Abstract

Perceptual surface roughness classification describes how a surface's texture feels haptically in terms of perceptual categories such as smooth, rough, bumpy, etc. Computer vision and pattern recognition algorithms which estimate a surface's perceptual roughness have a wide range of application areas including robotics, assistive devices, telesurgery and teleperception. In this paper, we propose a novel approach to perceptual surface roughness classification that, unlike previous approaches, is designed to handle multiple roughness categories within the same image. The steps of our approach include (1) texton extraction and classification using a multi-class, non-linear Support Vector Machine; (2) segmentation using the Iterated Conditional Modes algorithm; and (3) overall perceptual roughness classification using a Nearest Neighbor classifier. The proposed approach is evaluated using visio-haptic subjective measures of roughness on images of the 3D texture of real world objects.

Original languageEnglish (US)
Title of host publicationHAVE 2007 - The 6th IEEE International Workshop on Haptic, Audio and Visual Environments and Games, Proceedings
Pages154-159
Number of pages6
DOIs
StatePublished - Dec 1 2007
Event6th IEEE International Workshop on Haptic, Audio and Visual Environments and Games, HAVE 2007 - Ottawa, ON, Canada
Duration: Oct 12 2007Oct 14 2007

Publication series

NameHAVE 2007 - The 6th IEEE International Workshop on Haptic, Audio and Visual Environments and Games, Proceedings

Other

Other6th IEEE International Workshop on Haptic, Audio and Visual Environments and Games, HAVE 2007
CountryCanada
CityOttawa, ON
Period10/12/0710/14/07

Keywords

  • Haptic user interfaces
  • Image texture analysis
  • Visiohaptics

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction

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