MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY and CENTER FOR BIOLOGICAL AND COMPUTATIONAL LEARNING DEPARTMENT OF BRAIN AND COGNITIVE SCIENCES

[1]  Tomaso A. Poggio,et al.  Example-Based Learning for View-Based Human Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Marianthi Markatou,et al.  Robust Inference : The Approach Based on Influence Functions , 1996 .

[3]  Roberto Brunelli,et al.  Robust estimation of correlation with applications to computer vision , 1995, Pattern Recognit..

[4]  Alex Pentland,et al.  View-based and modular eigenspaces for face recognition , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[5]  K. Raghunath Rao,et al.  On the recognition of occluded shapes and generic faces using multiple-template expansion matching , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[6]  K. Raghunath Rao,et al.  A novel approach for template matching by nonorthogonal image expansion , 1993, IEEE Trans. Circuits Syst. Video Technol..

[7]  Yann LeCun,et al.  Efficient Pattern Recognition Using a New Transformation Distance , 1992, NIPS.

[8]  J. D. Brasher,et al.  Relationship between maximizing the signal-to-noise ratio and minimizing the classification error probability for correlation filters. , 1992, Optics letters.

[9]  B. V. Kumar,et al.  Minimum squared error synthetic discriminant functions , 1992 .

[10]  D. Casasent,et al.  Minimum noise and correlation energy optical correlation filter. , 1992, Applied optics.

[11]  D Casasent,et al.  Advanced distortion-invariant minimum average correlation energy (MACE) filters. , 1992, Applied optics.

[12]  D. Casasent,et al.  Gaussian-minimum average correlation energy filters. , 1991, Applied optics.

[13]  Ronen Basri,et al.  Recognition by Linear Combinations of Models , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Roberto Brunelli,et al.  HyperBF Networks for Real Object Recognition , 1991, IJCAI.

[15]  D. Casasent,et al.  Performance evaluation of minimum average correlation energy filters. , 1991, Applied optics.

[16]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[17]  Abhijit Mahalanobis,et al.  Unified framework for the synthesis of synthetic discriminant functions with reduced noise variance and sharp correlation structure , 1990 .

[18]  F. Girosi,et al.  Networks for approximation and learning , 1990, Proc. IEEE.

[19]  T. Poggio,et al.  A network that learns to recognize three-dimensional objects , 1990, Nature.

[20]  D. Casasent,et al.  Minimum average correlation energy filters. , 1987, Applied optics.

[21]  C. Jennison,et al.  Robust Statistics: The Approach Based on Influence Functions , 1987 .

[22]  B. V. Vijaya Kumar,et al.  Minimum-variance synthetic discriminant functions , 1986 .

[23]  D. Casasent,et al.  Correlation synthetic discriminant functions. , 1986, Applied optics.

[24]  R. Kallman.  Construction of low noise optical correlation filters. , 1986, Applied optics.

[25]  Werner A. Stahel,et al.  Robust Statistics: The Approach Based on Influence Functions , 1987 .

[26]  John G. Proakis,et al.  Probability, random variables and stochastic processes , 1985, IEEE Trans. Acoust. Speech Signal Process..

[27]  D Casasent,et al.  Unified synthetic discriminant function computational formulation. , 1984, Applied optics.

[28]  H. Caulfield,et al.  Computer recognition of 2-D patterns using generalized matched filters. , 1982, Applied optics.

[29]  David Casasent,et al.  Principal-Component Imagery For Statistical Pattern Recognition Correlators , 1982 .

[30]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[31]  H J Caulfield,et al.  Improved discrimination in optical character recognition. , 1969, Applied optics.

[32]  I. Miller.  Probability, Random Variables, and Stochastic Processes , 1966 .

[33]  S. Ullman,et al.  Recognition by Linear Combinations of Models , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[34]  A. Hall.  Applied Optics. , 2022, Science.