SpecTrans: Versatile Material Classification for Interaction with Textureless, Specular and Transparent Surfaces
Venue
SIGCHI Conference on Human Factors in Computing Systems, ACM (2015), pp. 2191-2200
Publication Year
2015
Authors
Munehiko Sato, Shigeo Yoshida, Alex Olwal, Boxin Shi, Atsushi Hiyama, Tomohiro Tanikawa, Michitaka Hirose, Ramesh Raskar
BibTeX
Abstract
Surface and object recognition is of significant importance in ubiquitous and
wearable computing. While various techniques exist to infer context from material
properties and appearance, they are typically neither designed for real-time
applications nor for optically complex surfaces that may be specular, textureless,
and even transparent. These materials are, however, becoming increasingly relevant
in HCI for transparent displays, interactive surfaces, and ubiquitous computing. We
present SpecTrans, a new sensing technology for surface classification of exotic
materials, such as glass, transparent plastic, and metal. The proposed technique
extracts optical features by employing laser and multi-directional, multispectral
LED illumination that leverages the material’s optical properties. The sensor
hardware is small in size, and the proposed classification method requires
significantly lower computational cost than conventional image-based methods, which
use texture features or reflectance analysis, thereby providing real-time
performance for ubiquitous computing. Our evaluation of the sensing technique for
nine different transparent materials, including air, shows a promising recognition
rate of 99.0%. We demonstrate a variety of possible applications using SpecTrans’
capabilities.
