eprintid: 27440
rev_number: 13
eprint_status: archive
userid: 4824
dir: disk0/00/02/74/40
datestamp: 2019-12-02 11:05:56
lastmod: 2019-12-16 11:53:55
status_changed: 2019-12-02 11:05:56
type: doctoralThesis
metadata_visibility: show
creators_name: Markowsky, Peter
title: Model-based Stochastical Segmentation of
Higher-dimensional Data
subjects: ddc-510
divisions: i-110001
adv_faculty: af-11
cterms_swd: Bilderkennung
cterms_swd: Kombinatorische Optimierung
cterms_swd: Punktprozess
abstract: This thesis is motivated by the problem of segmenting extremely noisy images of geometric objects.
To this end, it combines randomized combinatorial set cover optimization with a statistical model
of object interaction. The set cover approach provides stability and applicability in cases in which
many traditional methods of segmentation fail due to noise and imperfect data. The statistical model
provides additional information that is not directly supplied by the image, and leads to a more realistic
depiction of physical object properties in the resulting segmentation.
This dissertation is divided into three parts: The first covers topics of randomized combinatorial
optimization. This includes improving bounds of convergence and establishing a method of parallelization
for an existing approach, as well as linking solutions to different combinatorial problems,
such as geometric set cover and a general linear program.
Part two is concerned with constructing a point process model of object interaction that fits later
applications, and exploring some theoretical and practical pitfalls in its simulation, estimation, and
coupling with a combinatorial approach.
Part three compares previously discussed methods empirically, and demonstrates the performance
of the established combination of randomized optimization and statistical model on microscopic cell
images and 3D μCT scans of fiber reinforced materials.
date: 2019
id_scheme: DOI
id_number: 10.11588/heidok.00027440
ppn_swb: 1684925185
own_urn: urn:nbn:de:bsz:16-heidok-274402
date_accepted: 2019-11-19
advisor: HASH(0x55a9a6465c20)
language: eng
bibsort: MARKOWSKYPMODELBASED2019
full_text_status: public
place_of_pub: Heidelberg
citation:   Markowsky, Peter  (2019) Model-based Stochastical Segmentation of Higher-dimensional Data.  [Dissertation]     
document_url: https://archiv.ub.uni-heidelberg.de/volltextserver/27440/1/Thesis_Markowsky.pdf