Non-rigid structure from motion using ranklet-based tracking and non-linear optimization
Image and Vision Computing
Texture classification using invariant ranklet features
Pattern Recognition Letters
Weighted Dissociated Dipoles for Evolutive Learning
Proceedings of the 2007 conference on Artificial Intelligence Research and Development
A sparsity-enforcing method for learning face features
IEEE Transactions on Image Processing
A nonparametric approach to face detection using ranklets
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Weighted dissociated dipoles: an extended visual feature set
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Fast algorithms for the computation of Ranklets
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Local rank transform: Properties and applications
Pattern Recognition Letters
A ranklet-based CAD for digital mammography
IWDM'06 Proceedings of the 8th international conference on Digital Mammography
Statistical learning approaches with application to face detection
ASB'03 Proceedings of the 1st international conference on Advanced Studies in Biometrics
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We introduce a family of multiscale, orientation-selective, non-parametric features ("ranklets") Modelled on Haar wavelets. We clarify their relation to the Wilcoxon rank-sum test and the rank transfor and provide an efficient scheme for computation based on the Mann-Whitney statistics. Finally, we show that ranklets outperform other rank features, Haar wavelets, SNoW and linear SVMs (based on independently published results) in face detection experiments over the 24'045 test images in the MIT-CBCL database.