Segmentation and classification of THCs on PCBAs

Daniel Herchenbach, Wei Li, Matthias Breier
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引用次数: 13

Abstract

The dramatic increase of electronic waste requires automatic recycling, including technologies from machine vision. A framework for segmentation and classification of THC (through-hole components) mounted on PCBAs is presented, using both RGB and depth frames from the Kinect sensor by Microsoft. A segmentation approach, combining local and global features in a flexible manner, is shown to optimize a freely definable cost function globally. We interleave segmentation and classification as we form the final components using a simple, yet robust shape model.
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pcb上THCs的分割与分类
电子垃圾的急剧增加需要自动回收,包括机器视觉技术。采用微软Kinect传感器的RGB帧和深度帧,提出了一种对安装在pcb上的THC(通孔组件)进行分割和分类的框架。采用一种结合局部和全局特征的分割方法,对可自由定义的代价函数进行全局优化。我们交织分割和分类,因为我们形成使用一个简单的,但稳健的形状模型的最终组件。
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