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DOI: 10.5445/IR/1000087843
Veröffentlicht am 26.11.2018
DOI: 10.5194/isprs-archives-XLII-1-401-2018

Comparison of two methods for 2D pose estimation of industrial workpieces in images - CNN vs. Classical image processing system

Siegfarth, C.; Voegtle, T.; Fabinski, C.

Today, automatic image analysis is one of the basic approaches in the field of industrial applications. One of frequent tasks is pose estimation of objects which can be solved by different methods of image analysis. For comparison two of them have been selected and investigated in this project: Convolutional Neural Networks (CNNs) and a classical method of image analysis based on contour extraction. The main point of interest was to investigate the potential and limits of CNNs to fulfil the requirements of this special task regarding accuracy, reliability and time performance. The classical approach served as comparison to a state-of-the-art solution. The workpiece for these investigations was a commonly used transistor element. As database an image archive consisting of 9000 images with different illumination and perspective conditions has been generated. One part was used for training of the CNN and the creation of a so-called shape model respectively, the rest for the investigation of the extraction quality. With CNN technique two different approaches have been realised. Even if CNNs are predestined for classification this method ... mehr

Zugehörige Institution(en) am KIT Institut für Photogrammetrie und Fernerkundung (IPF)
Publikationstyp Zeitschriftenaufsatz
Jahr 2018
Sprache Englisch
Identifikator ISSN: 1682-1750
URN: urn:nbn:de:swb:90-878436
KITopen-ID: 1000087843
Erschienen in The international archives of photogrammetry, remote sensing and spatial information sciences
Band 42
Heft 1
Seiten 401-405
Bemerkung zur Veröffentlichung 2018 ISPRS Technical Commission I Midterm Symposium on Innovative Sensing - From Sensors to Methods and Applications; Karlsruhe; Germany; 10 October 2018 through 12 October 2018. Ed.: Weinmann
Schlagworte Automatic image analysis, CNN, Shape model, Industrial Application
Nachgewiesen in Scopus
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