eprintid: 23315
rev_number: 16
eprint_status: archive
userid: 3263
dir: disk0/00/02/33/15
datestamp: 2017-10-10 11:45:02
lastmod: 2017-10-11 08:31:37
status_changed: 2017-10-10 11:45:02
type: doctoralThesis
metadata_visibility: show
creators_name: Swoboda, Paul
title: New Convex Relaxations and Global Optimality in Variational Imaging
subjects: ddc-004
subjects: ddc-510
divisions: i-110400
adv_faculty: af-11
cterms_swd: Bildverarbeitung
cterms_swd: Maschinelles Sehen info
cterms_swd: Optimierung
abstract: Variational methods constitute the basic building blocks for solving many image analysis tasks, be it segmentation, depth estimation, optical flow, object detection etc. Many of these problems can be expressed in the framework of Markov Random Fields (MRF) or as continuous labelling problems. Finding the Maximum A-Posteriori (MAP) solutions of suitably constructed MRFs or the optimizers of the labelling problems give solutions to the aforementioned tasks. In either case, the associated optimization problem amounts to solving structured energy minimization problems.
In this thesis we study novel extensions applicable to Markov Random Fields and continuous labelling problems through which we are able to incorporate statistical global constraints. To this end, we devise tractable relaxations of the resulting energy minimization problem and efficient algorithms to tackle them. Second, we propose a general mechanism to find partial optimal solutions to the problem of finding a MAP-solution of an MRF, utilizing only standard relxations.
date: 2016
id_scheme: DOI
id_number: 10.11588/heidok.00023315
ppn_swb: 165645047X
own_urn: urn:nbn:de:bsz:16-heidok-233151
date_accepted: 2016-08-01
advisor: HASH(0x55e0f7eeaf50)
language: eng
bibsort: SWOBODAPAUNEWCONVEXR2016
full_text_status: public
citation:   Swoboda, Paul  (2016) New Convex Relaxations and Global Optimality in Variational Imaging.  [Dissertation]     
document_url: https://archiv.ub.uni-heidelberg.de/volltextserver/23315/1/thesis.pdf