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A new feedback-based method for parameter adaptation in image processing routines

Khan, Ariful Maula; Mikut, Ralf ORCID iD icon 1; Reischl, Markus ORCID iD icon 1
1 Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung (IOSB)

Abstract (englisch):

The parametrization of automatic image processing routines is time-consuming if a lot of image processing parameters are involved. An expert can tune parameters sequentially to get desired results. This may not be productive for applications with difficult image analysis tasks, e.g. when high noise and shading levels in an image are present or images vary in their characteristics due to different acquisition conditions. Parameters are required to be tuned simultaneously. We propose a framework to improve standard image segmentation methods by using feedback-based automatic parameter adaptation. Moreover, we compare algorithms by implementing them in a feedforward fashion and then adapting their parameters. This comparison is proposed to be evaluated by a benchmark data set that contains challenging image distortions in an increasing fashion. This promptly enables us to compare different standard image segmentation algorithms in a feedback vs. feedforward implementation by evaluating their segmentation quality and robustness. We also propose an efficient way of performing automatic image analysis when only abstract ground truth is present. ... mehr

Volltext §
DOI: 10.5445/IR/1000062083
DOI: 10.1371/journal.pone.0165180
Zitationen: 2
Zitationen: 2
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Informatik (IAI)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2016
Sprache Englisch
Identifikator ISSN: 1932-6203
KITopen-ID: 1000062083
HGF-Programm 47.01.02 (POF III, LK 01) Biol.Netzwerke u.Synth.Regulat. IAI
Erschienen in PLoS one
Verlag Public Library of Science (PLoS)
Band 11
Heft 10
Seiten e0165180
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Vorab online veröffentlicht am 20.10.2016
Nachgewiesen in Scopus
Web of Science
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