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Joint Reconstruction-Segmentation with Graph PDEs

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MDL - Mathematics of deep learning

In most practical image segmentation tasks, the image to be segmented will need to first be reconstructed from indirect, damaged, and/or noisy observations. Traditionally, this reconstruction-segmentation task would be done in sequence: first apply the reconstruction method, and then the segmentation method. Joint reconstruction-segmentation is a method for using segmentation and reconstruction techniques simultaneously, to use information from the segmentation to guide the reconstruction, and vice versa. Past work on this has employed relatively simple segmentation algorithms, such as the Chan–Vese algorithm. In this talk, we will demonstrate how joint reconstruction-segmentation can be done using the graph-PDE-based segmentation techniques developed by Bertozzi & Flenner (2012) and Merkurjev, Kostic, & Bertozzi (2013), with ideas drawn from Budd & van Gennip (2020) and Budd, van Gennip, & Latz (2021).

This work is joint with Yves van Gennip, Carola Schonlieb, Simone Parisotto, and Jonas Latz. 

This talk is part of the Isaac Newton Institute Seminar Series series.

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