Deep Learning for Surface Material Classification Using Haptic and Visual Information
Deep Learning for Surface Material Classification Using Haptic and Visual Information
复制标题
使用触觉和视觉信息进行表面材料分类的深度学习
DOI：
10.1109/tmm.2016.2598140
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发表时间：
2016
影响因子：
7.3
通讯作者：
Steinbach Eckehard
中科院分区：
文献类型：
--
作者：
Zheng Haitian;Fang Lu;Ji Mengqi;Strese Matti;Ozer Yigitcan;Steinbach Eckehard
当用户用手持刚性工具在物体表面划伤时，可以捕获一个加速度信号，该信号携带有关表面材料属性的相关信息。更重要的是，这种触觉加速信号可以与表面图像一起使用，共同识别表面材料。在本文中，我们提出了一种新的基于全卷积网络的深度学习方法来处理表面材料分类问题，该方法将上述加速信号和相应的表面纹理图像作为输入。与现有依赖于手工特征精心设计的表面材料分类解决方案相比，我们的方法利用先进的深度学习方法自动提取判别特征。在TUM表面材料数据库上进行的实验表明，该方法具有鲁棒性和有效性。
When a user scratches a hand-held rigid tool across an object surface, an acceleration signal can be captured, which carries relevant information about the surface material properties. More importantly, such haptic acceleration signals can be used together with surface images to jointly recognize the surface material. In this paper, we present a novel deep learning method dealing with the surface material classification problem based on a fully convolutional network, which takes the aforementioned acceleration signal and a corresponding image of the surface texture as inputs. Compared to the existing surface material classification solutions which rely on a careful design of hand-crafted features, our method automatically extracts discriminative features utilizing advanced deep learning methodologies. Experiments performed on the TUM surface material database demonstrate that our method achieves state-of-the-art classification accuracy robustly and efficiently.