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

Khan, Ariful Maula; Mikut, Ralf; Reischl, Markus

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. Such a framework evaluates robustness of different image processing pipelines using a graded data set. This is useful for both end-users and experts.

Zugehörige Institution(en) am KIT Institut für Angewandte Informatik (IAI)
Publikationstyp Zeitschriftenaufsatz
Jahr 2016
Sprache Englisch
Identifikator DOI: 10.1371/journal.pone.0165180
ISSN: 1932-6203
URN: urn:nbn:de:swb:90-620834
KITopen ID: 1000062083
HGF-Programm 47.01.02; LK 01
Erschienen in PLoS one
Band 11
Heft 10
Seiten e0165180
Lizenz CC BY 4.0: Creative Commons Namensnennung 4.0 International
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
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