Robust minimization of lighting variation for real-time defect detection

Edmund Y. Lam
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引用次数: 4

Abstract

In machine vision applications that involve comparing two images, it is necessary to match the capture conditions, which can affect their graylevels. Illumination and exposure are two important causes for lighting variation that we should compensate for in the resulting images. A standard technique for this purpose is to map one of the images to achieve the smallest mean square error (MSE) between the two. However, applications in defect detection for manufacturing processes are more challenging, because the existence of defects would affect the mapping significantly. In this paper, we present a robust method that is more tolerant to defects, and discuss its formulation as a linear programming to achieve fast implementations. This algorithm is also flexible and capable of incorporating further constraints, such as ensuring non-negativity of the pixel values.

实时缺陷检测中光照变化的鲁棒最小化
在涉及比较两幅图像的机器视觉应用中,有必要匹配捕获条件,这可能会影响它们的灰度。光照和曝光是导致光照变化的两个重要原因,我们应该在生成的图像中进行补偿。用于此目的的标准技术是映射其中一张图像以实现两者之间的最小均方误差(MSE)。然而,缺陷检测在制造过程中的应用更具挑战性,因为缺陷的存在会严重影响映射。本文提出了一种对缺陷容忍度更高的鲁棒方法,并讨论了其作为线性规划的形式以实现快速实现。该算法还具有灵活性,能够纳入进一步的约束,例如确保像素值的非负性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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