论文标题
通过提升技术增强表面材料的触觉区分性
Enhancing Haptic Distinguishability of Surface Materials with Boosting Technique
论文作者
论文摘要
判别特征对于多种学习应用至关重要，例如对象检测和分类。神经网络广泛用于提取图像和语音信号的区分特征。但是，触觉域中缺乏大数据集通常会限制此类技术的适用性。本文提出了一个一般框架，用于分析触觉信号的判别特性。我们证明了光谱特征的有效性和增强嵌入技术在增强触觉信号的区分性方面的有效性。实验表明我们的框架需要较少的培训数据，对不同的预测指标进行概述，并且表现优于相关的最先进。
Discriminative features are crucial for several learning applications, such as object detection and classification. Neural networks are extensively used for extracting discriminative features of images and speech signals. However, the lack of large datasets in the haptics domain often limits the applicability of such techniques. This paper presents a general framework for the analysis of the discriminative properties of haptic signals. We demonstrate the effectiveness of spectral features and a boosted embedding technique in enhancing the distinguishability of haptic signals. Experiments indicate our framework needs less training data, generalizes well for different predictors, and outperforms the related state-of-the-art.
