Directed connected operators: asymmetric hierarchies for image filtering and segmentation

Benjamin Perret (1) , Jean Cousty (1) , Olena Tankyevych (2) , Hugues Talbot (1) , Nicolas Passat (3)

1 LIGM - Laboratoire d'Informatique Gaspard-Monge
2 LISSI - Laboratoire Images, Signaux et Systèmes Intelligents
3 CRESTIC - Centre de Recherche en Sciences et Technologies de l'Information et de la Communication - EA 3804

Connected operators provide well-established solutions for digital image processing, typically in conjunction with hierarchical schemes. In graph-based frameworks, such operators basically rely on symmetric adjacency relations between pixels. In this article, we introduce a notion of directed connected operators for hierarchical image processing, by also considering non-symmetric adjacency relations. The induced image representation models are no longer partition hierarchies (i.e., trees), but directed acyclic graphs that generalize standard morphological tree structures such as component trees, binary partition trees or hierarchical watersheds. We describe how to efficiently build and handle these richer data structures, and we illustrate the versatility of the proposed framework in image filtering and image segmentation.

Mots clés: Mathematical morphology; connected operators; hierarchical image representation; antiextensive filtering; segmentation

Domaines: Traitement des images [eess.IV]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Benjamin Perret, Jean Cousty, Olena Tankyevych, Hugues Talbot, Nicolas Passat. Directed connected operators: asymmetric hierarchies for image filtering and segmentation. 2013. ⟨hal-00869727v1⟩