Unsupervised Morphological Multiscale Segmentation of Scanning Electron Microscopy Images

This paper deals with a problem of unsupervised multiscale segmentation in the domain of scanning electron microscopy, which is tackled by mathematical morphology techniques. The proposed approach includes various steps. First, the image is decomposed into various compact scales of representation, where objects at each scale are homogeneous in size. Multiscale decomposition is based on a morphological scale-space followed by scale merging using hierarchical clustering and earth mover distance. Then the compact scales are segmented independently using watershed transform. Finally the segmented scales are combined using a tree of objects in order to obtain a multiscale segmentation.

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Additional Info

Field Value
Source https://hal.science/hal-00939242
Author Franchi, Gianni, Angulo, Jesus, Moreaud, Maxime
Maintainer CCSD
Last Updated May 7, 2026, 04:36 (UTC)
Created May 7, 2026, 04:36 (UTC)
Identifier hal-00939242
Language en
Rights https://about.hal.science/hal-authorisation-v1/
contributor Centre de Morphologie Mathématique (CMM) ; Mines Paris - PSL (École nationale supérieure des mines de Paris) ; Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)
creator Franchi, Gianni
date 2014-01-29T00:00:00
harvest_object_id c18704b1-d45f-4d06-ade1-60c12fc2e71f
harvest_source_id 3374d638-d20b-4672-ba96-a23232d55657
harvest_source_title test moissonnage SELUNE
metadata_modified 2026-01-09T00:00:00
set_spec type:UNDEFINED