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What and Who
Title:Graph Decomposition Problems in Image Analysis
Speaker:Björn Andres
coming from:D2
Speakers Bio:
Event Type:Joint Lecture Series
Visibility:D1, D2, D3, D4, D5, SWS, RG1, MMCI
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Level:Public Audience
Date, Time and Location
Date:Wednesday, 5 April 2017
Duration:60 Minutes
Building:E1 5
A large part of image analysis is about breaking things into pieces. Decompositions of a graph are a mathematical abstraction of the possible outcomes. Breaking things (like an image itself) into pieces in a controlled way is hard. Optimization problems whose feasible solutions define decompositions of a graph are a mathematical abstraction of this task. One example is the correlation clustering problem whose feasible solutions relate one-to-one to the decompositions of a graph, and whose objective function puts a cost or reward on neighboring nodes ending up in distinct components. This talk shows work by the Combinatorial Image Analysis research group that applies this problem and proposed generalizations to diverse image analysis tasks. It sketches the algorithms introduced by the group for finding feasible solutions for large instances in practice, solutions that are often superior in the metrics of application-specific benchmarks. Finally, it sketches the algorithm introduced by the group for finding lower bounds and points to new findings and open problems of polyhedral geometry in this context.
Name(s):Jennifer Müller
EMail:--email address not disclosed on the web
Video Broadcast
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Attachments, File(s):
Jennifer Müller/MPI-INF, 03/16/2017 11:07 AM
Last modified:
Uwe Brahm/MPII/DE, 04/05/2017 07:01 AM
  • Jennifer Müller, 03/28/2017 03:25 PM
  • Jennifer Müller, 03/16/2017 11:08 AM -- Created document.