High-speed and reliable object recognition based on low-dimensional local shape features

Masanobu Nagase; Shuichi Akizuki; Manabu Hashimoto

doi:10.1109/ICARCV.2014.7064284

High-speed and reliable object recognition based on low-dimensional local shape features

Masanobu Nagase, Shuichi Akizuki, Manabu Hashimoto

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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	https://doi.org/10.1109/ICARCV.2014.7064284
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

ASJC Scopus subject areas

Computer Vision and Pattern Recognition
Human-Computer Interaction
Artificial Intelligence
Control and Systems Engineering

Access to Document

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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

Nagase, M, Akizuki, S & Hashimoto, M 1997, High-speed and reliable object recognition based on low-dimensional local shape features. in 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014., 7064284, Institute of Electrical and Electronics Engineers Inc., pp. 82-87, 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014, Singapore, Singapore, 14/12/10. https://doi.org/10.1109/ICARCV.2014.7064284

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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.",

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