Abstract
This paper introduces a reliable 3-D object recognition method which can statistically minimize its mismatching. This method basically uses a 3-D object model which is represented as a set of feature points with 3-D coordinate. Each feature point also has an attribute value about local shape around the point. The attribute value is represented as an orientation histogram of normal vector, which can be calculated by using several neighboring feature points around each point. Here, this attribute value means its local shape. By estimating relative similarity of two points of all possible combination in the model, the distinctiveness of each point is defined. In the proposed method, only a small number of distinctive feature points are selected and used for matching with all feature points extracted from an acquired range image. Finally, position and pose of the target object can be estimated from some correctly matched points. Experimental results using real scene have proved that recognition rate of this method is 93.8%, which is 42.2% higher than the conventional Spin Image method. Also, computing time is about nine times faster than that.
| Original language | English |
|---|---|
| Pages (from-to) | 1058-1062 |
| Number of pages | 5 |
| Journal | Seimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering |
| Volume | 79 |
| Issue number | 11 |
| Publication status | Published - 2013 Nov |
| Externally published | Yes |
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Keywords
- 3-D descriptor
- 3-D feature point matching
- Bin-picking
- Object recognition
- Point cloud data
- Robot vision
ASJC Scopus subject areas
- Mechanical Engineering
Cite this
Reliable object recognition using 3-D feature points for minimizing its mismatching. / Nagase, Masanobu; Akizuki, Shuichi; Hashimoto, Manabu.
In: Seimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering, Vol. 79, No. 11, 11.2013, p. 1058-1062.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Reliable object recognition using 3-D feature points for minimizing its mismatching
AU - Nagase, Masanobu
AU - Akizuki, Shuichi
AU - Hashimoto, Manabu
PY - 2013/11
Y1 - 2013/11
N2 - This paper introduces a reliable 3-D object recognition method which can statistically minimize its mismatching. This method basically uses a 3-D object model which is represented as a set of feature points with 3-D coordinate. Each feature point also has an attribute value about local shape around the point. The attribute value is represented as an orientation histogram of normal vector, which can be calculated by using several neighboring feature points around each point. Here, this attribute value means its local shape. By estimating relative similarity of two points of all possible combination in the model, the distinctiveness of each point is defined. In the proposed method, only a small number of distinctive feature points are selected and used for matching with all feature points extracted from an acquired range image. Finally, position and pose of the target object can be estimated from some correctly matched points. Experimental results using real scene have proved that recognition rate of this method is 93.8%, which is 42.2% higher than the conventional Spin Image method. Also, computing time is about nine times faster than that.
AB - This paper introduces a reliable 3-D object recognition method which can statistically minimize its mismatching. This method basically uses a 3-D object model which is represented as a set of feature points with 3-D coordinate. Each feature point also has an attribute value about local shape around the point. The attribute value is represented as an orientation histogram of normal vector, which can be calculated by using several neighboring feature points around each point. Here, this attribute value means its local shape. By estimating relative similarity of two points of all possible combination in the model, the distinctiveness of each point is defined. In the proposed method, only a small number of distinctive feature points are selected and used for matching with all feature points extracted from an acquired range image. Finally, position and pose of the target object can be estimated from some correctly matched points. Experimental results using real scene have proved that recognition rate of this method is 93.8%, which is 42.2% higher than the conventional Spin Image method. Also, computing time is about nine times faster than that.
KW - 3-D descriptor
KW - 3-D feature point matching
KW - Bin-picking
KW - Object recognition
KW - Point cloud data
KW - Robot vision
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UR - http://www.scopus.com/inward/citedby.url?scp=84888251624&partnerID=8YFLogxK
M3 - Article
VL - 79
SP - 1058
EP - 1062
JO - Seimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering
JF - Seimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering
SN - 0912-0289
IS - 11
ER -