Design and Test with a Tomato Identification System based on Visual Technologies
Ya-dong Tang, Jing-zhao Shi, Yong-chang Yu, Yu-jing He, He Li, Shuai-jun Zhang
Available Online February 2017.
- https://doi.org/10.2991/icmeim-17.2017.22How to use a DOI?
- BQPSO Algorithm, Threshold Segmentation, Offset Extract, Characteristic Matching.
- 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.
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 SP - 115 EP - 122 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 -