the neighboring ants and the cooperation between ants is performed by exchanging information through pheromone updating. The simulated results show the efficiency of the new algorithm, which is able to preserve the detail of the object and is insensitive to noise.