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颜色隐形眼镜的中心偏差测量基于深度学习模型和高圆转换的颜色隐形眼镜
Gi-Nam Kim1, Sung-Hoon Kim1, In Joo1
1Department of Computer Science, Chungbuk National University, Cheongju 28644, Republic of Korea.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
这项研究引入了一种使用图像处理检测彩色隐形眼镜中中心偏差缺陷的新方法. 该技术显著减少了检测错误,改善了眼睛佩戴设备的质量控制.
科学领域:
- 眼科制造业 眼科制造业 眼科制造业
- 图像处理 图像处理
- 质量控制 质量控制
背景情况:
- 彩色隐形眼镜需要严格的质量控制,因为眼睛会直接磨损.
- 中心偏差 (CD) 缺陷,其中有色区域 (CA) 偏离中心,是一个重要的质量问题.
- 准确测量CA偏差对于缺陷检测至关重要.
研究的目的:
- 开发和评估一种用于检测彩色隐形眼镜中中心偏差缺陷的图像处理方法.
- 量化拟议方法在测量彩色区域与中心点的偏差方面的准确性.
主要方法:
- 利用语义细分来简化镜头图像和最小化噪音.
- 采用Hough圆转换算法,精确测量彩色区域中心的偏差.
- 应用图像分析技术用于缺陷检测.
主要成果:
- 提出的方法有效地检测到彩色隐形眼镜的中心偏差缺陷.
- 与现有研究方法相比,错误率降低了71.2%.
- 证明了语义细分和霍夫圆形转换对该应用程序的有效性.
结论:
- 开发的图像处理技术为识别彩色隐形眼镜生产中的CD缺陷提供了可靠和准确的解决方案.
- 这种方法通过显著减少测量误差来提高质量控制.
- 这种方法有望提高彩色隐形眼镜的安全性和质量.
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