A Modular Framework for 2D/3D and Multi-modal Segmentation with Joint Super-Resolution
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.
KeywordsSegmentation Image Processing Range Imaging Time-of-Flight (ToF) Photonic Mixer Device (PMD)
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