Sparse Reconstructed Bovine Sign Measurement Method Based on SFM

DOI

10.2991/smont-19.2019.33How to use a DOI?

Keywords

SIFT feature points; SFM; Sparse reconstruction; Three-dimensional measurement

Abstract

[Objective] In order to improve the efficiency of measurement and reduce the labor force, a non-contact measurement method for cattle breeding in modern animal husbandry was developed. [Method] A sparse reconstruction method based on a few two-dimensional images is proposed, which includes four parts: extracting feature points using SIFT algorithm; matching the extracted feature points; calculating the three-dimensional coordinates of feature points; and finally sparse reconstruction based on SFM algorithm. [Conclusion] The bovine model reconstructed by this method can effectively solve the error problem in most manual measurements. It has relatively good applicability and robustness, high measurement accuracy, and high measurement efficiency, which also meets the requirements of evaluation of trait indicators. It can be widely used in the field of bovine physical measurement.

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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TY  - CONF
AU  - Wei Shi
AU  - Yuanyuan Tian
AU  - Yingjiu Huang
AU  - Wankai Zhang
PY  - 2019/04
DA  - 2019/04
TI  - Sparse Reconstructed Bovine Sign Measurement Method Based on SFM
BT  - Proceedings of the 2019 International Conference on Modeling, Simulation, Optimization and Numerical Techniques (SMONT 2019)
PB  - Atlantis Press
SP  - 150
EP  - 152
SN  - 1951-6851
UR  - https://doi.org/10.2991/smont-19.2019.33
DO  - 10.2991/smont-19.2019.33
ID  - Shi2019/04
ER  -