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A Review of Adaptable Conventional Image Processing Pipelines and Deep Learning on limited Datasets

Münke, Friedrich Rieken ORCID iD icon 1; Schützke, Jan ORCID iD icon 1; Berens, Felix ORCID iD icon; Reischl, Markus ORCID iD icon 1
1 Institut für Automation und angewandte Informatik (IAI), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

The objective of this paper is to study the impact of limited datasets on deep learning techniques and conventional methods in semantic image segmentation and to conduct a comparative analysis in order to determine the optimal scenario for utilizing both approaches. We introduce a synthetic data generator, which enables us to evaluate the impact of the number of training samples as well as the difficulty and diversity of the dataset. We show that deep learning methods excel when large datasets are available and conventional image processing approaches perform well when the datasets are small and diverse. Since transfer learning is a common approach to work around small datasets, we are specifically assessing its impact and found only marginal impact. Furthermore, we implement the conventional image processing pipeline to enable fast and easy application to new problems, making it easy to apply and test conventional methods alongside deep learning with minimal overhead.


Verlagsausgabe §
DOI: 10.5445/IR/1000167364
Veröffentlicht am 06.02.2024
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Automation und angewandte Informatik (IAI)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2024
Sprache Englisch
Identifikator ISSN: 0932-8092, 1432-1769
KITopen-ID: 1000167364
HGF-Programm 43.31.02 (POF IV, LK 01) Devices and Applications
Erschienen in Machine vision and applications
Verlag Springer
Band 35
Seiten Article no: 25
Nachgewiesen in Dimensions
Scopus
Web of Science
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