String shape recognition using enhanced matching method from 3D point cloud data

Tomoya Shirakawa; Takayuki Matsuno; Akira Yanou; Mamoru Minami

doi:10.1109/SII.2015.7405021

String shape recognition using enhanced matching method from 3D point cloud data

Tomoya Shirakawa, Takayuki Matsuno, Akira Yanou, Mamoru Minami

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

4 Citations (Scopus)

Abstract

The deformable object such as string, cloth and paper, is soft and can change its shape easily. It is difficult for a robot to manipulate deformable object because it needs to deal with various shape. For the operation of deformable object, it is important to recognize the shape of them. In our previous research, point chain model and the string shape recognition method were proposed. Point chain model is a structure to describe a string shape by a series of connections of points. And the string shape recognition method are algorithms to recognize a string shape from 3D point cloud data and output point chain model. However, the previous method sometimes occurred misrecognition of segments. Therefore, enhanced matching method is proposed to improve recognition performance.

Original language	English
Title of host publication	2015 IEEE/SICE International Symposium on System Integration, SII 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	449-454
Number of pages	6
ISBN (Print)	9781467372428
DOIs	https://doi.org/10.1109/SII.2015.7405021
Publication status	Published - Feb 10 2016
Event	8th Annual IEEE/SICE International Symposium on System Integration, SII 2015 - Nagoya, Japan Duration: Dec 11 2015 → Dec 13 2015

Other

Other	8th Annual IEEE/SICE International Symposium on System Integration, SII 2015
Country/Territory	Japan
City	Nagoya
Period	12/11/15 → 12/13/15

ASJC Scopus subject areas

Artificial Intelligence
Control and Systems Engineering

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10.1109/SII.2015.7405021

Cite this

String shape recognition using enhanced matching method from 3D point cloud data. / Shirakawa, Tomoya; Matsuno, Takayuki; Yanou, Akira et al.
2015 IEEE/SICE International Symposium on System Integration, SII 2015. Institute of Electrical and Electronics Engineers Inc., 2016. p. 449-454 7405021.

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

Shirakawa, T, Matsuno, T, Yanou, A & Minami, M 2016, String shape recognition using enhanced matching method from 3D point cloud data. in 2015 IEEE/SICE International Symposium on System Integration, SII 2015., 7405021, Institute of Electrical and Electronics Engineers Inc., pp. 449-454, 8th Annual IEEE/SICE International Symposium on System Integration, SII 2015, Nagoya, Japan, 12/11/15. https://doi.org/10.1109/SII.2015.7405021

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title = "String shape recognition using enhanced matching method from 3D point cloud data",

abstract = "The deformable object such as string, cloth and paper, is soft and can change its shape easily. It is difficult for a robot to manipulate deformable object because it needs to deal with various shape. For the operation of deformable object, it is important to recognize the shape of them. In our previous research, point chain model and the string shape recognition method were proposed. Point chain model is a structure to describe a string shape by a series of connections of points. And the string shape recognition method are algorithms to recognize a string shape from 3D point cloud data and output point chain model. However, the previous method sometimes occurred misrecognition of segments. Therefore, enhanced matching method is proposed to improve recognition performance.",

author = "Tomoya Shirakawa and Takayuki Matsuno and Akira Yanou and Mamoru Minami",

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AB - The deformable object such as string, cloth and paper, is soft and can change its shape easily. It is difficult for a robot to manipulate deformable object because it needs to deal with various shape. For the operation of deformable object, it is important to recognize the shape of them. In our previous research, point chain model and the string shape recognition method were proposed. Point chain model is a structure to describe a string shape by a series of connections of points. And the string shape recognition method are algorithms to recognize a string shape from 3D point cloud data and output point chain model. However, the previous method sometimes occurred misrecognition of segments. Therefore, enhanced matching method is proposed to improve recognition performance.

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String shape recognition using enhanced matching method from 3D point cloud data

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