Abstract
In this paper, we propose a 4-Dimensional Higher-order Local Auto-Correlation (4D HLAC). The method aims to extract the features of a 3D time series, which is regarded as a 4D static pattern. This is an orthodox extension of the original HLAC, which represents correlations among local values in 2D images and can effectively summarize motion in 3D space. To recognize motion in the real world, a recognition system should exploit motion information from the real-world structure. The 4D HLAC feature vector is expected to capture representations for general 3D motion recognition, because the original HLAC performed very well in image recognition tasks. Based on experimental results showing high recognition performance and low computational cost, we conclude that our method has a strong advantage for 3D time series recognition, even in practical situations.
| Original language | English |
|---|---|
| Title of host publication | ICPRAM 2015 - 4th International Conference on Pattern Recognition Applications and Methods, Proceedings |
| Publisher | SciTePress |
| Pages | 223-231 |
| Number of pages | 9 |
| Volume | 1 |
| ISBN (Electronic) | 9789897580765 |
| Publication status | Published - 2015 |
| Externally published | Yes |
| Event | 4th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2015 - Lisbon, Portugal Duration: 2015 Jan 10 → 2015 Jan 12 |
Other
| Other | 4th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2015 |
|---|---|
| Country | Portugal |
| City | Lisbon |
| Period | 15/1/10 → 15/1/12 |
Fingerprint
Keywords
- 4-dimensional pattern recognition
- Higher-Order Local Auto-Correlation
- IXMAS Dataset
- Point cloud time series
- Tesseractic pattern
- Voxel time series
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
Cite this
3-Dimensional motion recognition by 4-dimensional Higher-Order Local Auto-Correlation. / Mori, Hiroki; Kanda, Takaomi; Hirose, Dai; Asada, Minoru.
ICPRAM 2015 - 4th International Conference on Pattern Recognition Applications and Methods, Proceedings. Vol. 1 SciTePress, 2015. p. 223-231.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - 3-Dimensional motion recognition by 4-dimensional Higher-Order Local Auto-Correlation
AU - Mori, Hiroki
AU - Kanda, Takaomi
AU - Hirose, Dai
AU - Asada, Minoru
PY - 2015
Y1 - 2015
N2 - In this paper, we propose a 4-Dimensional Higher-order Local Auto-Correlation (4D HLAC). The method aims to extract the features of a 3D time series, which is regarded as a 4D static pattern. This is an orthodox extension of the original HLAC, which represents correlations among local values in 2D images and can effectively summarize motion in 3D space. To recognize motion in the real world, a recognition system should exploit motion information from the real-world structure. The 4D HLAC feature vector is expected to capture representations for general 3D motion recognition, because the original HLAC performed very well in image recognition tasks. Based on experimental results showing high recognition performance and low computational cost, we conclude that our method has a strong advantage for 3D time series recognition, even in practical situations.
AB - In this paper, we propose a 4-Dimensional Higher-order Local Auto-Correlation (4D HLAC). The method aims to extract the features of a 3D time series, which is regarded as a 4D static pattern. This is an orthodox extension of the original HLAC, which represents correlations among local values in 2D images and can effectively summarize motion in 3D space. To recognize motion in the real world, a recognition system should exploit motion information from the real-world structure. The 4D HLAC feature vector is expected to capture representations for general 3D motion recognition, because the original HLAC performed very well in image recognition tasks. Based on experimental results showing high recognition performance and low computational cost, we conclude that our method has a strong advantage for 3D time series recognition, even in practical situations.
KW - 4-dimensional pattern recognition
KW - Higher-Order Local Auto-Correlation
KW - IXMAS Dataset
KW - Point cloud time series
KW - Tesseractic pattern
KW - Voxel time series
UR - http://www.scopus.com/inward/record.url?scp=84938882272&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84938882272&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84938882272
VL - 1
SP - 223
EP - 231
BT - ICPRAM 2015 - 4th International Conference on Pattern Recognition Applications and Methods, Proceedings
PB - SciTePress
ER -