A neural-network appearance-based 3-D object recognition using independent component analysis
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[1] David B. Cooper,et al. Describing Complicated Objects by Implicit Polynomials , 1994, IEEE Trans. Pattern Anal. Mach. Intell..
[2] Narendra Ahuja,et al. Learning recognition and segmentation of 3-D objects from 2-D images , 1993, 1993 (4th) International Conference on Computer Vision.
[3] Sameer A. Nene,et al. Columbia Object Image Library (COIL100) , 1996 .
[4] Ramesh C. Jain,et al. Three-dimensional object recognition , 1985, CSUR.
[5] Vishvjit S. Nalwa,et al. A guided tour of computer vision , 1993 .
[6] E. Oja,et al. Independent Component Analysis , 2013 .
[7] Terrence J. Sejnowski,et al. The “independent components” of natural scenes are edge filters , 1997, Vision Research.
[8] Erkki Oja,et al. Subspace methods of pattern recognition , 1983 .
[9] Charles R. Dyer,et al. Model-based recognition in robot vision , 1986, CSUR.
[10] Massimiliano Pontil,et al. Support Vector Machines for 3D Object Recognition , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[11] Hiroshi Murase,et al. Subspace methods for robot vision , 1996, IEEE Trans. Robotics Autom..
[12] Hiroshi Murase,et al. Visual learning and recognition of 3-d objects from appearance , 2005, International Journal of Computer Vision.
[13] Marian Stewart Bartlett,et al. Viewpoint Invariant Face Recognition using Independent Component Analysis and Attractor Networks , 1996, NIPS.
[14] Clark F. Olson,et al. View-Based Recognition Using an Eigenspace Approximation to the Hausdorff Measure , 1999, IEEE Trans. Pattern Anal. Mach. Intell..
[15] Christian Jutten,et al. Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture , 1991, Signal Process..
[16] Christopher J. C. Burges,et al. A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.
[17] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[18] Lee E. Weiss,et al. Dynamic sensor-based control of robots with visual feedback , 1987, IEEE Journal on Robotics and Automation.
[19] Hiroshi Murase,et al. Learning and recognition of 3D objects from appearance , 1993, [1993] Proceedings IEEE Workshop on Qualitative Vision.
[20] Andrzej Cichocki,et al. A New Learning Algorithm for Blind Signal Separation , 1995, NIPS.
[21] I Biederman,et al. Neurocomputational bases of object and face recognition. , 1997, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.
[22] Keinosuke Fukunaga,et al. Introduction to Statistical Pattern Recognition , 1972 .
[23] Herman Martins Gomes,et al. Appearance-based object recognition using support vector machines , 2001, Proceedings XIV Brazilian Symposium on Computer Graphics and Image Processing.
[24] Irving Biederman,et al. Differences of Face and Object Recognition in Utilizing Early Visual Information , 1998 .
[25] Terrence J. Sejnowski,et al. An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.
[26] S.C. Douglas,et al. Multichannel blind deconvolution and equalization using the natural gradient , 1997, First IEEE Signal Processing Workshop on Signal Processing Advances in Wireless Communications.
[27] Seungjin Choi,et al. Factorial Code Representation of Faces for Recognition , 2000, Biologically Motivated Computer Vision.
[28] Alex Pentland,et al. Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[29] David G. Lowe,et al. Learning object recognition models from images , 1993, 1993 (4th) International Conference on Computer Vision.
[30] Pierre Comon,et al. Independent component analysis, A new concept? , 1994, Signal Process..
[31] Marian Stewart Bartlett,et al. Independent component representations for face recognition , 1998, Electronic Imaging.
[32] Alex Pentland,et al. View-based and modular eigenspaces for face recognition , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.
[33] Barr,et al. Superquadrics and Angle-Preserving Transformations , 1981, IEEE Computer Graphics and Applications.
[34] Jenq-Neng Hwang,et al. Three-dimensional object representation and invariant recognition using continuous distance transform neural networks , 1997, IEEE Trans. Neural Networks.
[35] T. Poggio,et al. A network that learns to recognize three-dimensional objects , 1990, Nature.
[36] Hiroshi Murase,et al. Real-time 100 object recognition system , 1996, Proceedings of IEEE International Conference on Robotics and Automation.
[37] L Sirovich,et al. Low-dimensional procedure for the characterization of human faces. , 1987, Journal of the Optical Society of America. A, Optics and image science.
[38] M. Turk,et al. Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.
[39] C. S. George Lee,et al. Weighted selection of image features for resolved rate visual feedback control , 1991, IEEE Trans. Robotics Autom..
[40] Shun-ichi Amari,et al. Natural Gradient Works Efficiently in Learning , 1998, Neural Computation.
[41] Erkki Oja,et al. Simple Neuron Models for Independent Component Analysis , 1996, Int. J. Neural Syst..