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

Stephan Matzka, Yvan R. Petillot, Andrew M. Wallace

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

Fingerprint Dive into the research topics of 'Determining efficient scan-patterns for 3-D object recognition using spin images'. Together they form a unique fingerprint.

Cite this