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.
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Langmann, B., Hartmann, K., Loffeld, O. (2012). A Modular Framework for 2D/3D and Multi-modal Segmentation with Joint Super-Resolution. In: Fusiello, A., Murino, V., Cucchiara, R. (eds) Computer Vision – ECCV 2012. Workshops and Demonstrations. ECCV 2012. Lecture Notes in Computer Science, vol 7584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33868-7_2
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DOI: https://doi.org/10.1007/978-3-642-33868-7_2
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