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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Significance Tests and Statistical Inequalities for Segmentation by Region Growing on Graph
verfasst von : Guillaume Née, Stéphanie Jehan-Besson, Luc Brun, Marinette Revenu
Erschienen in: Computer Analysis of Images and Patterns
Verlag: Springer Berlin Heidelberg
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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.