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Material Classification for Printed Circuit Boards by Spectral Imaging System

机译：光谱成像系统对印刷电路板的材料分类

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摘要

This paper presents an approach to a reliable material classification for printed circuit boards (PCBs) by constructing a spectral imaging system. The system works in the whole spectral range [400-700nm] and the high spectral resolution. An algorithm is presented for effectively classifying the surface material on each pixel point into several elements such as substrate, metal, resist, footprint, and paint, based on the surface-spectral reflectance estimated from the spectral imaging data. The proposed approach is an incorporation of spectral reflectance estimation, spectral feature extraction, and image segmentation processes for material classification of raw PCBs. The performance of the proposed method is compared with other methods using the RGB-reflectance based algorithm, the k-means algorithm and the normalized cut algorithm. The experimental results show the superiority of our method in accuracy and computational cost.
机译：本文提出了一种通过构建光谱成像系统对印刷电路板（PCB）进行可靠的材料分类的方法。该系统可在整个光谱范围[400-700nm]和高光谱分辨率下工作。提出了一种算法，可根据从光谱成像数据估计的表面光谱反射率，有效地将每个像素点上的表面材料分类为几种元素，例如基材，金属，抗蚀剂，覆盖区和油漆。所提出的方法是将光谱反射率估计，光谱特征提取和图像分割过程结合在一起，以对原始PCB进行材料分类。使用基于RGB反射率的算法，k均值算法和归一化剪切算法，将该方法的性能与其他方法进行了比较。实验结果表明我们的方法在准确性和计算成本上具有优势。

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