Abstract
So far most applications of swarm behaviour in image analysis use swarms as models for optimisation tasks. In our paper, we follow a different philosophy and propose to exploit them as valuable tools for modelling image processing problems. To this end, we consider models of swarming that are individual-based and of first order. We show that a suitable adaptation of the potential forces allows us to model three classical image processing tasks: grey scale quantisation, contrast enhancement, and line detection. These proof-of-concept applications demonstrate that modelling image analysis tasks with swarms can be simple, intuitive, and highly flexible.
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Notes
- 1.
If this becomes too time-consuming, one can also consider more efficient, so-called implicit schemes [13]. However, they require to solve linear or nonlinear systems of equations.
- 2.
It is clear from the structure of our approach and the experiments that the quantisation levels depend on the actual image histogram and are not necessarily equidistant.
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Acknowledgments
Our research activities have been supported financially by the Deutsche Forschungsgemeinschaft (DFG) through a Gottfried Wilhelm Leibniz Prize. This is gratefully acknowledged.
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Bergerhoff, L., Weickert, J. (2016). Modelling Image Processing with Discrete First-Order Swarms. In: Pillay, N., Engelbrecht, A., Abraham, A., du Plessis, M., Snášel, V., Muda, A. (eds) Advances in Nature and Biologically Inspired Computing. Advances in Intelligent Systems and Computing, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-319-27400-3_23
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