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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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. © Springer-Verlag Berlin Heidelberg 2007.

Original languageEnglish
Title of host publicationAdvances in Visual Computing - Third International Symposium, ISVC 2007, Proceedings
Pages559-570
Number of pages12
Volume4842 LNCS
EditionPART 2
Publication statusPublished - 2007
Event3rd International Symposium on Visual Computing - Lake Tahoe, NV, United States
Duration: 26 Nov 200728 Nov 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume4842 LNCS
ISSN (Print)0302-9743

Conference

Conference3rd International Symposium on Visual Computing
Abbreviated titleISVC 2007
CountryUnited States
CityLake Tahoe, NV
Period26/11/0728/11/07

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  • Cite this

    Matzka, S., Petillot, Y. R., & Wallace, A. M. (2007). Determining efficient scan-patterns for 3-D object recognition using spin images. In Advances in Visual Computing - Third International Symposium, ISVC 2007, Proceedings (PART 2 ed., Vol. 4842 LNCS, pp. 559-570). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4842 LNCS, No. PART 2).