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

DOI

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.

Copyright

© 2017, 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  - 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  - Proceedings of the 2017 International Conference on Manufacturing Engineering and Intelligent Materials (ICMEIM 2017)
PB  - Atlantis Press
SP  - 115
EP  - 122
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmeim-17.2017.22
DO  - 10.2991/icmeim-17.2017.22
ID  - Tang2017/02
ER  -