Abstract
This paper describes an image segmentation method based on an evolutionary approach. Unlike other application of evolutionary algorithms to this problem, our method does not require the definition of a global fitness function. Instead a survival probability for each individual guides the progress of the algorithm. The evolution involves the colonization of a bidimensional world by a number of populations. The individuals, belonging to different populations, compete to occupy all the available space and adapt to the local environmental characteristics of the world. We present various sets of experiments on simulated MR brain images in order to determine the optimal parameter settings. Experimental results on real image are also reported. Images used in this work are color camera photographs of beef meat.
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Bocchi, L., Ballerini, L. (2006). Image Space Colonization Algorithm. In: Rothlauf, F., et al. Applications of Evolutionary Computing. EvoWorkshops 2006. Lecture Notes in Computer Science, vol 3907. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11732242_32
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DOI: https://doi.org/10.1007/11732242_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33237-4
Online ISBN: 978-3-540-33238-1
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