TY - GEN
T1 - Thermography-based material classification using machine learning
AU - Aujeszky, Tamas
AU - Korres, Georgios
AU - Eid, Mohamad
PY - 2017/12/26
Y1 - 2017/12/26
N2 - 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.
AB - 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.
KW - Laser thermography
KW - haptic mapping
KW - machine learning
KW - material characterization
UR - http://www.scopus.com/inward/record.url?scp=85048745295&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048745295&partnerID=8YFLogxK
U2 - 10.1109/HAVE.2017.8240344
DO - 10.1109/HAVE.2017.8240344
M3 - Conference contribution
AN - SCOPUS:85048745295
T3 - HAVE 2017 - IEEE International Symposium on Haptic, Audio-Visual Environments and Games, Proceedings
SP - 1
EP - 6
BT - HAVE 2017 - IEEE International Symposium on Haptic, Audio-Visual Environments and Games, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Symposium on Haptic, Audio-Visual Environments and Games, HAVE 2017
Y2 - 22 October 2017 through 23 October 2017
ER -