Hassan Waseem, Joolee Joolekha Bibi, Jeon Seokhee
Department of Computer Science and Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do, South Korea.
Sci Rep. 2023 Jul 19;13(1):11684. doi: 10.1038/s41598-023-38929-6.
The current study strives to provide a haptic attribute space where texture surfaces are located based on their haptic attributes. The main aim of the haptic attribute space is to come up with a standardized model for representing and identifying haptic textures analogous to the RGB model for colors. To this end, a four dimensional haptic attribute space is established by conducting a psychophysical experiment where human participants rate 100 real-life texture surfaces according to their haptic attributes. The four dimensions of the haptic attribute space are rough-smooth, flat-bumpy, sticky-slippery, and hard-soft. The generalization and scalability of the haptic attribute space is achieved by training a 1D-CNN model for predicting attributes of haptic textures. The 1D-CNN is trained using the attribute data from psychophysical experiments and image features collected from the images of real textures. The prediction power granted by the 1D-CNN renders scalability to the haptic attribute space. The prediction accuracy of the proposed 1D-CNN model is compared against other machine learning and deep learning algorithms. The results show that the proposed method outperforms the other models on MAE and RMSE metrics.
当前的研究致力于提供一个触觉属性空间，纹理表面可基于其触觉属性定位于此空间中。触觉属性空间的主要目标是提出一个标准化模型，用于表示和识别触觉纹理，类似于用于颜色的RGB模型。为此，通过进行一项心理物理学实验建立了一个四维触觉属性空间，在该实验中，人类参与者根据100个真实生活中的纹理表面的触觉属性对其进行评分。触觉属性空间的四个维度是粗糙-光滑、平坦-凹凸不平、粘性-滑溜和硬-软。通过训练一个1D-CNN模型来预测触觉纹理的属性，实现了触觉属性空间的泛化和可扩展性。使用来自心理物理学实验的属性数据和从真实纹理图像中收集的图像特征对1D-CNN进行训练。1D-CNN赋予的预测能力为触觉属性空间带来了可扩展性。将所提出的1D-CNN模型的预测准确率与其他机器学习和深度学习算法进行比较。结果表明，所提出的方法在MAE和RMSE指标上优于其他模型。
