Liu et al., Texture Classification by Audio-Tactile Congruence, IEEE Haptics Symposium 2022
Channel: IEEE Haptics Symposium
Date: 2022-03-15

Y. Liu, S. Lu, H. Culbertson, Texture Classification by Audio-Tactile Crossmodal Congruence, IEEE Haptics Symposium 2022
Abstract: This work presents a large-scale texture classification method using a novel texture feature Projected Spectral Mapping (PSM) based on audio-tactile crossmodal congruence in unconstrained tool-surface interactions. We describe a quick-computable extraction process for PSM from the proposed crossmodal inter-band spectral mapping (IBSM) that relates the frequency components in different bands between the modalities. We conducted a texture classification on the LMT Haptic Texture Dataset with 69 textures in 9 categories to evaluate the PSM feature by both random sampling train-test split and participant-specific cross-validation. Compared to a variety of texture features from previous work, the results showed that our PSM feature reached greater than 74% classification accuracy by 3 out of 4 classifiers and outperformed all other features with significant improvement.
#haptics #haptics2022 #texture #classification #crossmodal #congruence 
https://www.hapticssymposium.org/