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A Modular Framework for 2D/3D and Multi-modal Segmentation with Joint Super-Resolution
Benjamin Langmann, Klaus Hartmann, and Otmar Loffeld
ZESS - Center for Sensor Systems, University of Siegen, Paul-Bonatz-Str. 9-11, 57068, Siegen, Germanylangmann@zess.uni-siegen.de
hartmann@zess.uni-siegen.de
loffeld@zess.uni-siegen.de
http://www.zess.uni-siegen.de
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.
Keywords: Segmentation, Image Processing, Range Imaging, Time-of-Flight (ToF), Photonic Mixer Device (PMD)
LNCS 7584, p. 12 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2012