Adaptive Image Segmentation With Distributed Behavior-Based Agents
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cooperative agents society organized as an irregular pyramid: a mammography segmentation application
Pattern Recognition Letters
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Information combination operators for data fusion: a comparative review with classification
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A cooperative framework for segmentation of MRI brain scans
Artificial Intelligence in Medicine
MRF Agent Based Segmentation: Application to MRI Brain Scans
AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
A Multi-agent Approach for Range Image Segmentation
CEEMAS '07 Proceedings of the 5th international Central and Eastern European conference on Multi-Agent Systems and Applications V
An Agent-Based Approach for Range Image Segmentation
Massively Multi-Agent Technology
Fusion of classic P300 detection methods' inferences in a framework of fuzzy labels
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Knowledge and intelligent computing system in medicine
Computers in Biology and Medicine
Resource constraints on computation and communication in the brain
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A Distributed and Collective Approach for Curved Object-Based Range Image Segmentation
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
A multi-agent approach for range image segmentation with Bayesian edge regularization
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
Review: on the use of agent technology in intelligent, multisensory and distributed surveillance
The Knowledge Engineering Review
A new distributed approach for range image segmentation
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
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Image interpretation consists in finding a correspondence between radiometric information and symbolic labeling with respect to specific spatial constraints. It is intrinsically a distributed process in terms of goals to be reached, zones in the image to be processed and methods to be applied. To cope with the difficulty inherent in this process, several information processing steps are required to gradually extract information from the gray levels in the image and to introduce symbolic information. In this paper we advocate the use of situated cooperative agents as a framework for managing such steps. Dedicated agent behaviors are dynamically adapted depending on their position in the image, of their topographic relationships and of the radiometric information available. The information collected by the agents is gathered, shared via qualitative maps, or used as soon as available by acquaintances. Incremental refinement of interpretation is obtained through a coarse to fine strategy. Our work is essentially focused on radiometry-based tissue interpretation where knowledge is introduced or extracted at several levels to estimate models for tissue-intensity distribution and to cope with noise, intensity non-uniformity and partial volume effect. Several experiments on phantom and real images were performed. A complete volume can be segmented in less than 5min with about 0.84% accuracy of the segmented reference. Comparison with other techniques demonstrates the potential interest of our approach for magnetic resonance imaging (MRI) brain scan interpretation.