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    Feature matching is an important step for many computer vision applications. This paper introduces the development of a new feature descriptor, called SYnthetic BAsis (SYBA), for feature point description and matching.  SYBA is built on the basis of the compressed sensing theory that uses synthetic basis functions to encode or reconstruct a signal. It is a compact and efficient binary descriptor that performs a number of similarity tests between a feature image region and a selected number of synthetic basis images and uses their similarity test results as the feature descriptors.  SYBA is compared with four well-known binary descriptors using three benchmarking datasets as well as a newly created dataset that was designed specifically for a more thorough statistical T-test. SYBA is less computationally complex and produces better feature matching results than other binary descriptors. It is hardware-friendly and suitable for embedded vision applications.

Graduate Students:

  Alok Desai

Publications:
  1. A. Desai, D.J. Lee, and D. Ventura, “An Efficient Feature Descriptor Based on Synthetic Basis Functions and Uniqueness Matching Strategy,” Computer Vision and Image Understanding, vol. 142, p. 37-49, January 2016.
  2. A. Desai, D.J. Lee, D. Ventura, “Matching Affine Features with SYBA Feature Descriptor,” Lecture Notes in Computer Science, International Symposium on Visual Computing, p. 488-457, Las Vegas, NV, USA, December 8-10 2014.
  3. A. Desai, D.J. Lee, C.N. Wilson, "Using Affine Features for an Efficient Binary Feature Descriptor", IEEE Southwest Symposium on Image Analysis and Interpretation, San Diego, CA, USA, April 4-8, 2014.
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