Abstract
Bottom-up segmentation methods merge similar neighboring regions according to a decision rule and a merging order. In this paper, we propose a contribution for each of these two points. Firstly, under statistical hypothesis of similarity, we provide an improved decision rule for region merging based on significance tests and the recent statistical inequality of McDiarmid. Secondly, we propose a dynamic merging order based on our merging predicate. This last heuristic is justified by considering an energy minimisation framework. Experimental results on both natural and medical images show the validity of our method.
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Née, G., Jehan-Besson, S., Brun, L., Revenu, M. (2009). Significance Tests and Statistical Inequalities for Segmentation by Region Growing on Graph. In: Jiang, X., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2009. Lecture Notes in Computer Science, vol 5702. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03767-2_114
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DOI: https://doi.org/10.1007/978-3-642-03767-2_114
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03766-5
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