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FAIR: Towards A New Feature for Affinely-Invariant Recognition

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2 Author(s)
Sara, R. ; Center for Machine Perception, Czech Tech. Univ., Prague ; Matousek, M.

In this paper we propose the first version of FAIR, a low-dimensional image neighborhood descriptor that shows performance comparable to SIFT introduced by Lowe. The dimension of FAIR we tested is 30, compared to the dimension of 128 in SIFT. Sensitivity of the FAIR descriptor to skew, rotation, image blur and noise is similar to SIFT. FAIR shows better localization in scale-space than SIFT. Several extensions of FAIR that could improve its performance are discussed

Published in:

Pattern Recognition, 2006. ICPR 2006. 18th International Conference on  (Volume:2 )

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