Proceedings of the 2019 International Conference on Precision Machining, Non-Traditional Machining and Intelligent Manufacturing (PNTIM 2019)

Object Depth Measurement Based on Feature Point Detection

Authors
Lai Yufeng, Wang Zhongsheng
Corresponding Author
Lai Yufeng
Available Online November 2019.
DOI
10.2991/pntim-19.2019.66How to use a DOI?
Keywords
Component; Feature Points; Absolute Depth; Image Segmentation; the Object Matching
Abstract

In order to simplify the method of obtaining absolute depth information, this paper proposes a method of automatic measurement of object depth information in images by monocular camera without calibration and adjustment of camera parameters, which is used to realize an automatic depth measurement system. This method uses the feature points on the object image to measure the depth of the object to enhance the robustness of the algorithm against partial occlusion or missing of the measured object in the scene. Experimental results show that the method is effective.

Copyright
© 2019, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2019 International Conference on Precision Machining, Non-Traditional Machining and Intelligent Manufacturing (PNTIM 2019)
Series
Atlantis Highlights in Engineering
Publication Date
November 2019
ISBN
978-94-6252-829-1
ISSN
2589-4943
DOI
10.2991/pntim-19.2019.66How to use a DOI?
Copyright
© 2019, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Lai Yufeng
AU  - Wang Zhongsheng
PY  - 2019/11
DA  - 2019/11
TI  - Object Depth Measurement Based on Feature Point Detection
BT  - Proceedings of the 2019 International Conference on Precision Machining, Non-Traditional Machining and Intelligent Manufacturing (PNTIM 2019)
PB  - Atlantis Press
SP  - 321
EP  - 325
SN  - 2589-4943
UR  - https://doi.org/10.2991/pntim-19.2019.66
DO  - 10.2991/pntim-19.2019.66
ID  - Yufeng2019/11
ER  -