Abstract
In this paper we demonstrate a system capable of classifying different types of materials in a contactless fashion by using active infrared thermography and machine learning algorithms. A laser diode heats the materials and the infrared camera records the thermal dissipation signature of each material. These data are then fed to machine learning algorithms to classify the materials. This system can potentially be used in teleoperation applications for robots that operate in unknown scenes.
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Concrete, PE block, acrylic, coal, marble, sorbothane, LPL, HPL, steel and silicone.
References
Aujeszky, T., Korres, G., Eid, M.: Thermography-based material classification using machine learning. In: 2017 IEEE International Symposium on Haptic, Audio and Visual Environments and Games (HAVE), pp. 1–6. IEEE (2017)
Aujeszky, T., Korres, G., Eid, M.: Measurement-based thermal modeling using laser thermography. IEEE Trans. Instrum. Meas. 67(6), 1359–1369 (2018)
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Aujeszky, T., Korres, G., Eid, M. (2019). Haptic Eye: A Contactless Material Classification System. In: Kajimoto, H., Lee, D., Kim, SY., Konyo, M., Kyung, KU. (eds) Haptic Interaction. AsiaHaptics 2018. Lecture Notes in Electrical Engineering, vol 535. Springer, Singapore. https://doi.org/10.1007/978-981-13-3194-7_25
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DOI: https://doi.org/10.1007/978-981-13-3194-7_25
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Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-3193-0
Online ISBN: 978-981-13-3194-7
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