Learning Lie groups for invariant visual perception
Proceedings of the 1998 conference on Advances in neural information processing systems II
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Statistics of shape via principal geodesic analysis on lie groups
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
International Journal of Computer Vision
Covariance tracking via geometric particle filtering
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
Minimal representations for uncertainty and estimation in projective spaces
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Efficient video mosaicking by multiple loop closing
PIA'11 Proceedings of the 2011 ISPRS conference on Photogrammetric image analysis
Automatic unconstrained online configuration of a master-slave camera system
ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems
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We present a family of "normal驴 distributions over a matrix group together with a simple method for estimating its parameters. In particular, the mean of a set of elements can be calculated. The approach is applied to planar projective homographies, showing that using priors defined in this way improves object recognition.