IEEE Transactions on Haptics

Expand your horizons with Colloquium, a monthly survey of abstracts from all CS transactions! Replaces OnlinePlus in January 2017.


From the April-June 2017 issue

Multimodal Feature-Based Surface Material Classification

By Matti Strese, Clemens Schuwerk, Albert Iepure, and Eckehard Steinbach

Free Featured ArticleWhen 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.

download PDF View the PDF of this article      csdl View this issue in the digital library


Editorials and Announcements

Announcements


Editorials


Guest Editorials


News


Reviewers List


Annual Index


Access recently published ToH articles

RSS Subscribe to the RSS feed of latest ToH content added to the digital library.


Mail Sign up for the Transactions Connection newsletter.


ToH is a joint publication of the IEEE Computer Society, IEEE Robotics and Automation Society and the IEEE Consumer Electronics Society.

Indexed in MEDLINE®/PubMed® & ISI

Computing Now