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. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Née, G., Jehan-Besson, S., Brun, L., & Revenu, M. (2009). Significance tests and statistical inequalities for segmentation by region growing on graph. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5702 LNCS, pp. 939–946). https://doi.org/10.1007/978-3-642-03767-2_114
Mendeley helps you to discover research relevant for your work.