The rotated speeded-up robust features algorithm (R-SURF)

• Main Author: Jurgensen, Sean M.
• Other Authors: Fargues, Monique P.
• Published: Monterey, California: Naval Postgraduate School 2014
• Online Access: http://hdl.handle.net/10945/42653

Approved for public release; distribution is unlimited === Includes supplemental materials === 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.