We propose a quantitative evaluation of morphological hierarchies (quasi-flat zones, constraint connectivity, watersheds, observation scale) in a novel framework based on the marked segmentation problem. We created a set of automatically generated markers for the one object image datasets of Grabcut and Weizmann. In order to evaluate the hierarchies, we applied the same segmentation strategy by combining several parameters and markers. Our results, which shows important differences among the considered hierarchies, give clues to understand the behaviour of each method in order to choose the best one for a given application. The code and the marker datasets are available online.
CITATION STYLE
Perret, B., Cousty, J., Ura, J. C. R., & Guimarães, S. J. F. (2015). Evaluation of morphological hierarchies for supervised segmentation. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9082, 39–50. https://doi.org/10.1007/978-3-319-18720-4_4
Mendeley helps you to discover research relevant for your work.