利用智能方法提高电子器件涂层质量

S.K. Speransky, I. Rodionov, K. S. Speransky
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引用次数: 0

摘要

本文提出了一种基于弱分类器的目标检测系统,并将其应用于等离子喷涂涂层。为了提高分类器的性能,我们使用了由Sobel算子生成的半色调图像和梯度图像的组合。每个弱分类器计算一个检测窗口的特征值。分别用300张阳性和300张阴性图像训练检测器,用200张阳性和200张阴性图像进行检测。首先,patch与13个滤波器中的一个进行卷积,包括delta函数、高斯导数、拉普拉斯、角检测器和边缘检测器。然后,将30个空间模板中的一个应用于过滤后的patch。该方法在MATLAB环境下实现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Use of Intelligent Approaches to Improve the Quality of Coatings on Electronic Devices
In this paper, we present an object detection system with a set of weak classifiers and its application to plasma sprayed coatings. In order to improve performance of the classifier, we used combinations of halftone images and gradient images generated by the Sobel operator. Each weak classifier computes its feature value for a detection window. Three hundred positive and 300 negative snapshots were used to train the detector, while 200 positive and 200 negative images were used for its testing. At first a patch is convolved with one of 13 filters, including delta function, Gaussian derivatives, Laplacian, corner detectors and edge detectors. Then, one of 30 spatial templates applies to the filtered patch. This method was implemented in the MATLAB environment.
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