The rotated speeded-up robust features algorithm (R-SURF)
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Authors
Jurgensen, Sean M.
Subjects
SURF
speeded-up robust features
feature detector
feature detection
boxfiltering
box filters
Fast Hessian 15.NUMBER OFPAGES 135
speeded-up robust features
feature detector
feature detection
boxfiltering
box filters
Fast Hessian 15.NUMBER OFPAGES 135
Advisors
Fargues, Monique P.
Cristi, Roberto
Date of Issue
2014-06
Date
Jun-14
Publisher
Monterey, California: Naval Postgraduate School
Language
Abstract
Weaknesses in the Fast Hessian detector utilized by the speeded-up robust features (SURF) algorithm are examined in this research. We evaluate the SURF algorithm to identify possible areas for improvement in the performance. A proposed alternative to the SURF detector is proposed called rotated SURF (R-SURF). This method utilizes filters that are rotated 45 degrees counter-clockwise, and this modification is tested with standard detector testing methods against the regular SURF detector. Performance testing shows that the R-SURF outperforms the regular SURF detector when subject to image blurring, illumination changes and compression. Based on the testing results, the R-SURF detector outperforms regular SURF slightly when subjected to affine (viewpoint) changes. For image scale and rotation transformations, R-SURF outperforms for very small transformation values, but the regular SURF algorithm performs better for larger variations. The application of this research in the larger recognition process is also discussed.
Type
Thesis
Description
Includes supplementary material
Series/Report No
Department
Electrical and Computer Engineering
Organization
Identifiers
NPS Report Number
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Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.