Digital image processing algorithms
Digital image processing algorithms
Learning Texture Discrimination Masks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation of Random Fields Via Borrowed Strength Density Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the second international conference on Autonomous agents
2nd International Conference on Autonomous Agents
Algorithms for Graphics and Imag
Algorithms for Graphics and Imag
Markov Random Field Models for Unsupervised Segmentation of Textured Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
An evolutionary autonomous agents approach to image featureextraction
IEEE Transactions on Evolutionary Computation
Multi-agent oriented constraint satisfaction
Artificial Intelligence
How social spiders inspired an approach To region detection
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
A light-weight agent architecture for collaborative multimedia systems
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Interactive virtual environments and distance education
Cooperative agents society organized as an irregular pyramid: a mammography segmentation application
Pattern Recognition Letters
A new swarm mechanism based on social spiders colonies: from web weaving to region detection
Web Intelligence and Agent Systems
Distributed Markovian segmentation: Application to MR brain scans
Pattern Recognition
TeMAS–a multi-agent system for temporally rich domains
Knowledge and Information Systems
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
A Novel Image Segmentation Algorithm Based on Artificial Ant Colonies
Medical Imaging and Informatics
An Agent-Based Approach for Range Image Segmentation
Massively Multi-Agent Technology
An artificial ant colonies approach to medical image segmentation
Computer Methods and Programs in Biomedicine
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
3-D object segmentation using ant colonies
Pattern Recognition
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
Image space colonization algorithm
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Review: on the use of agent technology in intelligent, multisensory and distributed surveillance
The Knowledge Engineering Review
A new evolutionary algorithm for image segmentation
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
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
A multi-agent system for image segmentation a bio-inspired approach
ISIICT'09 Proceedings of the Third international conference on Innovation and Information and Communication Technology
Automated segmentation of human brain MR images using a multi-agent approach
Artificial Intelligence in Medicine
A cooperative framework for segmentation of MRI brain scans
Artificial Intelligence in Medicine
A Swarm Intelligence inspired algorithm for contour detection in images
Applied Soft Computing
Adaptive edge detection with distributed behaviour-based agents in WSNs
International Journal of Sensor Networks
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This paper presents an autonomous agent-based image segmentation approach. In this approach, a digital image is viewed as a two-dimensional cellular environment in which the agents inhabit and attempt to label homogeneous segments. In so doing, the agents rely on some reactive behaviors such as breeding and diffusion. The agents that are successful in finding the pixels of a specific homogeneous segment will breed offspring agents inside their neighboring regions. Hence, the offspring agents will become likely to find more homogeneous-segment pixels. In the mean time, the unsuccessful agents will be inactivated, without further search in the environment.