Automatic prediction of interest point stability
| dc.contributor.author | Comer, Thomson H., author | |
| dc.contributor.author | Draper, Bruce A. (Bruce Austin), 1962-, advisor | |
| dc.contributor.author | Monnier, Patrick, committee member | |
| dc.contributor.author | Beveridge, Ross, committee member | |
| dc.date.accessioned | 2007-01-03T05:43:57Z | |
| dc.date.available | 2007-01-03T05:43:57Z | |
| dc.date.issued | 2009 | |
| dc.description | Department Head: L. Darrell Whitley. | |
| dc.description.abstract | Many computer vision applications depend on interest point detectors as a primary means of dimensionality reduction. While many experiments have been done measuring the repeatability of selective attention algorithms [MTS+05, BL02, CJ02, MP07, SMBI98], we are not aware of any method for predicting the repeatability of an individual interest point at runtime. In this work, we attempt to predict the individual repeatability of a set of 106 interest points produced by Lowe's SIFT algorithm [Low03], Mikolajczyk's Harris-Affine [Mik02], and Mikolajczyk and Schmid's Hessian-Affine [MS04]. These algorithms were chosen because of their performance and popularity. 17 relevant attributes are recorded at each interest point, including eigenvalues of the second moment matrix, Hessian matrix, and Laplacian-of-Gaussian score. A generalized linear model is used to predict the repeatability of interest points from their attributes. The relationship between interest point attributes proves to be weak, however the repeatability of an individual interest point can to some extent be influenced by attributes. A 4% improvement ofmean interest point repeatability is acquired through two related methods: the addition of five new thresholding decisions and through selecting the N best interest points as predicted by a GLM of the logarithm of all 17 interest points. A similar GLM with a smaller set of author-selected attributes has comparable performance. This research finds that improving interest point repeatability remains a hard problem, with an improvement of over 4% unlikely using the current methods for interest point detection. The lack of clear relationships between interest point attributes and repeatability indicates that there is a hole in selective attention research that may be attributable to scale space implementation. | |
| dc.format.medium | masters theses | |
| dc.identifier | 2009_Spring_Comer_Thomson.pdf | |
| dc.identifier | ETDF2009100003COMS | |
| dc.identifier.uri | http://hdl.handle.net/10217/41108 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation | Catalog record number (MMS ID): 991011903339703361 | |
| dc.relation | TA1650.C65 2009 | |
| dc.relation.ispartof | 2000-2019 | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.title | Automatic prediction of interest point stability | |
| dc.type | Text | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science (M.S.) |
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