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

机译：基于视觉技术的番茄识别系统设计与测试

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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.
机译：在本文中，使用基于边界控制的算法二进制量子粒子群群优化（BQPSO），其考虑量子粒子蜂拥的Δ级井井来确定OTSU阈值。 在优化中，粒子在Δ势阱中移动，最佳位置爆发为中心。 通过更新单个粒子的单个极值和全球极值的颗粒的颗粒的单个极值来确定最佳阈值，以进行图像分割。 至于配置文件，用于提取水果圈半径的随机圆形方法。 采用双目视觉系统，采用傅里叶变换算法提取左右番茄图像的偏移，并通过标记它们的排序基线，根据顺序一致性原则匹配。

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