基于模糊C均值聚类的玻璃瓶缺陷自动检测

Jaina George, S. Janardhana, J. Jaya, K. J. Sabareesaan
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引用次数: 4

摘要

眼镜和玻璃瓶的缺陷导致质量差,这给制造商带来了更多的问题。划痕和裂纹通常是不可避免的,但在生产高质量玻璃的过程中,必须尽量减少它们的发生。手工检查超大尺寸的眼镜是一种令人厌烦的方法。而且人工检测过程缓慢、耗时且容易出现人为错误。使用图像处理的自动缺陷检测系统可以克服许多这些缺点,并为制造商提供显着提高产品质量和降低成本的机会。潜在缺陷检测过程包括图像去噪和模糊C均值聚类算法。根据PSNR值的比较确定最佳滤波方法。然后用通用图像质量指数表示分割参数,Pearson相关系数表示相关量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic defect detection inspectacles and glass bottles based on Fuzzy C Means Clustering
Defects in spectacles and glass bottles result into poor quality and which makes more problems for the manufacturers. Scratches and cracks are typically unavoidable, but their occurrence must be minimized during the production of high-quality glasses. It is a tiresome method to manually inspect very large size glasses. And also the manual inspection process is slow, time-consuming and prone to human error. Automatic defect detection systems using image processing can overcome many of these disadvantages and offer manufacturers an opportunity to significantly improve the product quality and reduce costs. The Potential defects detection process includes image denoising and Fuzzy C Means Clustering Algorithm. Based upon the PSNR value comparison determines the best filtering approach. Then the Universal Image Quality Index shows the segmentation parameters and Pearson correlation coefficient shows the amount of correlation.
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