Image Representations for Visual Learning

Computer vision researchers are developing new approaches to object recognition and detection that are based almost directly on images and avoid the use of intermediate three-dimensional models. Many of these techniques depend on a representation of images that induces a linear vector space structure and in principle requires dense feature correspondence. This image representation allows the use of learning techniques for the analysis of images (for computer vision) as well as for the synthesis of images (for computer graphics).

[1]  Samuel B. Williams,et al.  Association for Computing Machinery , 2009 .

[2]  Martin A. Fischler,et al.  The Representation and Matching of Pictorial Structures , 1973, IEEE Transactions on Computers.

[3]  David J. Burr,et al.  Elastic Matching of Line Drawings , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Tomaso Poggio,et al.  Computational vision and regularization theory , 1985, Nature.

[5]  Tomaso Poggio,et al.  Visual information: Do computers need attention? , 1986, Nature.

[6]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[7]  William Grimson,et al.  Object recognition by computer - the role of geometric constraints , 1991 .

[8]  T Poggio,et al.  Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks , 1990, Science.

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

[10]  Alan L. Yuille,et al.  Generalized Deformable Models, Statistical Physics, and Matching Problems , 1990, Neural Computation.

[11]  Lawrence Sirovich,et al.  Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Dean Pomerleau,et al.  Efficient Training of Artificial Neural Networks for Autonomous Navigation , 1991, Neural Computation.

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

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

[15]  H H Bülthoff,et al.  Psychophysical support for a two-dimensional view interpolation theory of object recognition. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[16]  Joachim M. Buhmann,et al.  Distortion Invariant Object Recognition in the Dynamic Link Architecture , 1993, IEEE Trans. Computers.

[17]  Edward H. Adelson,et al.  Representing moving images with layers , 1994, IEEE Trans. Image Process..

[18]  Amnon Shashua,et al.  Algebraic Functions For Recognition , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  N. Logothetis,et al.  Shape representation in the inferior temporal cortex of monkeys , 1995, Current Biology.

[20]  Tomaso A. Poggio,et al.  Regularization Theory and Neural Networks Architectures , 1995, Neural Computation.

[21]  Joseph J Atick,et al.  The vocabulary of shape: principal shapes for probing perception and neural response. , 1996, Network.