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Spectral imaging method for material classification and inspection of printed circuit boards

机译：用于印刷电路板材料分类和检查的光谱成像方法

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摘要

We propose a spectral imaging method for material classification and inspection of raw printed circuit boards (PCBs). The method is composed of two steps (1) estimation the PCB surface-spectral reflectances and (2) unsupervised classification of the reflectance data to make the inspection of PCB easy and efficient. First, we develop a spectral imaging system that captures high dynamic range images of a raw PCB with spatially and spectrally high resolutions in the region of visible wavelength. The surface-spectral reflectance is then estimated at every pixel point from multiple spectral images, based on the reflection characteristics of different materials. Second, the surface-spectral reflectance data are classified into several groups, according to the number of PCB materials. We develop an unsupervised classification algorithm incorporating both spectral information and spatial information, based on the Nystrom approximation of the normalized cut method. The initial seeds for the Nystrom procedure are effectively chosen using a guidance module based on the K-means algorithm. Low-dimensional spectral features are efficiently extracted from the original high-dimensional spectral reflectance data. The feasibility of the proposed method is examined in experiments using real PCBs in detail.
机译：我们提出一种光谱成像方法，用于材料分类和原始印刷电路板（PCB）的检查。该方法包括两个步骤：（1）估算PCB表面光谱的反射率，以及（2）对反射率数据进行无监督分类，从而使PCB的检查变得简单而有效。首先，我们开发了一种光谱成像系统，该系统可以捕获原始PCB的高动态范围图像，并在可见波长范围内具有空间和光谱上的高分辨率。然后根据不同材料的反射特性，从多个光谱图像中的每个像素点估计表面光谱反射率。其次，根据PCB材料的数量，将表面光谱反射率数据分为几组。我们基于归一化割方法的Nystrom近似值，开发了一种结合了光谱信息和空间信息的无监督分类算法。使用基于K均值算法的指导模块可以有效地选择Nystrom程序的初始种子。从原始的高维光谱反射率数据中有效地提取出低维光谱特征。在使用实际PCB的实验中详细检查了所提出方法的可行性。

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