A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Unsupervised Image Segmentation Using a Colony of Cooperating Ants
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Modeling and visualization of leaf venation patterns
ACM SIGGRAPH 2005 Papers
Edge detection using ant algorithms
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Artificial Ants to Extract Leaf Outlines and Primary Venation Patterns
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Swarm Intelligence: Introduction and Applications
Swarm Intelligence: Introduction and Applications
Varying the population size of artificial foraging swarms on time varying landscapes
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
| Hi-index | 12.05 |
This paper extends on previous work in applying an ant algorithm to image feature extraction, focusing on edge pattern extraction, as well as the broader study of self-organisation mechanisms in digital image environments. A novel method of distributed adaptive thresholding is introduced to the ant algorithm, which enables automated distributed adaptive thresholding across the swarm. This technique is shown to increase performance of the algorithm, and furthermore, eliminates the requirement for a user set threshold, allowing the algorithm to autonomously adapt an appropriate threshold for a given image, or data set. Additionally this approach is extended to allow for simultaneous multiple-swarm multiple-feature extraction, as well as dynamic adaptation to changing imagery.