Multimodal Feature-Based Surface Material Classification
Multimodal Feature-Based Surface Material Classification
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DOI：
10.1109/toh.2016.2625787
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发表时间：
2017-04-01
影响因子：
2.9
通讯作者：
Steinbach, Eckehard
中科院分区：
文献类型：
--
作者：
Strese, Matti;Schuwerk, Clemens;Steinbach, Eckehard
当工具在物体表面上敲击或拖动时，在工具中引起振动，这可以使用加速度传感器来捕获。工具-表面相互作用还产生可听见的声波，其可以使用麦克风记录。从相机图像中提取的特征提供了关于表面的附加信息。我们提出了一种工具介导的表面分类，结合这些信号，并证明所提出的方法是强大的可变扫描时间参数。我们检查徒手记录的69个纹理表面记录由不同的用户，并提出了一个分类系统，使用感知相关的功能，如硬度，粗糙度和摩擦;选定的功能适应语音识别，如修改后的倒谱系数应用到我们的加速度信号;和表面纹理相关的图像特征。我们专注于减轻可变的接触力和勘探速度条件对这些功能的影响，作为一个强大的基于机器学习的方法进行表面分类的先决条件。该系统的工作原理没有明确的扫描力和速度测量。实验结果表明，我们所提出的方法允许成功分类的纹理表面在可变的徒手运动条件下，施加不同的人类操作员。从所描述的声音，图像，摩擦力和加速度特征中选择的六个特征的建议子集，在我们的实验中与朴素贝叶斯分类器相结合时，分类准确率为74%。
When a tool is tapped on or dragged over an object surface, vibrations are induced in the tool, which can be captured using acceleration sensors. The tool-surface interaction additionally creates audible sound waves, which can be recorded using microphones. Features extracted from camera images provide additional information about the surfaces. We present an approach for tool-mediated surface classification that combines these signals and demonstrate that the proposed method is robust against variable scan-time parameters. We examine freehand recordings of 69 textured surfaces recorded by different users and propose a classification system that uses perception-related features, such as hardness, roughness, and friction; selected features adapted from speech recognition, such as modified cepstral coefficients applied to our acceleration signals; and surface texture-related image features. We focus on mitigating the effect of variable contact force and exploration velocity conditions on these features as a prerequisite for a robust machine-learning-based approach for surface classification. The proposed system works without explicit scan force and velocity measurements. Experimental results show that our proposed approach allows for successful classification of textured surfaces under variable freehand movement conditions, exerted by different human operators. The proposed subset of six features, selected from the described sound, image, friction force, and acceleration features, leads to a classification accuracy of 74 percent in our experiments when combined with a Naive Bayes classifier.