Perceptual Surface Roughness Classification of 3D Textures Using Support Vector Machines

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.

[1]  J. Besag.  On the Statistical Analysis of Dirty Pictures , 1986 .

[2]  Andrew Zisserman,et al.  A Statistical Approach to Texture Classification from Single Images , 2004, International Journal of Computer Vision.

[3]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[4]  Sethuraman Panchanathan,et al.  Distal object perception through haptic user interfaces , 2006 .

[5]  Shree K. Nayar,et al.  Correlation model for 3D texture , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[6]  Mario Fritz,et al.  On the Significance of Real-World Conditions for Material Classification , 2004, ECCV.

[7]  Andrew Zisserman,et al.  A Statistical Approach to Texture Classification from Single Images , 2005, International Journal of Computer Vision.

[8]  Kristin J. Dana,et al.  Recognition methods for 3D textured surfaces , 2001, IS&T/SPIE Electronic Imaging.

[9]  Glenn Healey,et al.  The Analysis and Recognition of Real-World Textures in Three Dimensions , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  T. McDaniel,et al.  Methodology for Efficient Perception in Exclusively Haptic Environments , 2006, 2006 IEEE International Workshop on Haptic Audio Visual Environments and their Applications (HAVE 2006).

[11]  Shree K. Nayar,et al.  Histogram model for 3D textures , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[12]  A. Streri,et al.  Touching for knowing : cognitive psychology of haptic manual perception , 2003 .

[13]  Maria Petrou,et al.  Illuminant Rotation Invariant Classification of 3D Surface Textures using Lissajou's Ellepses , 2002 .

[14]  Jitendra Malik,et al.  Recognizing surfaces using three-dimensional textons , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[15]  K. Kahol,et al.  A methodology to establish ground truth for computer vision algorithms to estimate haptic features from visual images , 2005, IEEE International Workshop on Haptic Audio Visual Environments and their Applications.

[16]  Narendra Ahuja,et al.  3D Texture Classification Using the Belief Net of a Segmentation Tree , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[17]  Aram Kawewong,et al.  3D texture classification by using pre-testing stage and reliability table , 2005, IEEE International Conference on Image Processing 2005.

[18]  A. Farag,et al.  Color segmentation using an Eigen color representation , 2005, 2005 7th International Conference on Information Fusion.

[19]  Shree K. Nayar,et al.  Reflectance and texture of real-world surfaces , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.