Multimodal Feature-Based Surface Material Classification.

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.