November 2017

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02/11/2017 - 14:00 to 15:00
 
 
 
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07/11/2017 - 12:00 to 13:00
 
08/11/2017 - 09:30 to 10/11/2017 - 17:00
 
08/11/2017 - 10:00 to 15:00
 
08/11/2017 - 09:30 to 10/11/2017 - 17:00
 
 
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15/11/2017 - 14:00 to 16:00
 
16/11/2017 - 09:00 to 16:00
 
17/11/2017 - 12:00 to 14:00
 
17/11/2017 - 15:00 to 17:00
 
 
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21/11/2017 - 09:00 to 22/11/2017 - 17:00
 
21/11/2017 - 09:00 to 22/11/2017 - 17:00
 
 
24/11/2017 - 10:30 to 12:00
 
 
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30/11/2017 - 15:00 to 16:00
 
 
 

Events

30/01/2018 - 14:00

Speaker: Dr. Joan Duran, Universitat de les Illes Balears, Department of Mathematics and Computer Science
Title: Collaborative Regularization Approaches in Multi-Channel Variational Imaging: Theory, Applications and Perspectives

Information: When dealing with color imaging problems, one of the main issues is how to couple channels in order to enable joint directions of smoothing. We propose to consider the gradient of a multi-channel image as a tensor, the smoothness of which is measured by taking different norms along the different dimensions (pixels, derivatives, channels). The resulting regularization framework is called collaborative total variation (CTV). We analyze theoretical properties of a large number of CTV models and show which of them are best suited for image denoising and other inverse problems. This has been a project in collaboration with Prof. Daniel Cremers from the Technical University of Munich and Prof. Michael Moller from the University of Siegen.

The proposed dates for seminars: 30.01 (Tuesday) at AVF RAL 2pm (and 01.02 (Thursday) at Manchester) 

01/02/2018 - 14:00

Speaker: Dr. Joan Duran, Universitat de les Illes Balears, Department of Mathematics and Computer Science
Title: Collaborative Regularization Approaches in Multi-Channel Variational Imaging: Theory, Applications and Perspectives

Information: When dealing with color imaging problems, one of the main issues is how to couple channels in order to enable joint directions of smoothing. We propose to consider the gradient of a multi-channel image as a tensor, the smoothness of which is measured by taking different norms along the different dimensions (pixels, derivatives, channels). The resulting regularization framework is called collaborative total variation (CTV). We analyze theoretical properties of a large number of CTV models and show which of them are best suited for image denoising and other inverse problems. This has been a project in collaboration with Prof. Daniel Cremers from the Technical University of Munich and Prof. Michael Moller from the University of Siegen.

Dates for seminars: (30.01 (Tuesday) at RAL and) 1 February 2018 (Thursday) 2pm at Frank Adams 2, Alan Turing Building, University of Manchester. 

08/02/2018 - 14:00

Prof. Hamish Carr, University Leeds (based on work with Brian Duffy & Torsten Moeller)

Hosted by: Edoardo Pasca - Rutherford Appleton Laboratory

 

Thursday 08 February 2018 – 14:00 hours
S44/R89 RAL and via VC link to CR3 DL

 

Geometric Measures of Isocontour Regions

 

Visualisation supports the scientific workflow, often by providing qualitative evidence of the presence or absence of phenomena.

More advanced forms of visualisation blend into analytical techniques that provide quantitative measurements, either of an entire data set, or of some region of interest inside it.  For this, the most typical approach is to extract a significant or representative isocontour, then to compute geometric properties inside it.  In 3D, these properties may include surface area, contained volume, mean and standard deviation, or any property integrable over the region, such as the function defining the isocontour in the first place.

 

Reliable computation of these properties requires taking into account the underlying nature of integration, as well as of quantisation, gradient errors, geometric meshing artifacts, numerical errors and convergence properties. This talk will survey the set of geometric and integrable properties that can conveniently be extracted, and how to compute them using the isocontour’s value as a parameter for efficient extraction. 

 

Your streaming link will be a RAL public stream and the link to forward to people is

https://sas.stfc.ac.uk/vportal/VideoPlayer.jsp?ccsid=C-a87babe0-3710-45eb-8adb-2d50d8a916b9:1#

13/02/2018 - 12:00

Yi Guo “Characterizing voids during ductile fracture of a dual phase steel using X-ray computed tomography and serial sectioning EBSD” and 

Shelley Rawson “Bio and bio-inspired materials”

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