Connected image processing with multivariate attributes: an unsupervised Markovian classification approach

Benjamin Perret (1) , Christophe Collet (2)

1 LIGM - Laboratoire d'Informatique Gaspard-Monge
2 ICube - Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie

This article presents a new approach for constructing connected operators for image processing and analysis. It relies on a hierarchical Markovian unsupervised algorithm in order to classify the nodes of the traditional Max-Tree. This approach enables to naturally handle multivariate attributes in a robust non-local way. The technique is demonstrated on several image analysis tasks: filtering, segmentation, and source detection, on astronomical and biomedical images. The obtained results show that the method is competitive despite its general formulation. This article provides also a new insight in the field of hierarchical Markovian image processing showing that morphological trees can advantageously replace traditional quadtrees.

Mots clés: Image filtering; image segmentation; connected filter; Max-Tree; connected component tree; hierarchical modeling; Markov model; mathematical morphology

Domaines: Traitement des images [eess.IV]

Benjamin Perret, Christophe Collet. Connected image processing with multivariate attributes: an unsupervised Markovian classification approach. Computer Vision and Image Understanding, 2015, 133, pp.1-14. ⟨10.1016/j.cviu.2014.09.008⟩. ⟨hal-01071418⟩