Abstract
In this paper, we propose a high-speed 3-D object recognition method using new feature values. Features for the object recognition method proposed in this study consist of three values. One is the Difference of Normals (DoN) feature value that has been proposed by Ioannou. The other two represent information about curvature. We use these three-dimensional features to recognize the position and pose of multiple objects stacked randomly. Because they are low-dimensional, high-speed matching can be achieved. We have also reduced the computing time needed for data matching by using only effective points selected on the basis of their estimated distinctiveness. Experimental results using actual scenes have demonstrated that the computing time is about 93 times faster than that of the conventional SHOT method. Furthermore, the proposed method achieves a 98.2% recognition rate, which is 17.9% higher than that of the SHOT method. Also, we confirmed that the proposed method achieves higher-speed matching and higher recognition success rate than the conventional methods.
| Original language | English |
|---|---|
| Title of host publication | 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 82-87 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781479951994 |
| DOIs | |
| Publication status | Published - 1997 Mar 19 |
| Externally published | Yes |
| Event | 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014 - Singapore, Singapore Duration: 2014 Dec 10 → 2014 Dec 12 |
Other
| Other | 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014 |
|---|---|
| Country | Singapore |
| City | Singapore |
| Period | 14/12/10 → 14/12/12 |
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ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Human-Computer Interaction
- Artificial Intelligence
- Control and Systems Engineering
Cite this
High-speed and reliable object recognition based on low-dimensional local shape features. / Nagase, Masanobu; Akizuki, Shuichi; Hashimoto, Manabu.
2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014. Institute of Electrical and Electronics Engineers Inc., 1997. p. 82-87 7064284.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - High-speed and reliable object recognition based on low-dimensional local shape features
AU - Nagase, Masanobu
AU - Akizuki, Shuichi
AU - Hashimoto, Manabu
PY - 1997/3/19
Y1 - 1997/3/19
N2 - In this paper, we propose a high-speed 3-D object recognition method using new feature values. Features for the object recognition method proposed in this study consist of three values. One is the Difference of Normals (DoN) feature value that has been proposed by Ioannou. The other two represent information about curvature. We use these three-dimensional features to recognize the position and pose of multiple objects stacked randomly. Because they are low-dimensional, high-speed matching can be achieved. We have also reduced the computing time needed for data matching by using only effective points selected on the basis of their estimated distinctiveness. Experimental results using actual scenes have demonstrated that the computing time is about 93 times faster than that of the conventional SHOT method. Furthermore, the proposed method achieves a 98.2% recognition rate, which is 17.9% higher than that of the SHOT method. Also, we confirmed that the proposed method achieves higher-speed matching and higher recognition success rate than the conventional methods.
AB - In this paper, we propose a high-speed 3-D object recognition method using new feature values. Features for the object recognition method proposed in this study consist of three values. One is the Difference of Normals (DoN) feature value that has been proposed by Ioannou. The other two represent information about curvature. We use these three-dimensional features to recognize the position and pose of multiple objects stacked randomly. Because they are low-dimensional, high-speed matching can be achieved. We have also reduced the computing time needed for data matching by using only effective points selected on the basis of their estimated distinctiveness. Experimental results using actual scenes have demonstrated that the computing time is about 93 times faster than that of the conventional SHOT method. Furthermore, the proposed method achieves a 98.2% recognition rate, which is 17.9% higher than that of the SHOT method. Also, we confirmed that the proposed method achieves higher-speed matching and higher recognition success rate than the conventional methods.
UR - http://www.scopus.com/inward/record.url?scp=84949925226&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84949925226&partnerID=8YFLogxK
U2 - 10.1109/ICARCV.2014.7064284
DO - 10.1109/ICARCV.2014.7064284
M3 - Conference contribution
AN - SCOPUS:84949925226
SP - 82
EP - 87
BT - 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014
PB - Institute of Electrical and Electronics Engineers Inc.
ER -