Discrete Visual Perception

Nikos Paragios (1, 2) , Nikos Komodakis (3, 4)

1 CVN - Centre de vision numérique
2 GALEN - Organ Modeling through Extraction, Representation and Understanding of Medical Image Content
3 ENPC - École des Ponts ParisTech
4 LIGM - Laboratoire d'Informatique Gaspard-Monge

Computational vision and biomedical image have made tremendous progress of the past decade. This is mostly due the development of efficient learning and inference algorithms which allow better, faster and richer modeling of visual perception tasks. Graph-based representations are among the most prominent tools to address such perception through the casting of perception as a graph optimization problem. In this paper, we briefly introduce the interest of such representations, discuss their strength and limitations and present their application to address a variety of problems in computer vision and biomedical image analysis.

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Imagerie médicale

Nikos Paragios, Nikos Komodakis. Discrete Visual Perception. International Conference on Pattern Recognition, Aug 2014, Stockholm, Sweden. pp.1-7, ⟨10.1109/icpr.2014.13⟩. ⟨hal-01023406⟩