Name
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-76856-2_55 | ISVC |
Keywords | Field | DocType |
efficient scanpatterns,squares regression algorithm,adjusted coefficient,exemplary application,3-d object recognition,exemplary cost-benefit function,spin image,robust multivariate regression,efficient scan-patterns,large dataset,corresponding classification performance,correlation coefficient,object recognition,coefficient of determination,multivariate regression,least trimmed squares | Spin-½,Pattern recognition,Regression,Least trimmed squares,Multivariate statistics,Computer science,Correlation,Simple linear regression,Artificial intelligence,Coefficient of determination,Cognitive neuroscience of visual object recognition | Conference |
Volume | ISSN | ISBN |
4842 | 0302-9743 | 3-540-76855-6 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stephan Matzka | 1 | 28 | 4.43 |
| Yvan R. Petillot | 2 | 112 | 10.72 |
| Andrew M. Wallace | 3 | 175 | 31.01 |