Proceedings of the 2017 International Conference on Manufacturing Engineering and Intelligent Materials (ICMEIM 2017)

Design and Test with a Tomato Identification System based on Visual Technologies

Authors
Ya-dong Tang, Jing-zhao Shi, Yong-chang Yu, Yu-jing He, He Li, Shuai-jun Zhang
Corresponding Author
Ya-dong Tang
Available Online February 2017.
DOI
https://doi.org/10.2991/icmeim-17.2017.22How to use a DOI?
Keywords
BQPSO Algorithm, Threshold Segmentation, Offset Extract, Characteristic Matching.
Abstract
In this paper, a boundary control-based algorithm Binary Quantum Particle Swarm Optimization (BQPSO) that considers quantum particle swarm with Delta potential well was used to determine otsu threshold. In the optimization, particles moved in the Delta potential well with the best position POPSIZE as center. The best threshold was determined by updating individual extremum of a single particle pbest and global extremum of particles swarm gbest to their good-enough fitness values, in order for image segmentation. As for profiles, random circle method was used to extract radius of fruit circle. With binocular vision system, a Fourier-transform algorithm was adopted to extract offsets of left and right tomato images, and by marking their sorting baseline, they were matched according to sequential consistency principle.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
2017 International Conference on Manufacturing Engineering and Intelligent Materials (ICMEIM 2017)
Part of series
Advances in Engineering Research
Publication Date
February 2017
ISBN
978-94-6252-317-3
ISSN
2352-5401
DOI
https://doi.org/10.2991/icmeim-17.2017.22How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Ya-dong Tang
AU  - Jing-zhao Shi
AU  - Yong-chang Yu
AU  - Yu-jing He
AU  - He Li
AU  - Shuai-jun Zhang
PY  - 2017/02
DA  - 2017/02
TI  - Design and Test with a Tomato Identification System based on Visual Technologies
BT  - 2017 International Conference on Manufacturing Engineering and Intelligent Materials (ICMEIM 2017)
PB  - Atlantis Press
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmeim-17.2017.22
DO  - https://doi.org/10.2991/icmeim-17.2017.22
ID  - Tang2017/02
ER  -