Scope: This bibliography contains references of neural systems for invariant representation or recognition.

Support level: Medium - 2002. This bibliography is medium well supported till the year 2002, i.e. I have collected quite a few references but the collection is not complete.

Publication types: All. This bibliography may also contain non-journal references, which are shown in grey and are not included in the bibtex file. Red titles indicate survey articles.

Online papers: An 'A' or 'P' (or 'T' for text only) after a reference link indicates that it leads to an abstract or full paper, respectively. Upper case letters refer to online documents provided by a journal, which might be accessible only to subscribers (indicated by a '?'). Lower case letters refer to online documents provided by an author. Also the links at the end of the references are differentiated in this way by upper and lower case letters at the beginning of the words 'abstract.html' etc. and by a '?', if the corresponding document is only accessible to subscribers. The site location is indicated by a preceding .uk, .com, etc.

BibTeX: A bibtex file for the journal article references is also available (compressed and uncompressed).

Feedback: Please feel free to send me any kind of feedback about this bibliography.

Related Resources

• Bibliography Dynamic Link Matching (a subset of this bibliography)
• Bibliography Learning Invariances in Neural Systems (a subset of this bibliography)

References

1. AndeVEsse87.

Anderson, C.H. and Van Essen, D.C. (1987).
Shifter circuits: A computational strategy for dynamic aspects of visual processing
Proceedings of the National Academy of Sciences of the United States of America, 84:6297-6301.

2. AntoTiroYaru+94.

Antonucci, M., Tirozzi, B., Yarunin, N.D., and Dotsenko, V.S. (1994).
Numerical simulation of neural networks with translation and rotation invariant pattern recognition
International Journal of Modern Physics B, 8(11-12):1529-1541.

3. AoniKura98

Aonishi, T. and Kurata, K. (1. April 1998).
Deformation Theory of Dynamic Link Matching
Neural Computation, 10(3):651-669.
(.jp-paper.ps.gz)

4. AoniKuraMito98.

Aonishi, T., Kurata, K., and Mito, T. (1998).
A Phase Locking Theory for Matching Common Parts of Two Images by Dynamic Link Matching
Biological Cybernetics, 78(4):253-264.
(.com-Abstract, .com-Paper.pdf?, .jp-paper.ps.gz)

5. AuguWint97.

Augusteijn, M.F. and Winterbottom, M.C. (June 1997).
Invariant object recognition using higher-order neural networks, line-segment spectra and multi-resolution training
International Journal of Neural Systems, 8(3):251-262.

6. BanaDull97.

Banarse, D.S. and Duller, A.W.G. (1997).
Deformation invariant visual object recognition: experiments with a self-organising neural architecture
Neural Computing and Applications, 6(2):79-90.

7. BarnCasa90.

Barnard, E. and Casasent, D.P. (1990).
Shift Invariance and the Neocognitron
Neural Networks, 3(4):403-410.

8. BarnCasa91

Barnard, E. and Casasent, D.P. (September 1991).
Invariance and Neural Nets
IEEE Transactions on Neural Networks, 2(5):498-508.

9. BarrBray92

Barrow, H.G. and Bray, A.J. (1992).
A Model of Adaptive Development of Complex Cortical Cells
In Artificial Neural Networks II: Proceedings of the International Conference on Artificial Neural Networks by Aleksander, I and Taylor, J. (eds.), Elsevier, Amsterdam, pp. 881-884..

10. BartSejn96a

Bartlett, M.S. and Sejnowski, T.J. (1996 a).
Unsupervised learning of invariant representations of faces through temporal association
In Computational Neuroscience: International Review of Neurobiology Suppl. 1. J.M. Bower, ed. Academic Press, San Diego, CA., 1996. pp. 317-322.
(.edu-paper.ps.Z)

11. BartSejn96b

Bartlett, M.S. and Sejnowski, T.J. (1996 b).
Learning Viewpoint Invariant Representations of Faces in an Attractor Network
Poster presented at the 18th Cognitive Science Society Meeting, San Diego, CA, July 12-15.
(.edu-paper.ps.Z)

12. BartSejn97

Bartlett, M.S. and Sejnowski, T.J. (1997).
Viewpoint invariant face recognition using independent component analysis and attractor networks
In M. Mozer, M. Jordan, T. Petsche (Eds.), Advances in Neural Information Processing Systems 9, MIT Press, Cambridge, MA. pp. 817-823.
(.edu-paper.ps)

13. BartSejn98

Bartlett, M.S. and Sejnowski, T.J. (1998).
Learning Viewpoint Invariant Face Representations from Visual Experience by Temporal Association
In H. Wechsler, P.J. Phillips, V. Bruce, S. Fogelman-Soulie, T. Huang (Eds.), Face Recognition: From Theory to Applications, NATO ASI Series F. Springer-Verlag.
(.edu-paper.ps)

14. Beck93

Becker, S. (1993).
Learning to Categorize Objects Using Temporal Coherence
In Advances in Neural Information Processing Systems 5, by Hanson, S.J., Cowan, J.D., and Giles, C.L., (eds.), pp. 361-368, San Mateo, CA: Morgan Kaufmann Publishers.
(.ca-paper.ps.gz)

15. Beck96

Becker, S. (1996).
Mutual Information Maximization: Models of Cortical Self-Organization
Network: Computation in Neural Systems, 7(1):7-31.
(.ca-abstract.html, .ca-paper.ps.gz)

16. Beck97

Becker, S. (1997).
Learning temporally persistent hierarchical representations
In Advances in Neural Information Processing Systems 9, by Mozer, M., Jordan, M., and Petsche, T. (eds.), MIT Press, Cambridge, MA, pages 824-830.

17. Beck99

Becker, S. (15. February 1999).
Implicit Learning in 3D Object Recognition: The Importance of Temporal Context
Neural Computation, 11(2):347-374.
(.ca-abstract.html, .ca-paper.ps.gz)

18. BeckHint92a

Becker, S. and Hinton, G.E. (1992 a).
A Self-Organizing Neural Network that Discovers Surfaces in Random-Dot Stereograms
Nature, 355(6356):161-163.
(.ca-abstract.html)

19. BeckHint92b

Becker, S. and Hinton, G.E. (1992 b).
Learning to Make Coherent Predictions in Domains with Discontinuities
In Advances in Neural Information Processing Systems 4, Morgan Kaufmann Publishers, pp. 372-379.
(.ca-paper.ps.gz)

20. BeckHint93

Becker, S. and Hinton, G.E. (1993).
Learning Mixture Models of Spatial Coherence
Neural Computation, 5(2):267-277.
(.ca-abstract.html)

21. BeckHint95

Becker, S. and Hinton, G.E. (1995).
Spatial coherence as an internal teacher for a neural network
in Backpropagation: Theory, Architectures, and Applications, Y. Chauvin and D. Rumelhart (eds), part of the series Developments in Connectionist Theory, Hillsdale, NJ: Lawrence Erlbaum.
(.ca-abstract.html, .ca-paper.ps.gz)

22. BialZee87.

Bialek, W. and Zee, A. (1987).
Statistical Mechanics and Invariant Perception
Physical Review Letters, 58(7):741-744.

23. BienDour94.

Bienenstock, E.L. and Doursat, R. (1994).
A Shape-Recognition Model Using Dynamical Links
Network: Computation in Neural Systems, 5:241-258.
(.edu-abstract)

24. BienVDMals87

Bienenstock, E.L. and von der Malsburg, C. (1987).
A neural network for invariant pattern recognition
Europhysics Letters, 4(1):121-126.

25. BlocKnigRose62.

Block, H.D., Knight, B.W., and Rosenblatt, F. (1962).
Analysis of a Four-Layer Series-Coupled Perceptron II
Rev. Mod. Phys., 34:135-152.

26. BullGros91.

Bullock, D. and Grossberg, S. (1991).
Adaptive neural networks for control of movement trajectories invariant under speed and force rescaling
Human Movement Science, 10(1):3-53.

27. BuonMerz99

Buonomano, D.V. and Merzenich, M.M. (1. January 1999).
A Neural Network Model of Temporal Code Generation and Position-Invariant Pattern Recognition
Neural Computation, 11(1):103-116.
(.org-Abstract, .org-Paper?, .org-Paper?)

28. CasaNeib95.

Casasent, D.P. and Neiberg, L.M. (1995).
Classifier and shift-invariant automatic target recognition neural networks
Neural Networks, 8(7-8):1117-1129.

29. CasaSmok94.

Casasent, D.P. and Smokelin, J.S. (1994).
Neural net design of macro Gabor wavelet filters for distortion-invariant object detection in clutter
Optical Engineering, 33(7):2264-2271.

30. Cava78

Cavanagh, P. (1978).
Size and position invariance in the visual system
Perception, 7:167-177.

31. Chan92.

Chan, L.W.L. (1992).
Neural networks for collective translational invariant object recognition
International Journal of Pattern Recognition and Artificial Intelligence, 6(1):143-156.

32. ChenDesa98.

Cheng, H.D. and Desai, R. (1998).
Scene Classification by Fuzzy Local Moments
International Journal of Pattern Recognition and Artificial Intelligence, 12(7):921-938.

33. ChiuTsen97.

Chiu, H.P. and Tseng, D.C. (30. May 1997).
Invariant handwritten Chinese character recognition using fuzzy min-max neural networks
Pattern Recognition Letters, 18(5):481-491.

34. CoolKuij89.

Coolen, A.C.C. and Kuijk, F.W. (1989).
A Learning Mechanism for Invariant Pattern Recognition in Neural Networks
Neural Networks, 2:495-506.

35. DeloTiraKoll94.

Delopoulos, A., Tirakis, A., and Kollias, S.D. (1994).
Invariant image classification using triple-correlation-based neural networks
IEEE Transactions on Neural Networks, 5(3):392-408.

36. Deno91.

Deno, D.C. (1991).
Review of 'Adaptive neural networks for control of movement trajectories invariant under speed and force rescaling', by Bullock and Grossberg, 1991
Human Movement Science, 10(1):73-80.

37. Deut81.

Deutsch, S. (1981).
A simplified version of Kunihiko Fukushima's neocognitron
Biological Cybernetics, 42(1):17-21.

38. DobnFiczPodb+92.

Dobnikar, A., Ficzko, J., Podbregar, D., and Rezar, U. (1992).
Invariant pattern classification neural network versus FT approach
Microprocessing & Microprogramming, 33(3):161-168.

39. Dots88.

Dotsenko, V.S. (1988).
Neural Networks: Translation-, Rotation-, and Scale-Invariant Pattern Recognition
Journal of Physics A: Mathematical and General, 21(15):L783--L787.

40. EdelWein91.

Edelman, S. and Weinshall, D. (1991).
A self-organizing multiple-view representation of 3D objects
Biological Cybernetics, 64(3):209-219.

41. EgleBraySton97

Eglen, S., Bray, A.J., and Stone, J.V. (1997).
Unsupervised Discovery of Invariances
Network: Computation in Neural Systems, 8(4):441-452.
(.uk-paper.ps.gz, .uk-paper.ps.gz)

42. EgleStonBarr96.

Eglen, S., Stone, J.V., and Barrow, H.G. (1996).
Learning perceptual invariances: A spatial model
Technical Report CSRP 404, School of Cognitive and Computing Sciences, University of Sussex.

43. EinhKaysKöni+02

Einhäuser, W., Kayser, C., König, P., and Körding, K.P. (February 2002).
Learning the invariance properties of complex cells from natural stimuli
European Journal of Neuroscience, 15(3):475-486.
(.com-paper.pdf)

44. Eise97

Eisele, M. (1997).
Unsupervised Learning of Temporal Constancies by Pyramidal-Type Neurons
In Mathematics of Neural Networks, Ellacott, Stephen W., Mason, John C., Anderson, Iain J. eds., Kluwer Academic Publishers, pp. 171-175.

45. ElliBank90.

Elliman, D.G. and Banks, R.N. (1990).
Shift invariant neural net for machine vision
IEE Proceedings I (Communications, Speech and Vision), 137(3):183-187.

46. FangHaus89.

Fang, M. and Hausler, G. (1989).
A feedback network with useful invariant properties (neural nets)
Optik, 83(4):134-138.

47. FengTiro97a

Feng, J. and Tirozzi, B. (November 1997 a).
An Analysis on Neural Dynamics with Saturated Sigmoidal Functions
Computers and Mathematics with Applications, 34(10):71-99.

48. FengTiro97b.

Feng, J. and Tirozzi, B. (1997 b).
A discrete version of the dynamic link network
Neurocomputing, 15:91-106.

49. Föld91

Földiák, P. (1991).
Learning Invariance from Transformation Sequences
Neural Computation, 3(2):194-200.
(two layer architecture: 1. simple cell layer with straight lines moving across; 2. complex cell layer trained with competitive learning based on activity traces)

50. Föld98.

Földiák, P. (1998).
Learning constancies for object perception
In Visual Constancies: Why things look as they do., Walsh, V. and Kulikowski, J.J., eds., Cambridge Univ. Press, Cambridge, U.K., in press.

51. FuchHake88a

Fuchs, A. and Haken, H. (1988 a).
Pattern Recognition and Associative Memory as Dynamical Processes in a Synergetic System I
Biological Cybernetics, 60:17-22.

52. FuchHake88b

Fuchs, A. and Haken, H. (1988 b).
Pattern Recognition and Associative Memory as Dynamical Processes in a Synergetic System II
Biological Cybernetics, 60:107-109.

53. FukuOmatNish97.

Fukumi, M., Omatu, S., and Nishikawa, Y. (1997).
Rotation-invariant neural pattern recognition system estimating a rotation angle
IEEE Transactions on Neural Networks, 8(3):568-581.

54. FukuOmatTake+92.

Fukumi, M., Omatu, S., Takeda, F., and Kosaka, T. (1992).
Rotation-invariant neural pattern recognition system with application to coin recognition
IEEE Transactions on Neural Networks, 3(2):272-279.

55. Fuku75.

Fukushima, K. (1975).
Cognitron: A Self-organizing Multilayered Neural Network
Biological Cybernetics, 20:121-136.

56. Fuku79.

Fukushima, K. (1979).
Neural network model for a mechanism of pattern recognition unaffected by shift in position-neocognitron
Transactions of the Institute of Electronics and Communication Engineers of Japan, Section E, E62(10):675-676.

57. Fuku80.

Fukushima, K. (1980).
Neocognitron: A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position
Biological Cybernetics, 36(4):193-202.

58. Fuku84.

Fukushima, K. (1984).
A Hierarchical Neural Network Model for Associative Memory
Biological Cybernetics, 50:105-113.

59. Fuku86

Fukushima, K. (1986).
A Neural Network Model for Selective Attention in Visual Pattern Recognition
Biological Cybernetics, 55:5-15.

60. Fuku88

Fukushima, K. (1988).
Neocognitron: A Hierarchical Neural Network Capable of Visual Pattern Recognition
Neural Networks, 1(2):119-130.

61. Fuku89.

Fukushima, K. (1989).
Analysis of the Process of Visual Pattern Recognition by the Neocognitron
Neural Networks, 2(6):413-420.

62. Fuku99

Fukushima, K. (1999).
Self-organization of shift-invariant receptive fields
Neural Networks, 12(6):791-801.
(three layer architecture: 1. input layer with straight lines sweeping across; 2. simple cell layer with competitive learning; 3. complex cell layer with competitive learning based on output traces)

63. FukuMiya82.

Fukushima, K. and Miyake, S. (1982).
Neocognitron: a new algorithm for pattern recognition tolerant of deformations and shifts in position
Pattern Recognition, 15(6):455-469.

64. FukuMiyaIto83

Fukushima, K., Miyake, S., and Ito, T. (1983).
Neocognitron: A Neural Network Model for a Mechanism of Visual Pattern Recognition
IEEE Transactions on Systems, Man and Cybernetics, 13(5):826-834.

65. FukuOkadHiro94

Fukushima, K., Okada, M., and Hiroshige, K. (1994).
Neocognitron with dual C-cell layers
Neural Networks, 7(1):41-47.

66. FukuWake91.

Fukushima, K. and Wake, N. (1991).
Handwritten alphanumeric character recognition by the neocognitron
IEEE Transactions on Neural Networks, 2(3):355-365.

67. GaneVenk96.

Ganesh Murthy, C.N.S. and Venkatesh, Y.V. (1996).
Modified neocognitron for improved 2-D pattern recognition
IEE Proceedings on Vision, Image and Signal Processing, 143(1):31-40.

68. GhahPatt93.

Ghahramani, E. and Patterson, L.R.B. (1993).
Scale, translation, and rotation invariant orthonormalized optical/optoelectronic neural networks
Applied Optics, 32(35):7225-7232.

69. GileMaxw87

Giles, C.L. and Maxwell, T. (1. December 1987).
Learning, invariance, and generalization in high-order neural networks
Applied Optics, 26(23):4972-4978.

70. Goch94

Gochin, P.M. (September 1994).
Properties of simulated neurons from a model of primate inferior temporal cortex
Cerebral Cortex, 5:532-543.

71. GracSpan91

Grace, A.E. and Spann, M. (1991).
A comparison between Fourier-Mellin descriptors and moment based features for invariant object recognition using neural networks
Pattern Recognition Letters, 12(10):635-643.

72. GrinTsodAmit93.

Griniasty, M., Tsodyks, M.V., and Amit, D.J. (1993).
Conversion of Temporal Correlations Between Stimuli to Spatial Correlations Between Attractors
Neural Computation, 5(1):1-17.

73. HamCho94.

Ham, F.M. and Cho, B. (1994).
Rotation, translation, and scale-invariant 2-D object recognition using a hybrid neural network
Journal of Artificial Neural Networks, 1(4):521-540.

74. HaseItohIchi96.

Hasegawa, A., Itoh, K., and Ichioka, Y. (March 1996).
Generalization of shift invariant neural networks: image processing of corneal endothelium
Neural Networks, 9(2):345-356.

75. HataKaka95.

Hatakeyama, Y. and Kakazu, Y. (1995).
Detecting a target object using an expanded neocognitron
Mathematical and Computer Modelling, 21(1-2):173-183.

76. HimeInig92.

Himes, G.S. and Inigo, R.M. (1992).
Automatic target recognition using a neocognitron
IEEE Transactions on Information Theory, 4(2):167-172.

77. Hint87

Hinton, G.E. (1987).
Learning Translation Invariant Recognition in a Massively Parallel Network
In PARLE: Parallel Architectures and Languages Europe by Goos, G. and Hartmanis, J. (eds.), Lecture Notes in Computer Science, Springer-Verlag, Berlin, pp. 1-13,.
(four layer backpropagation network learning translation invariant recognition of small binary patterns, generalization of training patterns to new positions)

78. Hint89

Hinton, G.E. (1. September 1989).
Connectionist Learning Procedures
Artificial Intelligence, 40(1-3):185-234.
(On page 208 this paper includes one paragraph on temporal smoothness as a possible objective for learning.)

79. HintBeck90.

Hinton, G.E. and Becker, S. (1990).
An Unsupervised Learning Procedure that Discovers Surfaces in Random-Dot Stereograms
Proceedings of the International Joint Conference on Neural Networks, Washington, DC, January, Vol. 1, pp. 218-222, Lawrence Erlbaum Associates, Hillsdale, NJ.

80. HummBied92

Hummel, J.E. and Biederman, I. (1992).
Dynamic Binding in a Neural Network for Shape Recognition
Psychological Review, 99(3):480-517.

81. InigXuArru+92.

Inigo, R.M., Xu, C.Q., Arrue, B.C., and McVey, E.S. (1992).
Hardware-implementable neural network for rotation-scaling invariant pattern classification
Journal of Electronic Imaging, 1(3):293-312.

82. ItoHamaKamr+93.

Ito, K., Hamamoto, M., Kamruzzaman, J., and Kumagai, Y. (1993).
Invariant object recognition by artificial neural network using Fahlman and Lebiere's learning algorithm
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences (A), E76-A(7):1267-1272.

83. ItoFukuMiya90.

Ito, T., Fukushima, K., and Miyake, S. (1990).
Realization of a neural network model Neocognitron on a hypercube parallel computer
International Journal of High Speed Computing, 2(1):1-16.

84. ItoFukuMiya91.

Ito, T., Fukushima, K., and Miyake, S. (1991).
Examination of implementing a neural network on a parallel computer-Neocognitron on NCUBE
Systems and Computers in Japan, 22(6):1-9.

85. JacoJordBart91

Jacobs, R.A., Jordan, M.I., and Barto, A.G. (1991).
Task decomposition through competition in a modular connectionist architecture: The what and where vision task
Cognitive Science, 15:219-250.

86. KakiHoraArim+94.

Kakizaki, S., Horan, P., Arimoto, A., Sako, H., and others (1994).
Optical implementation of a translation-invariant second-order neural network for multiple-pattern classification
Applied Optics, 33(35):8270-8280.

87. KanaChelYosh+92.

Kanaoka, T., Chellappa, R., Yoshitaka, M., and Tomita, S. (1992).
A higher-order neural network for distortion invariant pattern recognition
Pattern Recognition Letters, 13(12):837-841.

88. KaysEinhDümm+01

Kayser, C., Einhäuser, W., Dümmer, O., König, P., and Körding, K.P. (2001).
Extracting slow subspaces from natural videos leads to complex cells
Proc. International Conference on Artificial Neural Networks (ICANN).

89. KerlVall93.

Kerlirzin, P. and Vallet, F. (1993).
Robustness in Multilayer Perceptrons
Neural Computation, 5:473-482.

90. KhotLu90.

Khotanzad, A. and Lu, J.H. (1990).
Classification of Invariant Image Representations Using a Neural Network
IEEE Transactions on Acoust. Speech and Signal Process., 38(6):1028-1038.

91. KimLee91.

Kim, E.J. and Lee, Y. (1991).
Handwritten hangul recognition using a modified neocognitron
Neural Networks, 4(6):743-750.

92. KindBrau93.

Kinder, M. and Brauer, W. (1993).
Classification of Trajetories---Extracting Invariants with a Neural Network
Neural Networks, 6:1011-1017.

93. KobaKanaHama+94.

Kobara, K., Kanaoka, T., Hamamoto, Y., Tomita, S., and others (1994).
Use of gradated patterns in an associative neural memory for invariant pattern recognition
International Journal of Pattern Recognition and Artificial Intelligence, 8(2):595-607.

94. Koho96

Kohonen, T. (1996).
Emergence of Invariant-Feature Detectors in the Adaptive-Subspace SOM
Biological Cybernetics, 75(4):281-291.
(.com-Abstract, .com-Paper.pdf?)

95. KohoKaskLapp97

Kohonen, T., Kaski, S., and Lappalainen, H. (1997).
Self-Organized Formation of Various Invariant-Feature Filters in the Adaptive-Subspace SOM
Neural Computation, 9(6):1321-1344.
(.fi-abstract)

96. Koll96.

Kollias, S.D. (4. June 1996).
A multiresolution neural network approach to invariant image recognition
Neurocomputing, 12(1):35-57.

97. KoneMaurVDMals94

Konen, W., Maurer, T., and von der Malsburg, C. (1994).
A fast dynamic link matching algorithm for invariant pattern recognition
Neural Networks, 7(6/7):1019-1030.
(.de-paper.ps.gz)

98. KoneVDMals93

Konen, W. and von der Malsburg, C. (1993).
Learning to generalize from single examples in the dynamic link architecture
Neural Computation, 5:719-735.

99. KördKöni01

Körding, K.P. and König, P. (December 2001).
Neurons with two sites of synaptic integration learn invariant representations
Neural Computation, 13(12):2823-2849.
(.org-Abstract, .org-Paper?, .org-Paper?)

100. KreeZipp88.

Kree, R. and Zippelius, A. (1988).
Recognition of Topological Features of Graphs and Images in Neural Networks
Journal of Physics A: Mathematical and General, 21:L813-L818.

101. KropKremPono98.

Kropotov, Y.D., Kremen', I.Z., and Ponomarev, V.A. (1. September 1998).
A realistic neural network simulating a visual system and its usage in tasks of invariant image description
Journal of Optical Technology, 65(9):716.

102. LanzVituCatt+94.

Lanza, A., Vitulo, P., Cattaneo, C., Caresana, M., and others (1994).
Behaviour of feed-forward neural networks in invariant track finding
Computer Physics Communications, 79(3):364-372.

103. LCunBoseDenk+89

LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., and Jackel, L.D. (1989).
Backpropagation applied to handwritten zip code recognition
Neural Computation, 1(4):541-551.

104. LeeChoCho95.

Lee, B., Cho, Y., and Cho, S. (1995).
Translation, scale and rotation invariant pattern recognition using principal component analysis (PCA) and reduced second-order neural network
Neural, Parallel & Scientific Computations, 3(3):417-429.

105. LiWu93.

Li, C. and Wu, C.H. (1993).
Introducing rotation invariance into the neocognitron model for target recognition
Pattern Recognition Letters, 14(12):985-995.

106. LiNasr93.

Li, W. and Nasrabadi, N.M. (1993).
Invariant object recognition based on a neural network of cascaded RCE nets
International Journal of Pattern Recognition and Artificial Intelligence, 7(4):815-829.

107. LinWang96.

Lin, W.G. and Wang, S.S. (1996).
A new neural model for invariant pattern recognition
Neural Networks, 9(5):899-913.

108. LoveDownTsoi97.

Lovell, D.R., Downs, T., and Tsoi, A.C. (1997).
An evaluation of the neocognitron
IEEE Transactions on Neural Networks, 8(5):1090-1105.

109. MelRudeArch98

Mel, B.W., Ruderman, D.L., and Archie, K.A. (1998).
Translation-Invariant Orientation Tuning in Visual "Complex" Cells Could Derive from Intradendritic Computations
The Journal of Neuroscience, 18(11):4325-4334.
(.org-Abstract, .edu-abstract.html, .org-Paper?, .edu-paper.ps.gz)

110. MenoHein88.

Menon, M.M. and Heinemann, K.G. (1988).
Classification of Patterns Using a Self-Organizing Neural Network
Neural Networks, 1:201-215.

111. MinnMVeyInig92.

Minnix, J.I., McVey, E.S., and Inigo, R.M. (1992).
A Multilayered Self--Organizing Artificial Neural Network for Invariant Recognition
IEEE Transactions on Information Theory, 4(2):162-167.

112. Mitc91

Mitchison, G. (1991).
Removing Time Variation with the Anti-Hebbian Differential Synapse
Neural Computation, 3(3):312-320.
(includes a bias term to select among equivalent invariants)

113. MiyaFuku84.

Miyake, S. and Fukushima, K. (1984).
A Neural Network Model for the Mechanism of Feature-Extraction
Biological Cybernetics, 50:377-384.

114. OReilJohn93.

O'Reilly, R.C. and Johnson, M.H. (1993).
Object Recognition and Sensitive Periods: A Computational Analysis of Visual Imprinting
Department of Psychology, University of Colorado, Boulder, CO, Technical Report PDP.CNS.93.1.
(.edu-abstract, .edu-paper.ps.Z)

115. OReilJohn94

O'Reilly, R.C. and Johnson, M.H. (1994).
Object Recognition and Sensitive Periods: A Computation Analysis of Visual Imprinting
Neural Computation, 6(3):357-389.

116. OReilMClel92.

O'Reilly, R.C. and McClelland, J.L. (1992).
The Self-Organization of Spatially Invariant Representations
Department of Psychology, University of Colorado, Boulder, CO, Technical Report PDP.CNS.92.5.
(.edu-abstract, .edu-paper.ps.Z)

117. OhnoOkadFuku95.

Ohno, M., Okada, M., and Fukushima, K. (1995).
Neocognitron learning by backpropagation
Systems and Computers in Japan, 26(5):19-28.

118. OlshAndeVEsse93

Olshausen, B.A., Anderson, C.H., and Van Essen, D.C. (1. November 1993).
A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information
The Journal of Neuroscience, 13(11):4700-4719.
(.org-Abstract)

119. OlshAndeVEsse95

Olshausen, B.A., Anderson, C.H., and Van Essen, D.C. (March 1995).
A multiscale routing circuit for forming size- and position-invariant object representations
The Journal of Computational Neuroscience, 2(1):45-62.

120. OparPlekSolo96.

Oparin, A.N., Plekhanova, I.V., and Solovév, N.G. (1996).
Image recognition by a neural network invariant to affine transformations
Journal of Optical Technology, 63(10):764-767.

121. OramFöld96.

Oram, M.W. and Földiák, P. (1996).
Learning generalization and localization: Competition for stimulus type and receptive field
Neurocomputing, 11(2-4):297-321.

122. PargRoll98

Parga, N. and Rolls, E.T. (1998).
Transform-Invariant Recognition by Association in a Recurrent Network
Neural Computation, 10(6):1507-1525.

123. Patr96.

Patra, P.K. (1996).
Neural network for invariant image classification
Journal of the Institution of Electronics and Telecommunication Engineers, 42(4-5):281-290.

124. PengShaGan+98a

Peng, H.C., Sha, L.F., Gan, Q., and Wei, Y. (1998 a).
Energy function for learning invariance in multilayer perceptron
Electronics Letters, 34(3):292-294.

125. PengShaGan+98b

Peng, H.C., Sha, L.F., Gan, Q., and Wei, Y. (1998 b).
Combining adaptive sigmoid packet and trace neural network for fast invariance-learning
Electronics Letters, 34(9):898-900.

126. PeraLisb92

Perantonis, S.J. and Lisboa, P.J.G. (March 1992).
Translation, Rotation, and Scale Invariant Pattern Recognition by Higher-Order Neural Networks and Moment Classifier
IEEE Transactions on Neural Networks, 3(2):241-251.

127. Perf93.

Perfetti, R. (1993).
Existence of binary invariant sets in feedback neural networks with application to synthesis
IEEE Transactions on Neural Networks, 4(1):153-156.

128. PhilKaySmyt95

Phillips, W.A., Kay, J., and Smyth, D. (1995).
The Discovery of Structure by Multistream Networks of Local Processors with Contextual Guidance
Network: Computation in Neural Systems, 6(2):225-246.

129. PölzWech90.

Pölzleitner, W. and Wechsler, H. (1990).
Selective and Focused Invariant Recognition Using Distributed Associative Memories (DAM)
IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(8):809-814.

130. RaoBall98

Rao, R.P.N. and Ballard, D.H. (1998).
Development of localized oriented receptive fields by learning a translation-invariant code for natural images
Network: Computation in Neural Systems, 9(2):219-234.
(.edu-paper.pdf.gz)

131. RaveOmatBaka94.

Raveendran, P., Omatu, S., and Bakar, W.A. (1994).
Moments generated by neural networks to recognize invariant image representations
Journal of Artificial Neural Networks, 1(4):541-553.

132. ReidSpirOcho89

Reid, M.B., Spirkovska, L., and Ochoa, E. (1989).
Simultaneous position, scale, and rotation invariant pattern classification using third-order neural networks
International Journal of Neural Networks - Research and Applications, 1(3):154-159.

133. ReitAltm84

Reitböck, H.J.P. and Altmann, J. (1984).
A Model for Size- and Rotation-Invariant Pattern Processing in the Visual System
Biological Cybernetics, 51:113-121.

134. RekeUshiRosk95.

Rekeczky, C., Ushida, A., and Roska, T. (1995).
Rotation invariant detection of moving and standing objects using analogic cellular neural network algorithms based on ring-codes
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences (A), E78-A(10):1316-1330.

135. RiesPogg99

Riesenhuber, M. and Poggio, T. (November 1999).
Hierarchical models of object recognition in cortex
Nature Neuroscience, 2(11):1019-1025.
(.com-Abstract.html, .com-Paper.html, .com-Paper)

136. RiesPogg00

Riesenhuber, M. and Poggio, T. (November 2000).
Models of object recognition
Nature Neuroscience, 3(11 suppl):1199-1204.
(.com-Abstract.html, .com-Paper.html, .com-Paper)

137. Roll94.

Rolls, E.T. (1994).
Brain mechanisms for invariant visual recognition and learning
Behavioural Processes, 3:1134-1138.

138. Roll95.

Rolls, E.T. (1995).
Learning mechanisms in the temporal lobe visual cortex
Behavioural Brain Research, 66:177-185.

139. RollTove94.

Rolls, E.T. and Tovée, M.J. (1994).
Processing speed in the cerebral cortex, and the neurophysiology of visual backward masking
Proceedings of the Royal Society of London, Series B, 257:9-15.

140. SaliAbbo97

Salinas, E. and Abbott, L.F. (6. June 1997).
Invariant visual responses from attentional gain fields
Journal of Neurophysiology, 77(6):3267-3272.
(.org-Abstract?, .org-Paper?)

141. SangZhan97.

Sang, N. and Zhang, T. (December 1997).
Rotation and scale change invariant point pattern relaxation matching by the Hopfield neural network
Optical Engineering, 36(12):3378-3385.

142. SchrSejn92

Schraudolph, N.N. and Sejnowski, T.J. (1992).
Competitive Anti-Hebbian Learning of Invariants
In Moody, J.E., Hanson, S.J., and Lippmann, R.P., editors, Advances in Neural Information Processing Systems 4, Morgan Kaufmann, San Mateo, pp. 1017-1024.
(.ch-paper.ps.gz)

143. Schw77.

Schwartz, E.L. (1977).
Spatial Mapping in the Primate Sensory Projection: Analytic Structure and Relevance to Perception
Biological Cybernetics, 25:181-194.

144. Schw81.

Schwartz, E.L. (1981).
Cortical Anatomy, size invariance and spatial frequency analysis
Perception, 10:455-468.

145. SeibWaxm89

Seibert, M. and Waxman, A.M. (1989).
Spreading Activation Layers, Visual Saccades, and Invariant Representations for Neural Pattern Recognition Systems
Neural Networks, 2(1):9-27.

146. ShenLeje91.

Sheng, Y. and Lejeune, C. (1991).
Invariant Pattern Recognition Using Fourier-Mellin Transforms and Neural Networks
Journal of Optics, 22(5):223-228.

147. SiebWaxm89.

Siebert, M. and Waxman, A.M. (1989).
Spreading Activation Layers, Visual Saccades, and Invariant Representations for Neural Pattern Recognition Systems
Neural Networks, 2(1):9-27.

148. SpirReid92.

Spirkovska, L. and Reid, M.B. (1992).
Robust position, scale, and rotation invariant object recognition using higher-order neural networks
Pattern Recognition, 25(9):975-985.

149. Spra05

Spratling, M.W. (2005).
Learning viewpoint invariant perceptual representations from cluttered images
IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(5):753-761.

150. SquiCael95.

Squire, D.M.G. and Caelli, T.M. (1995).
Shift, Rotation and Scale Invariant Signatures for Two-Dimensional Contours, in a Neural Network Architecture
In Stephen W. Ellacott, John C. Mason, and Iain J. Anderson, editors, Mathematics of Neural Networks: Models, Algorithms and Applications, Kluwer Academic Publishers, Boston, pp. 344-348..

151. SrinJoua92.

Srinivasa, N. and Jouaneh, M. (1992).
A Neural Network Model for Invariant Pattern Recognition
IEEE Transactions on Signal Processing, 40(6):1595-1599.

152. SrinJoua93.

Srinivasa, N. and Jouaneh, M. (1993).
An Invariant Pattern Recognition Machine Using a Modified ART Architecture
IEEE Transactions on Systems, Man and Cybernetics, 23(5):1432--1437.

153. StöcEckhReit97.

Stöcker, M., Eckhorn, R., and Reitböck, H.J.P. (15. October 1997).
Size and position invariant visual representation supports retinotopic maps via selective backward paths: a dynamic second order neural network model for a possible functional role of recurrent connections in..
Neurocomputing, 17(2):111-132.

154. StöcReit96.

Stöcker, M. and Reitböck, H.J.P. (December 1996).
A neural network for position invariant pattern recognition combining spiking neurons with the Fourier-transform
International Journal of Neural Systems, 7(6):727-733.

155. Ston94.

Stone, J.V. (1994).
Learning Spatio-temporal Invariances
Proceedings of British Machine Vision Conference, York, pp. 681-690.

156. Ston95a.

Stone, J.V. (1995 a).
Learning Spatio-temporal Invariances
Neural Computation and Psychology Proceedings of the 3rd Neural Computation and Psychology Workshop (NCPW3), Stirling, Scotland, 31 August-2 September 1994, editors L.S. Smith and P.J.B. Hancock. Springer Verlag: Workshops in Computing Series, April 1995.

157. Ston95b:

Stone, J.V. (1995 b).
Learning Spatio-Temporal Invariances
In Smith, L.S. and Hancock, P.J.B., eds., Neural Computation and Psychology (Workshop in Computing Series), pp. 75-85, Springer, Berlin.

158. Ston95c.

Stone, J.V. (1995 c).
Hierarchical Learning of Spatio-Temporal Invariances
International Conference on Artificial Neural Networks, June, Cambridge, pp 110-115.

159. Ston95d.

Stone, J.V. (1995 d).
Learning Visual Invariances Through Spatio-Temporal Constraints
Applied Vision Association Annual Meeting, "Invariance and Constancy in Vision" Workshop, Reading, April.

160. Ston96a

Stone, J.V. (1. October 1996 a).
Learning Perceptually Salient Visual Parameters Using Spatiotemporal Smoothness Constraints
Neural Computation, 8(7):1463-1492.
(.uk-paper.ps.gz)

161. Ston96b

Stone, J.V. (1996 b).
Learning Stereo Disparity Using Temporal Smoothness Constraints: A Computational Model
Spatial Vision, 10(1):15-29.
(.uk-paper.ps.gz)

162. Ston96c

Stone, J.V. (1996 c).
A Canonical Microfunction For Learning Perceptual Invariances
Perception, 25:207-220.
(.uk-paper.ps.gz)

163. StonBray95a

Stone, J.V. and Bray, A.J. (1995 a).
A Learning Rule for Extracting Spatio-Temporal Invariances
Network: Computation in Neural Systems, 6(3):429-436.
(.uk-paper.ps.gz)

164. StonBray95b.

Stone, J.V. and Bray, A.J. (1995 b).
Extracting Temporal Invariances
International Workshop on Artificial Neural Networks, Malaga, June.

165. StriRoll02

Stringer, S.M. and Rolls, E.T. (November 2002).
Invariant Object Recognition in the Visual System with Novel Views of 3D Objects
Neural Computation, 14(11):2585-2596.
(.org-Abstract, .org-Paper?, .org-Paper?)

166. SuárMore92.

Suárez Araujo, C.P. and Moreno-Díaz, R. (1992).
eural Structures to Compute Homothetic Invariances for Artificial Perception Systems
In Proceedings of Computer Aided Systems Theory (EUROCAST '91) / Brain Processes, Theories and Models, Pichler, F., Morreno-Díaz, R. (eds.), series Lecture Notes in Computer Science, volume 585, Springer-Verlag, Berlin, ISBN 3-540-55354-1, pp. 525-538..

167. SuárMore95.

Suárez Araujo, C.P. and Moreno-Díaz, R. (1995).
On the neural mechanism of auditory and visual invariance perception
In Brain Processes, Theories and Models, Moreno-Díaz, R., Mira-Mira, J., (eds.), MIT- Press, ISBN 0-262-63170-9, pp.468-477..

168. SzuYangTelf+93.

Szu, H.H., Yang, X.Y., Telfer, B.A., and Sheng, Y. (1993).
Neural network and wavelet transform for scale-invariant data classification
Physical Review E, 48(2):1497-1501.

169. TakaKanaSkrz+94.

Takano, M., Kanaoka, T., Skrzypek, J., and Tomita, S. (1. June 1994).
A note on a higher-order neural network for distortion invariant pattern recognition
Pattern Recognition Letters, 15(6):631-635.

170. TangSrinOng96.

Tang, H.W., Srinivasan, V., and Ong, S.H. (1. July 1996).
Invariant object recognition using a neural template classifier
Image and Vision Computing, 14(7):473-483.

171. TaniFuku94.

Tanigawa, M. and Fukushima, K. (1994).
How to adjust the thresholds of feature-extracting cells in the neocognitron
Systems and Computers in Japan, 25(10):98-105.

172. TarkKomaLewe91.

Tarkowski, W., Komarnicki, M., and Lewenstein, M. (1991).
Optimal storage of invariant sets of patterns in neural network memories
Journal of Physics A: Mathematical and General, 24(17):4197-4217.

173. TeoKhooSim97.

Teo, M.Y., Khoo, L.P., and Sim, S.K. (1997).
Application of genetic algorithms to optimise Neocognitron network parameters
Neural Network World, 7(3):293-304.

174. TeoSim95.

Teo, M.Y. and Sim, S.K. (1995).
Training the neocognitron network using design of experiments
Artificial Intelligence in Engineering, 9(2):85-94.

175. ThomWillGatw93.

Thomas, R.D., Williamson, A.G., and Gatward, R.A. (1993).
Summary of the interactive neocognitron: an environment for pattern classification performance enhancement
Journal of Microcomputer Applications, 16(2):211-218.

176. Ting93.

Ting, C.H.A. (1993).
Magnocellular pathway for rotation invariant neocognitron
International Journal of Neural Systems, 4(1):43-54.

177. TingChua93

Ting, C.H.A. and Chuang, K.C. (1993).
An adaptive algorithm for neocognitron to recognize analog images
Neural Networks, 6(2):285-299.

178. TroyKrukMill98.

Troyer, T.W., Krukowski, A.E., and Miller, K.D. (1. August 1998).
Contrast-Invariant Orientation Tuning in Cat Visual Cortex: Thalamocortical Input Tuning and Correlation-Based Intracortical Connectivity
The Journal of Neuroscience, 18(15):5908.

179. TsenHwanShee97.

Tseng, Y.H., Hwang, J.N., and Sheehan, F.H. (1997).
Three-dimensional object representation and invariant recognition using continuous distance transform neural networks
IEEE Transactions on Neural Networks, 8(1):141-147.

180. UangYinAndr+94.

Uang, C.M., Yin, S., Andres, P., Reeser, W., and others (1994).
Shift-invariant interpattern association neural network
Applied Optics, 33(11):2147-2151.

181. UllmSolo99

Ullman, S. and Soloviev, S. (October 1999).
Computation of pattern invariance in brain-like structures
Neural Networks, 12(7/8):1021-1036.

182. VOoyeNien93.

van Ooyen, A. and Nienhuis, B. (1993).
Pattern recognition in the neocognitron is improved by neuronal adaptation
Biological Cybernetics, 70(1):47-53.

183. VogtZipp92.

Vogt, H. and Zippelius, A. (1992).
Invariant Recognition in Potts Glass Neural Networks
Journal of Physics A: Mathematical and General, 25(8):2209--2226.

184. VDMals81

von der Malsburg, C. (1981).
The correlation theory of brain function
Internal Report 81-2, MPI Biophysical Chemistry, Göttingen, Germany (Slightly revised third printing, December 1983). Reprinted in E. Domany, J.L. van Hemmen, and K. Schulten, editors, Models of Neural Networks II, chapter 2, pages 95-119. Springer Verlag, Berlin, (1994).
(.de-paper.ps.gz)

185. VDMals88

von der Malsburg, C. (1988).
Pattern recognition by labeled graph matching
Neural Networks, 1:141-148.

186. VDMalsBien87

von der Malsburg, C. and Bienenstock, E.L. (1987).
A neural network for the retrieval of superimposed connection patterns
Europhysics Letters, 3(11):1243-1249.

187. Wall96

Wallis, G. (1996).
Using spatio-temporal correlations to learn invariant object recognition
Neural Networks, 9(9):1513-1519.

188. WallBadd97

Wallis, G. and Baddeley, R. (1997).
Optimal, Unsupervised Learning in Invariant Object Recognition
Neural Computation, 9(4):883-894.
(.de-abstract.html)

189. WallRoll96.

Wallis, G. and Rolls, E.T. (1996).
A model of invariant object recognition in the visual system
Technical Report, Oxfort University, Department of Experimentatl Psychology.

190. WallRoll97

Wallis, G. and Rolls, E.T. (1. February 1997).
Invariant Face and Object Recognition in the Visual System
Progress in Neurobiology, 51(2):167-194.
(.au-abstract)

191. WallRollFöld93

Wallis, G., Rolls, E.T., and Földiák, P. (1993).
Learning Invariant Responses to the Natural Transformations of Objects
In Proceedings of 1993 International Joint Conference on Neural Networks, Nagoya, Japan, 25-29 Oct., pp. 1087-1090.

192. WangLin96.

Wang, S.S. and Lin, W.G. (1996).
A new self-organizing neural model for invariant pattern recognition
Pattern Recognition, 29(4):677-687.

193. Webb91

Webber, C.J.S.C. (1991).
Self-Organization of Position- and Deformation-Tolerant Neural Representations
Network: Computation in Neural Systems, 2(1):43-61.

194. Webb94

Webber, C.J.S.C. (1994).
Self-Organisation of Transformation-Invariant Detectors for Constituents of Perceptual Patterns
Network: Computation in Neural Systems, 5:471-496.

195. Webb00.

Webber, C.J.S.C. (2000).
Self-organisation of symmetry networks: Transformation invariance from the spontaneous symmetry-breaking mechanism
Neural Computation, 12(3):565-596.

196. WechZimm88.

Wechsler, H. and Zimmerman, G.L. (1988).
2-D Invariant Object Recognition Using Distributed Associative Memory
IEEE Transactions on Pattern Analysis and Machine Intelligence, 10(6):811-821.

197. WenYehYang96.

Wen, Z., Yeh, P., and Yang, X.Y. (1996).
Fuzzy neural network for invariant optical pattern recognition
Optical Engineering, 35(8):2188-2195.

198. WhitElma92.

White, B.A. and Elmasry, M.I. (1992).
The digi-neocognitron: a digital neocognitron neural network model for VLSI
IEEE Transactions on Neural Networks, 3(1):73-85.

199. WidrWintBaxt88.

Widrow, B., Winter, R.G., and Baxter, R.A. (1988).
Layered Neural Nets for Pattern Recognition
IEEE Transactions on Acoust. Speech and Signal Process., 36(7):1109-1118.

200. WiskSejn98

Wiskott, L. and Sejnowski, T.J. (1998).
Constrained Optimization for Neural Map Formation: A Unifying Framework for Weight Growth and Normalization
Neural Computation, 10(3):671-716.
(.org-Abstract, .de-abstract.html, .org-Paper?, .de-paper.ps.gz)

201. WiskSejn02

Wiskott, L. and Sejnowski, T.J. (2002).
Slow Feature Analysis: Unsupervised Learning of Invariances
Neural Computation, 14(4):715-770.
(.org-Abstract, .de-abstract.html, .org-Paper?, .de-paper.ps.gz)

202. WiskVDMals96

Wiskott, L. and von der Malsburg, C. (1996).
Face Recognition by Dynamic Link Matching
In Lateral Interactions in the Cortex: Structure and Function, Sirosh, J., Miikkulainen, R., and Choe., Y., Edt., Electronic book, ISBN 0-9647060-0-8, http://nn.cs.utexas.edu/web-pubs/htmlbook96/, chapter 11..
(.de-abstract.html, .edu-paper, .de-paper.ps.gz)

203. Wood96

Wood, J. (1996).
Invariant pattern recognition: a review
Pattern Recognition, 29(1):1-17.

204. WoodShaw96a.

Wood, J. and Shawe-Taylor, J. (30. December 1996 a).
A unifying framework for invariant pattern recognition
Pattern Recognition Letters, 17(14):1415-1422.

205. WoodShaw96b.

Wood, J. and Shawe-Taylor, J. (1. August 1996 b).
Representation theory and invariant neural networks
Discrete Applied Mathematics, 69(1-2):33-60.

206. WürtKoneBehr99.

Würtz, R.P., Konen, W., and Behrmann, K.O. (1. January 1999).
On the Performance of Neuronal Matching Algorithms
Neural Networks, 12(1):127-134.

207. YadiGernDvir+96.

Yadid-Pecht, O., Gerner, M., Dvir, L., Brutman, E., and others (1996).
Recognition of handwritten musical notes by a modified Neocognitron
Machine Vision and Applications, 9(2):65-72.
(.com-Abstract)

208. Yang91.

Yang, G.G. (1. April 1991).
Performance analysis of optical associative memory with translation invariance
Optik, 87(2):63.

209. YangHeal92.

Yang, G.G. and Healey, P. (1. May 1992).
Optical quadratic associative memory with translation and scale invariance
Optics Communications, 89(2-4):183.

210. YeunChanCheu94.

Yeung, D.S., Chan, H.Y., and Cheung, K.F. (1994).
Incorporating production rules with spatial information onto a neocognitron neural network
International Journal of Neural Systems, 5(2):131-142.

211. YuYangLu91.

Yu, F.T.S., Yang, X.Y., Lu, T., and others (1991).
Application of Moment Invariant Pattern Recognition to Optical Neural Net
Optik, 89(2):55-58.

212. ZemeHint91a.

Zemel, R.S. and Hinton, G.E. (1991 a).
Discovering Viewpoint Invariant Relationships that Characterize Objects
Tech. Rep., Department of Computer Science, University of Toronto, Toronto, Canada.

213. ZemeHint91b

Zemel, R.S. and Hinton, G.E. (1991 b).
Discovering Viewpoint-Invariant Relationships that Characterize Objects
In Lippmann, R.P., Moody, J.E., and Touretzky, D.S., eds., Advances in Neural Information Processing Systems 3, pp. 299-305, Denver, Morgan Kaufmann, Mateo.

214. ZhanDoiGige+96.

Zhang, W., Doi, K., Giger, M.L., Nishikawa, R.M., and others (1996).
An improved shift-invariant artificial neural network for computerized detection of clustered microcalcifications in digital mammograms
Medical Physics, 23(4):595-601.

215. ZhanDoiGige+94.

Zhang, W., Doi, K., Giger, M.L., Wu, Y., and others (1994).
Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network
Medical Physics, 21(4):517-524.

Author Index

301 authors, 41 (13%) with homepage. First author references are printed boldface.

Year Index

Journal Index

The numbers in the columns `R', `A', and `P' indicate the year from which on you can expect to find online references, abstracts, and papers, respectively. A '?' indicates documents, that may be accessible only to subscribers.

69 journals, 41 (59%) with homepage.