Artificial Ants to Extract Leaf Outlines and Primary Venation Patterns

Authors:
Robert J. Mullen;Dorothy Monekosso;Sarah Barman;Paolo Remagnino;Paul Wilkin
Affiliations:
Digital Image Research Center, Kingston University, London, UK;Digital Image Research Center, Kingston University, London, UK;Digital Image Research Center, Kingston University, London, UK;Digital Image Research Center, Kingston University, London, UK;Royal Botanic Gardens KEW, London, UK
Venue:
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Year:
2008

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Abstract

This paper presents preliminary results on an investigation into using artificial swarms to extract and quantify features in digital images. An ant algorithm has been developed to automatically extract the outlines and primary venation patterns from digital images of living leaf specimens via an edge detection method. A qualitative and quantitative analysis of the results is carried out herein. The artificial swarms are shown to converge onto the edges within the leaf images and statistical accuracy, as measured against ground truth images, is shown to increase in accordance with the swarm convergence. Visual results are promising, however limitations due to background noise need to be addressed for the given application. The findings in this study present potential for increased robustness in using swarm based methods, by exploiting their stigmergic behaviour to reduce the need for parameter fine-tuning with respect to individual image characteristics.