Laurent Hoeltgen
Laurent Hoeltgen
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Algorithm
Floating Point Accuracy and Precision
Floating point computations on computers may behave differently than one might expect. Every software developer should be aware of these since computed results may be off by orders of magnitude in the worst case. My first encounter with this subject was when I implemented the algorithms for the inpainting mask optimization. Recently I investigated the accuracy of QR decompositions that use Gram-Schmidt for the orthogonalisation process. The findings have been posted on this site in a series of posts in 2020.
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Simple Neural Networks with Julia
Oral presentation
2019-06-14
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Sparse $l_{1}$ Regularisation of Matrix Valued Models for Acoustic Source Characterisation
We present a strategy for the recovery of a sparse solution of a common problem in acoustic engineering, which is the reconstruction of …
Laurent Hoeltgen
,
Michael Breuß
,
Gert Herold
,
Ennes Sarradj
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DOI
Sparse $l_{1}$ Regularisation of Matrix Valued Models for Acoustic Source Characterisation
Poster presentation
2017-02-28
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Bregman Iteration for Correspondence Problems: A Study of Optical Flow
Bregman iterations are known to yield excellent results for denoising, deblurring and compressed sensing tasks, but so far this …
Laurent Hoeltgen
,
Michael Breuß
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Optimal interpolation data for image reconstructions
This work analyses several approaches for determining optimal sparse data sets for image reconstructions by means of linear homogeneous …
Laurent Hoeltgen
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An Optimal Control Approach to Find Sparse Data for Laplace Interpolation
Oral presentation
2013-08-19
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Slides
Continuous Spatial and Tonal Point Optimisation for Interpolation and Approximation of Convex Signals with Homogeneous Diffusion
Oral presentation
2013-03-18
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Slides
Bregman Iteration for Optical Flow
Oral presentation
2012-09-17
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Slides
Intermediate Flow Field Filtering in Energy Based Optic Flow Computations
Poster presentation
2011-07-25
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