Title :
FAIR: Towards A New Feature for Affinely-Invariant Recognition
Author :
R. Sara;M. Matousek
Author_Institution :
Czech Technical University
fDate :
6/28/1905 12:00:00 AM
Abstract :
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
Keywords :
"Testing","Image representation","Noise robustness","Image retrieval","Image recognition","Robot localization","Image registration","Shape","Art","Layout"
Conference_Titel :
Pattern Recognition, 2006. ICPR 2006. 18th International Conference on
Print_ISBN :
0-7695-2521-0
DOI :
10.1109/ICPR.2006.542
