Abstract
This study compares a deep learning approach with the traditional computer vision method of ellipse detection on the task of detecting semi-transparent drinking glasses filled with water in images. Deep neural networks can, in principle, be trained until they exhibit excellent performance in terms of detection accuracy. However, their ability to generalise to different types of surroundings relies on large amounts of training data, while ellipse detection can work in any environment without requiring additional data or algorithm tuning. Two deep neural networks trained on different image data sets containing drinking glasses were tested in this study. Both networks achieved high levels of detection accuracy, independently of the test image resolution. In contrast, the ellipse detection method was less consistent, greatly depending on the visibility of the top and bottom of the glasses, and water levels. The method detected the top of the glasses in less than half of the cases, at lower resolutions; and detection results were even worse for the water level and bottom of the glasses, in all resolutions.
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Abdul Jabbar was supported by a UNRSC50:50 PhD scholarship from The University of Newcastle.
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Jabbar, A., Mendes, A., Chalup, S. (2019). Comparing Ellipse Detection and Deep Neural Networks for the Identification of Drinking Glasses in Images. In: Tzovaras, D., Giakoumis, D., Vincze, M., Argyros, A. (eds) Computer Vision Systems. ICVS 2019. Lecture Notes in Computer Science(), vol 11754. Springer, Cham. https://doi.org/10.1007/978-3-030-34995-0_29
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