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 proceedingConference 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 languageEnglish
Title of host publication2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages82-87
Number of pages6
ISBN (Electronic)9781479951994
DOIs
Publication statusPublished - 1997 Mar 19
Externally publishedYes
Event2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014 - Singapore, Singapore
Duration: 2014 Dec 102014 Dec 12

Other

Other2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014
CountrySingapore
CitySingapore
Period14/12/1014/12/12

Fingerprint

Object recognition

ASJC Scopus subject areas

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

Cite this

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 (pp. 82-87). [7064284] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICARCV.2014.7064284

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 proceedingConference 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
Nagase M, Akizuki S, Hashimoto M. 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. Institute of Electrical and Electronics Engineers Inc. 1997. p. 82-87. 7064284 https://doi.org/10.1109/ICARCV.2014.7064284
Nagase, Masanobu ; Akizuki, Shuichi ; Hashimoto, Manabu. / High-speed and reliable object recognition based on low-dimensional local shape features. 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014. Institute of Electrical and Electronics Engineers Inc., 1997. pp. 82-87
@inproceedings{7738e58016a943b38e4eba4b9405dabb,
title = "High-speed and reliable object recognition based on low-dimensional local shape features",
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.",
author = "Masanobu Nagase and Shuichi Akizuki and Manabu Hashimoto",
year = "1997",
month = "3",
day = "19",
doi = "10.1109/ICARCV.2014.7064284",
language = "English",
pages = "82--87",
booktitle = "2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

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 -