Name
Title | ||
|---|---|---|
Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns |
Abstract | ||
|---|---|---|
ABSTRACTWe introduce Deep Thermal Imaging, a new approach for close-range automatic recognition of materials to enhance the understanding of people and ubiquitous technologies of their proximal environment. Our approach uses a low-cost mobile thermal camera integrated into a smartphone to capture thermal textures. A deep neural network classifies these textures into material types. This approach works effectively without the need for ambient light sources or direct contact with materials. Furthermore, the use of a deep learning network removes the need to handcraft the set of features for different materials. We evaluated the performance of the system by training it to recognize 32 material types in both indoor and outdoor environments. Our approach produced recognition accuracies above 98% in 14,860 images of 15 indoor materials and above 89% in 26,584 images of 17 outdoor materials. We conclude by discussing its potentials for real-time use in HCI applications and future directions. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3173574.3173576 | Conference on Human Factors in Computing Systems |
Keywords | DocType | Volume |
Material recognition, in the wild, deep learning, sensing, context-aware mobile computing, thermal imaging | Journal | abs/1803.02310 |
Citations | PageRank | References |
3 | 0.66 | 30 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Youngjun Cho | 1 | 4 | 1.70 |
| Nadia Berthouze | 2 | 123 | 14.38 |
| Nicolai Marquardt | 3 | 1166 | 64.63 |
| Simon Justin Julier | 4 | 38 | 10.49 |