Bell AJ, Sejnowski TJ. (1995). An information-maximization approach to blind separation and blind deconvolution. Neural computation. 7 [PubMed]
Black MJ, Jepson A. (1996). EigenTracking: Robust matching and tracking of articulated objects using a view-based representation Proceedings of the Fourth European Conference on Computer Vision, ECCV96.
Blake A, Isard M. (1996). Contour tracking by stochastic propogation of conditional density Proc 4th European Conf Computer Vision.
Dempster AP, Laird NM, Rubin DB. (1977). Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc B. 39
Dodwell PC. (1983). The Lie transformation group model of visual perception. Perception & psychophysics. 34 [PubMed]
Eves H. (1980). Elementary matrix theory.
Foldiak P. (1991). Learning invariance from transformation sequences Neural Comput. 3
Frey BJ, Jojic N. (1999). Estimating mixture models of images and inferring spatial transformations using the em algorithm IEEE Computer Vision and Pattern Recognition.
Fukushima K. (1980). Neocognitron: a self organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological cybernetics. 36 [PubMed]
Gibson J. (1966). The senses considered as perceptual systems.
Grimes DB, Rao RP. (2005). Bilinear sparse coding for invariant vision. Neural computation. 17 [PubMed]
Helgason S. (2001). Differential geometry, Lie groups, and symmetric spaces.
Hinton GE. (1987). Learning translation invariant recognition in a massively parallel network PARLE: Parallel architectures and languages Europe .
Lecun Y et al. (1989). Backpropagation applied to handwritten zip code recognition Neural Comput. 1
Lecun Y, Simard P, Denker J. (1993). Efficient pattern recognition using a new transformation distance Advances in neural information processing systems. 5
Marks RJ. (1991). Introduction to Shannon sampling and interpolation theory.
Nordberg K. (1994). Signal representation and processing using operator groups Tech Rep Linkoping Studies in Science and Technology, Dissertations No. 366.
Olshausen BA, Anderson CH, Van Essen DC. (1995). A multiscale dynamic routing circuit for forming size- and position-invariant object representations. Journal of computational neuroscience. 2 [PubMed]
Olshausen BA, Field DJ. (1996). Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature. 381 [PubMed]
Pitts W, Mcculloch WS. (1947). How we know universals. Bull Math Biophys. 9
Rao RP, Ballard DH. (1998). Development of localized oriented receptive fields by learning a translation-invariant code for natural images. Network (Bristol, England). 9 [PubMed]
Rao RP, Ballard DH. (1999). Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nature neuroscience. 2 [PubMed]
Ruderman DL, Rao RPN. (1999). Learning Lie groups for invariant visual perception Advances in neural information processing systems. 11
Shi J, Tomasi C. (1994). Good features to track Proc IEEE CVPR. 94
Tenenbaum JB, Freeman WT. (2000). Separating style and content with bilinear models. Neural computation. 12 [PubMed]
Vasconcelos N, Lippman A. (2005). A multiresolution manifold distance for invariant image similarity IEEE Trans Multimedia. 7
Vasilescu MAO, Terzopoulos D. (2002). Multilinear Analysis of Image Ensembles: Tensor Faces Proc European Conf Comput Vis.
Wang JA, Adelson EH. (1994). Representing moving images with layers. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 3 [PubMed]
Wiskott L, Sejnowski TJ. (2002). Slow feature analysis: unsupervised learning of invariances. Neural computation. 14 [PubMed]
van_Gool L, Moons T, Pauwels E, Oosterlinck A. (1995). Vision and Lies approach to invariance Image Vis Comput. 13