Thermography-based material classification using machine learning

Tamas Aujeszky, Georgios Korres, Mohamad Eid

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Infrared thermography has been widely used today for nondestructive evaluation and testing of materials and other qualitative approaches. However, the field of thermography is much less developed. Most of the existing research uses a relatively simple model, while more realistic models are currently in development. One interesting scenario for thermography is determining the material composition of objects based on their thermal response to excitation, which could lead to applications such as multimodal human-computer interaction, teleoperation and non-contact haptic mapping. This paper presents a system that is capable of classification between a range of different materials in real time, using laser excitation step thermography and a set of machine learning classifiers. Experimental results demonstrate a consistently high accuracy in determining the label of the material, even when the dataset is composed of multiple different sessions of data acquisition.

Original languageEnglish (US)
Title of host publicationHAVE 2017 - IEEE International Symposium on Haptic, Audio-Visual Environments and Games, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
Volume2017-December
ISBN (Electronic)9781538609798
DOIs
StatePublished - Dec 26 2017
Event15th IEEE International Symposium on Haptic, Audio-Visual Environments and Games, HAVE 2017 - Abu Dhabi, United Arab Emirates
Duration: Oct 22 2017Oct 23 2017

Other

Other15th IEEE International Symposium on Haptic, Audio-Visual Environments and Games, HAVE 2017
CountryUnited Arab Emirates
CityAbu Dhabi
Period10/22/1710/23/17

Fingerprint

Learning systems
Laser excitation
Human computer interaction
Remote control
Labels
Data acquisition
Classifiers
Testing
Chemical analysis

Keywords

  • haptic mapping
  • Laser thermography
  • machine learning
  • material characterization

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Media Technology

Cite this

Aujeszky, T., Korres, G., & Eid, M. (2017). Thermography-based material classification using machine learning. In HAVE 2017 - IEEE International Symposium on Haptic, Audio-Visual Environments and Games, Proceedings (Vol. 2017-December, pp. 1-6). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/HAVE.2017.8240344

Thermography-based material classification using machine learning. / Aujeszky, Tamas; Korres, Georgios; Eid, Mohamad.

HAVE 2017 - IEEE International Symposium on Haptic, Audio-Visual Environments and Games, Proceedings. Vol. 2017-December Institute of Electrical and Electronics Engineers Inc., 2017. p. 1-6.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Aujeszky, T, Korres, G & Eid, M 2017, Thermography-based material classification using machine learning. in HAVE 2017 - IEEE International Symposium on Haptic, Audio-Visual Environments and Games, Proceedings. vol. 2017-December, Institute of Electrical and Electronics Engineers Inc., pp. 1-6, 15th IEEE International Symposium on Haptic, Audio-Visual Environments and Games, HAVE 2017, Abu Dhabi, United Arab Emirates, 10/22/17. https://doi.org/10.1109/HAVE.2017.8240344
Aujeszky T, Korres G, Eid M. Thermography-based material classification using machine learning. In HAVE 2017 - IEEE International Symposium on Haptic, Audio-Visual Environments and Games, Proceedings. Vol. 2017-December. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1-6 https://doi.org/10.1109/HAVE.2017.8240344
Aujeszky, Tamas ; Korres, Georgios ; Eid, Mohamad. / Thermography-based material classification using machine learning. HAVE 2017 - IEEE International Symposium on Haptic, Audio-Visual Environments and Games, Proceedings. Vol. 2017-December Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1-6
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