Abstract
This paper presents a method to determine efficient scan-patterns 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 R 2=0.80. The correlation coefficients are then used in an exemplary cost-benefit function of an exemplary application of the proposed method.
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Matzka, S., Petillot, Y.R., Wallace, A.M. (2007). Determining Efficient Scan-Patterns for 3-D Object Recognition Using Spin Images. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2007. Lecture Notes in Computer Science, vol 4842. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76856-2_55
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DOI: https://doi.org/10.1007/978-3-540-76856-2_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76855-5
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