The Rotated Speeded-Up Robust Features Algorithm (R-SURF)

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.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2014
Accession Number
ADA610752

Entities

People

  • Sean M. Jurgensen

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Autonomy
  • Ground and Sea Platforms
  • Space

DTIC Thesaurus Topics

  • Algorithms
  • Computer Vision
  • Computers
  • Detectors
  • Digital Images
  • Geometry
  • Identification
  • Image Processing
  • Image Recognition
  • Machine Learning
  • Neural Networks
  • Pattern Recognition
  • Recognition
  • Test Methods
  • Two Dimensional
  • Unmanned Aerial Vehicles
  • Unmanned Vehicles

Fields of Study

  • Computer science

Readers

  • Atmospheric Science / Meteorology, specifically Wind Wave Turbulence.
  • Image Processing and Computer Vision.
  • Marksmanship and Weaponry.