University of Birmingham > Talks@bham > Artificial Intelligence and Natural Computation seminars > Compressed Sensing for Non-Standard Tomography and Data Labelling by Assignment

Compressed Sensing for Non-Standard Tomography and Data Labelling by Assignment

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If you have a question about this talk, please contact Hector Basevi.

Host: Prof Peter Tiňo

Abstract: In the first part of the talk I will present various problems of tomography based on undersampled projection data that are relevant to a range of applications. They closely relate to Compressed Sensing, a sensing paradigm that has attracted attention in both mathematics and various disciplines of data science during the last years. Of particular interest are sensor models originating from practical applications that are not covered by the current theory of Compressed Sensing. I will demonstrate how progress on the mathematical side has an impact on computational data analysis approaches in practice.

In the second part of the talk, I will discuss the design of a dynamical system on an elementary statistical manifold, called assignment flow, for the contextual classification of data given on a graph. The approach can be seen as a smooth alternative to Bayesian MAP -inference using discrete graphical models. It provides a sound mathematical framework for studying, e.g., advanced image analysis problems related to supervised image labelling, unsupervised label learning and regularisation parameter learning from data.


This talk is part of the Artificial Intelligence and Natural Computation seminars series.

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