SYBA Feature Descriptor
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
- 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
- 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.
- 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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