Thermography-based material classification using machine learning
Thermography-based material classification using machine learning
复制标题
使用机器学习进行基于热成像的材料分类
DOI：
--
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
M. Eid
中科院分区：
文献类型：
--
作者：
Tamás Aujeszky;Georgios Korres;M. Eid
红外热成像技术已广泛应用于材料的无损评价和检测以及其他定性方法。然而，热成像领域的发展要少得多。大多数现有的研究使用相对简单的模型，而更现实的模型目前正在开发中。热成像的一个有趣的场景是根据物体对激励的热响应来确定物体的材料成分，这可能会导致多模式人机交互，远程操作和非接触式触觉映射等应用。本文提出了一种系统，能够在真实的时间，使用激光激发阶跃热成像和一组机器学习分类器之间的一系列不同的材料的分类。实验结果表明，即使数据集由多个不同的数据采集会话组成，在确定材料的标签方面也具有一致的高精度。
Infrared thermography has been widely used today for nondestructive evaluation and testing of materials and other qualitative approaches. However, the field of thermography is much less developed. Most of the existing research uses a relatively simple model, while more realistic models are currently in development. One interesting scenario for thermography is determining the material composition of objects based on their thermal response to excitation, which could lead to applications such as multimodal human-computer interaction, teleoperation and non-contact haptic mapping. This paper presents a system that is capable of classification between a range of different materials in real time, using laser excitation step thermography and a set of machine learning classifiers. Experimental results demonstrate a consistently high accuracy in determining the label of the material, even when the dataset is composed of multiple different sessions of data acquisition.
用于研究应用的宽带成像系统。
DOI：
10.1063/1.3124796
发表时间：
2009
期刊：
The Review of scientific instruments
影响因子：
--
作者：
Yefremenko,V;Gordiyenko,E;Shustakova,G;Fomenko,Yu;Datesman,A;Wang,G;Pearson,J;Cohen,EEW;Novosad,V
通讯作者：
Novosad,V