A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
A fast fixed-point algorithm for independent component analysis
Neural Computation
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Matching Images Features in a Wide Base Line with ICA Descriptors
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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Motivated by the problems of vision-based mobile robot map building and localization, in this work, we show that using statistical learning methods the performance of the standard descriptor based methodology for matching image features in a wide base line can be improved. First, we propose two kinds of descriptors for image features and two statistical learning methods. Later, we present a study of the performance of descriptors with and without the statistical learning methods. This work does not pretend to present an exhaustive description of the mentioned methods but to give a good idea the effectiveness of using statistical learning methods together with descriptors for matching image features in a wide base line.