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Label Assistant: A Workflow for Assisted Data Annotation in Image Segmentation Tasks

Schilling, M. P. ORCID iD icon; Rettenberger, L. ORCID iD icon; Münke, F.; Cui, H.; Popova, A. A.; Levkin, P. A. ORCID iD icon; Mikut, R. ORCID iD icon; Reischl, M.


Recent research in the field of computer vision strongly focuses on deep learning architectures to tackle image processing problems. Deep neural networks are often considered in complex image processing scenarios since traditional computer vision approaches are expensive to develop or reach their limits due to complex relations. However, a common criticism is the need for large annotated datasets to determine robust parameters. Annotating images by human experts is time-consuming, burdensome, and expensive. Thus, support is needed to simplify annotation, increase user efficiency, and annotation quality. In this paper, we propose a generic workflow to assist the annotation process and discuss methods on an abstract level. Thereby, we review the possibilities of focusing on promising samples, image pre-processing, pre-labeling, label inspection, or post-processing of annotations. In addition, we present an implementation of the proposal by means of a developed flexible and extendable software prototype nested in hybrid touchscreen/laptop device.

Volltext §
DOI: 10.5445/IR/1000140420
Veröffentlicht am 29.11.2021
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Automation und angewandte Informatik (IAI)
Institut für Biologische und Chemische Systeme (IBCS)
Publikationstyp Vortrag
Publikationsdatum 26.11.2021
Sprache Englisch
Identifikator KITopen-ID: 1000140420
HGF-Programm 47.14.02 (POF IV, LK 01) Information Storage and Processing in the Cell Nucleus
Veranstaltung 31. Workshop Computational Intelligence (2021), Berlin, Deutschland, 25.11.2021 – 26.11.2021
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