Determining efficient scan-patterns for 3-D object recognition using spin images

Authors:
Stephan Matzka;Yvan R. Petillot;Andrew M. Wallace
Affiliations:
School of Engineering & Physical Sciences, Heriot-Watt University, Edinburgh, UK and Institute for Applied Research, Ingolstadt University of Applied Sciences, Germany;School of Engineering & Physical Sciences, Heriot-Watt University, Edinburgh, UK;School of Engineering & Physical Sciences, Heriot-Watt University, Edinburgh, UK
Venue:
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Year:
2007

Citing 3
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Abstract

This paper presents a method to determine efficient scanpatterns for spin images using robust multivariate regression. A large dataset is generated using scan-patterns with random radial scanlines through an oriented point and determining the corresponding classification performance. Eight features are chosen, which are used as predictor variables for a multivariate least trimmed squares regression algorithm, achieving an adjusted coefficient of determination of R2=0.80. The correlation coefficients are then used in an exemplary cost-benefit function of an exemplary application of the proposed method.