A modular framework for 2D/3D and multi-modal segmentation with joint super-resolution

Abstract

A versatile multi-image segmentation framework for 2D/3D or multi-modal segmentation is introduced in this paper with possible application in a wide range of machine vision problems. The framework performs a joint segmentation and super-resolution to account for images of unequal resolutions gained from different imaging sensors. This allows to combine high resolution details of one modality with the distinctiveness of another modality. A set of measures is introduced to weight measurements according to their expected reliability and it is utilized in the segmentation as well as the super-resolution. The approach is demonstrated with different experimental setups and the effect of additional modalities as well as of the parameters of the framework are shown. © 2012 Springer-Verlag.