General–Vision BIBTEX File for Neural Networks

\begin{matrix}c \\ ◯ \end{matrix}Roland Schwaiger March 11, 1994

1 Vision with Neural Networks

1.1 Neural Networks

[Arb87] [BC91] [Glü91]

1.1.1 Distributed Associative Memories [PW90]

1.1.2 Multi Layer Neural Networks (MLNN)

[Aus] [Aus89] [CK89] [GGM88] [KL90] [KB93] [Koi91] [MMI91a] [MMI91b] [MMI92]

1.1.3 Higher Order Neural Networks

[DS91] [PL92]

1.1.4 Hopfield Networks

[BvdM87] [Dot88] [FH88a] [FH88b] [GAP92] [KZ88] [vdMB87] [VZ92]

1.1.5 Adaptive Resonance Theory (ART)

[SJ92] [SJ93] [WWB88] 1.1.6 (Neo)cognitron

[JDB88] [BC90] [Fuk75] [Fuk80] [FMI83] [MF84] [Fuk86] [Fuk89] [MH88] [Tin89]

1.2 Moment Invariants

[CLL91] [Gos85] [FS93] [Hu62] [LB93] [LLD89] [VC93] [YYL91]

1.3 Transformations

1.3.1 Fourier-, Fourier-Mellin-Transformation

[AR83] [AR84] [Alt87] [RA84] [ASBH90] [CP76] [CM87] [LB93] [SL91] [TG89]

[Tiw89] [WZ88]

1.3.2 Wigner Transformation [JW84]

1.3.3 Radon Transformation [Pin89]

1.4 Related Papers

1.4.1 Kolmogorov’s Theorem

[CG92] [K˙92]

1.4.2 Hough Transformation [PYIK89]

1.4.3 Edge Detection

[CM85] [Der90] [LWM93] [MC93] 1.4.4 Mathematical Background

[Pie90] [Zer34]

1.4.5 Biological Background

[BGST82] [CCNT89] [Fis73] [JL91] [Sch77] [Zee88]

1.4.6 Philosophical Background [Bar80]

1.4.7 Robot Vision

[BCK92] [CD86]

2 Other Papers

[BZ87] [FWL91] [Pfa93] [CFP91]

2.1 Neural Networks

[BR92] [ERY91] [KN92] [KV93] [SN93]

References

[Alt87] Jürgen Altmann. On the digital implementation of the rotationinvariant fourier-mellin transform. Journal of Information Processing and Cybernetics, EIK–23(1):13–36, 1987.

[AR83] Jürgen Altmann and Herbert J. P. Reitböck. Größen-, rotationsund translationsinvariante mustererkennung durch eine schnelle korrelationsmethode. VDE-Fachberichte, 35:278–283, 1983. Fourier Transformation, Fourier–Mellin Transformation.

[AR84] Jürgen Altmann and Herbert J. P. Reitböck. A fast correlation method for scale- and translation-invariant pattern recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-6(1):46–57, January 1984. Fourier-Mellin Transform.

[Arb87] Michael A. Arbib. Brains, Machines, and Mathematics, chapter 4, pages 48–90. Springer Verlag, 1987. Perceptron, Visual Memory, Minsky-Papert Theory, Winograd-Spira Theory.

[ASBH90] Klaus Arbter, Wesley E. Snyder, Hans Burkhardt, and Gerd Hirzinger. Application of affine-invariant fourier descriptors to recognition of 3-d objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(7):640–647, July 1990.

[Aus] James Austin. Adam: An associative neural architecture for invariant pattern classification. In IEEE First International Conference on Artificial Neural Networks, pages 196–200.

[Aus89] J. Austin. High speed invariant recognition using adaptive neural networks. In IEEE 3rd International Conference on Image Processing and its Applications (conf. publ. no. 307), pages 28–32, 1989.

[Bar80] H. B. Barlow. The absolute efficiency of perceptual decisions. Philosophical Transactions of the Royal Society of London B, 290:71– 82, 1980.

[BC90] Etienne Barnard and David Casasent. Shift invariance and the neocognitron. Neural Networks, 3:403–410, 1990.

[BC91] Etienne Barnard and David Casasent. Invariance and neural nets. IEEE Transactions on Neural Networks, 2(5):498–508, September 1991.

[BCK92] Ruud M. Bolle, Andrea Califano, and Rick Kjeldsen. A complete and extendable approach to visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(5):534– 548, May 1992. Hough Transform.

[BGST82] C. Braccini, G. Gambardella, G. Sandini, and V. Tagliasco. A model of the early stages of the human visual system: Functional and topological transformations performed in the peripheral visual field. Biological Cybernetics, 44:47–58, 1982. DOG.

[BR92] Avrim L. Blum and Ronald L. Rivest. Training a 3-node neural network is np-complete. Neural Networks, 5:117–127, 1992.

[BvdM87] E. Bienenstock and C. von der Malsburg. A neural network for invariant pattern recognition. Europhysics Letters, 4(1):121–126, July 1987. Graph Matching, Hopfield Networks.

[BZ87] William Bialek and A. Zee. Statistical mechanics and invariant perception. Physical Review Letters, 58(7):741–744, February 1987.

[CCNT89] Steen S. Christensen, Rodney M. J. Cotterill, Claus Nielsen, and Henrik Thovtrup. The primary visual system: An attempt to understand the function of its structure. In Models of Brain Function, chapter 11, pages 191–210. Cambridge University Press, 1989.

[CD86] R. T. Chin and C. R. Dyer. Model-based recognition in robot vision. Computing Surveys, 18(1):67–108, March 1986.

[CFP91] Fernand S. Cohen, Zhigang Fan, and Maqbool A. Patel. Classification of rotated and scaled textured images using gaussian markov random field models (gmrf). IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(2):192–202, February 1991. GMRF.

[CG92] Neil E. Cotter and Thierry J. Guillerm. The cmac and a theorem of komogorov. Neural Networks, 5:221–228, 1992.

[CK89] A. C. C. Coolen and F. W. Kuijk. A learning mechanism for invariant pattern recognition in neural networks. Neural Networks, 2:495–506, 1989.

[CLL91] R. Creutzburg, V. G. Labunets, and E. V. Labunets. Fast spectral algorithms for invariant pattern recognition and image matching.

Computer Analysis of Images and Patterns (Dresden 1991) Res. Inform., 5:85–95, 1991. Affine IV Moments.

[CM85] E. Charniak and D. McDermot. Introduction to Artificial Intelligence, chapter 3.4.2, pages 101–111. Addison Wesley, 1985. Convolution with Gaussians.

[CM87] E. De Castro and C. Morandi. Registration of translated and rotated images using finite fourier transforms. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-9(5):700– 703, September 1987. Fourier Transforms.

[CP76] David Casasent and Demetri Psaltis. Position-, rotation-, and scale-invariant optical correlation. Applied Optics, 15(7):1795– 1799, July 1976. Mellin Transform, Fourier Transform, Fourier-Mellin Transform.

[Der90] Rachid Deriche. Fast algorithms for low-level vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(1):78– 87, January 1990.

[Dot88] V. S. Dotsenko. Neural networks: Translation-, roltation-, and scale-invariant pattern recognition. Journal of Physics A: Math. Gen., 21:L783–L787, 1988. Hopfield Network.

[DS91] Jon P. Davis and William A. Schmidt. Invariant pattern recognition via higher-order preprocessing and backprop. SPIE Applications of Artificial Neural Networks II, 1469:804–811, 1991. HONN.

[ERY91] Shimon Edelman, Daniel Reisfeld, and Yechezkel Yeshurun. Learning to recognize faces from examples. Gaussian Receptive Fields, RBF, October 1991.

[FH88a] A. Fuchs and H. Haken. Pattern recognition and associative memory as dynamical processes in a synergetic system i. Biological Cybernetics, 60:17–22, 1988. Hopfield Network.

[FH88b] A. Fuchs and H. Haken. Pattern recognition and associative memory as dynamical processes in a synergetic system ii. Biological Cybernetics, 60:107–109, 1988. Hopfield Network.

[Fis73] Burkhart Fischer. Overlap of receptive field centers and representation of the visual field in the cat’s optic tract. Vision Res., 13:2113–2120, 1973.

[FMI83] Kunihiko Fukushima, Sei Miyake, and Takayuki Ito. Neocognitron: A neural network model for a mechanism of visual pattern recognition. IEEE Transactions on Systems, Man, and Cybernetics, SMC–13(5):826–834, September/October 1983.

[FS93] Jan Flusser and Tomàs̆ Suk. Pattern recognition by affine moment invariants. Pattern Recognition, 26(1):167–174, 1993. Affine Transform, Algebraic IV, Moment IV.

[Fuk75] Kunihiko Fukushima. Cognitron: A self-organizing multilayered neural network. Biological Cybernetics, 20:121–136, 1975.

[Fuk80] Kunihiko Fukushima. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, 36:193–202, 1980.

[Fuk86] Kunihiko Fukushima. A neural network model for selective attention in visual pattern recognition. Biological Cybernetics, 55:5–15, 1986.

[Fuk89] Kunihiko Fukushima. Analysis of the process of visual pattern recognition by the neocognitron. Neural Networks, 2:413–420, 1989.

[FWL91] Michael Franzini, Alex Waibel, and Kai-Fu Lee. Continous speech recognition with the connectionist viterbi training procedure: A summary of recent work. In IEEE International Joint Conference on Neural Networks 1991 Singapore. IEEE, 1991.

[GAP92] A. H. Gee, V. B. Aiyer, and R. W. Prager. A subspace approach to invariant pattern recognition using hopfield networks. Technical report, Cambridge University Engineering Department, May 1992.

[GGM88] C. L. Giles, R. D. Griffin, and T. Maxwell. Encoding geometric invariances in higher-order neural networks. In Neural Information Processing Systems, pages 301–309. American Institute of Physics, d. z. anderson edition, 1988.

[Glü91] Helmut Glünder. Pattern properties, invariances, and classification. In J. W. Roger, editor, Vision and Visual Dysfunction (14) Pattern Recognition by Man and Machine, chapter 6, pages 69– 82. MacMillan Press, 1991. Template Matching, Statistical PR, Geometrically Invariant PR.

[Gos85] Ardeshir Goshtasby. Template matching in rotated images. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-7(3):338–344, May 1985. Normalized Invariant Moments.

[Hu62] Ming-Kuei Hu. Visual pattern recognition by moment invariants. IRE Transactions on Information Theory, IT-8:179–187, 1962.

[JDB88] Ken Johnson, Cindy Daniell, and Jerry Burman. Feature extraction in the neocognitron. In IEEE International Conference on Neural Nets, pages II–117–II–126. IEEE, 1988.

[JL91] Anupam Joshi and Chia-Hoang Lee. On modelling the retina using neural networks. In IEEE International Joint Conference on Neural Networks 1991 Singapore. IEEE, 1991.

[JW84] Lowell Jacobson and Harry Wechsler. A theory for invariant object recognition in the frontoparallel plane. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-6(3):325–331, May 1984. Wigner Distribution, DAM.

[K˙92] Věra Ku̇rková. Kolmogorov’s theorem and multilayer neural networks. Neural Networks, 5:501–506, 1992.

[KB93] Margit Kinder and Wilfried Brauer. Classification of trajetories— extracting invariants with a neural network. Neural Networks, 6:1011–1017, 1993. Wickelfeatures, Tuple Coding.

[KL90] Alireza Khotanzad and Jiin-Her Lu. Classification of invariant image representations using a neural network. IEEE Transactions on Acoustics, Speech, and Signal Processing, 38(6):1028– 1038, June 1990. Moment Invariants, Zernike Moment based Features.

[KN92] Visakan Kadirkamanathan and Mahesan Niranjan. Application of an architecturally dynamic network for speech pattern classification. Proceedings of the Institute of Acoustics, 14(6):343–350, 1992. Gaussian Radial Basis Functions, Kalman Filter.

[Koi91] Pascal Koiran. Approximations of mappings and application to translational invariant networks. In IEEE International Joint Conference on Neural Networks 1991 Singapore. IEEE, 1991.

[KV93] P. Kerlirzin and F. Vallet. Robustness in multilayer perceptrons. Neural Computation, 5:473–482, 1993.

[KZ88] R. Kree and A. Zippelius. Recognition of topological features of graphs and images in neural networks. Journal of Physics A: Math. Gen., 21:L813–L818, 1988. Hopfield Network.

[LB93] Feng Lin and Robert D. Brandt. Towards absolute invariants of images under translation, rotationm and dilation. Pattern Recognition Letters, 14:369–379, May 1993. Fourier Transform, Fourier-Mellin Transform, Phase Taylor IV.

[LLD89] Ming-Chin Lu, Chong-Huah Lo, and Hon-Son Don. A neural network approach to 3-d object identification and pose estimation. In IEEE 3rd International Conference on Image Processing and its Applications (conf. publ. nr. 307), pages 2600–2605. IEEE, 1989. 3D Moment Invariants, 2 1/2D Images.

[LWM93] David Lee, G. W. Wasilkowski, and Rajiv Mehrotra. A new zero-crossing-based discontinuity detector. IEEE Transactions on Image Processing, 2(2):265–268, April 1993. Marr-Hildreth, residual, DOG.

[MC93] B. S. Manjunath and Rama Chellappa. A unified approach to boundary perception: Edges, textures, and illusory contours.

IEEE Transactions on Neural Networks, 4(1):96–107, January 1993. Gabor wavelet decomposition.

[MF84] Sei Miyake and Kunihiko Fukushima. A neural network model for the mechanism of feature-extraction. Biological Cybernetics, 50:377–384, 1984.

[MH88] Murali M. Menon and Karl G. Heinemann. Classification of patterns using a self-organizing neural network. Neural Networks, 1:201–215, 1988. Neocognitron.

[MMI91a] Jay I. Minnix, Eugene S. McVey, and Rafael M. Iñigo. Multistage self-organizing neural network with biologically inspired preprocessing features for rotation and scale invariant pattern recognitio n. In IEEE, pages 1605–1610, 1991. Walsh–Hadamard Transform, Neocognitron.

[MMI91b] Jay I. Minnix, Eugene S. McVey, and Rafael M. Iñigo. Rotation and scale invariant pattern recognition using a multistaged neural network. In SPIE Vol.1606, Visual Communications and Image Processing ’91: Image Processing, pages 241–251, 1991. Log-Polar Transform, Walsh Hadamard Transform, Neocognitron.

[MMI92] Jay I. Minnix, Eugene S. McVey, and Rafael M. Iñigo. A multilayered self–organizing artificial neural network for invariant recognition. IEEE Transactions on Knowledge and Data Engineering, 4(2):162–167, April 1992. Walsh–Hadamard Transform.

[Pfa93] Jochen Pfalzgraf. On a general notion of a hull. Technical report, Research Institute for Symbolic Computation, 1993. Category Theory.

[Pie90] M. Ann Piech. Decomposing the laplacian. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(8):830–831, August 1990.

[Pin89] David A. Pintsov. Invariant pattern recognition, symmetry, and radon transforms. Journal of the Optical Society of America A, 6(10):1544–1554, October 1989. Cross Correlation, Radon Transform, Hough Transform.

[PL92] Stavros J. Perantonis and Paulo J. G. Lisboa. Translation, rotation, and scale invariant pattern recognition by higher-order neural networks and moment classifier. IEEE Transactions on Neural Networks, 3(2):241–251, March 1992. HONN, Moment Classifiers.

[PW90] Wolfgang Pölzleitner and Harry Wechsler. Selective and focused invariant recognition using distributed associative memories (dam). IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(8):809–814, August 1990. Moore-Penrose DAM.

[PYIK89] J. Princen, H. K. Yuen, J. Illingworth, and J. Kittler. A comparison of hough transform methods. In IEEE 3rd International Conference on Image Processing and i ts Applications (conf. publ. no. 307), pages 73–77, 1989.

[RA84] Herbert J. P. Reitböck and Jürgen Altmann. A model for sizeand rotation-invariant pattern processing in the visual system. Biological Cybernetics, 51:113–121, 1984. Log-Polar Transform, Fourier Transform, R-Transform, Mellin-Transform.

[Sch77] E. L. Schwairtz. Spatial mapping in the primate sensory projection: Analytic structure and relevance to perception. Biological Cybernetics, 25:181–194, 1977.

[SJ92] Narayan Srinivasa and Musa Jouaneh. A neural network model for invariant pattern recognition. IEEE Transactions on Signal Processing, 40(6):1595–1599, June 1992. ART.

[SJ93] Narayan Srinivasa and Musa Jouaneh. An invariant pattern recognition machine using a modified art architecture. IEEE Transactions on Systems, Man, and Cybernetics, 23(5):1432–1437, September/October 1993. ART.

[SL91] Y. Sheng and C. Lejeune. Invariant pattern recognition using fourier-mellin transforms and neural networks. Journal of Optics, 22(5):223–228, 1991.

[SN93] R. S. Shadafan and M. Niranjan. A dynamic neural network architecture by sequential partitioning of the input space. In IEEE International Conference on Neural Networks, pages 226– 231, San Francisco, Ca., March/April 1993. IEEE.

[TG89] A. K. Tiwari and A. Gudadhe. The 2-dimensional distributional mellin transform and form invariant linear filtering. Journal of the Maulana Azad College of Technology, 22:15–22, 1989.

[Tin89] Christopher Hian-Ann Ting. Rotation invariant neocognitron. In IEEE International Conference on Image Processing and its Applications (conf. pub. no. 307), pages 2216–2221, 1989.

[Tiw89] A. K. Tiwari. Banach space valued distributional mellin transform and form invariant linear filtering. Indian Journal of Pure and Applied Mathematics, 20(5):493–504, May 1989. Mellin Transform.

[VC93] Sven Vinther and Roberto Cipolla. Towards 3d object model acquisition and recognition using 3d affine invariants. In 4th British Machine Vision Conference 1993, July 1993. Kalman Filter.

[vdMB87] C. von der Malsburg and E. Bienenstock. A neural network for the retrieval of superimposed connection patterns. Europhysics Letters, 3(11):1243–1249, June 1987. Hopfield Network.

[VZ92] H. Vogt and Anette Zippelius. Invariant recognition in pott glass neural networks. Journal of Physics A: Math. Gen., 25:2209– 2226, 1992. Hopfield Network.

[WWB88] Bernhard Widrow, Rodney G. Winter, and Robert A. Baxter. Layered neural nets for pattern recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 36(7):1109–1118, July 1988. ADALINE, MADALINE.

[WZ88] Harry Wechsler and George Lee Zimmerman. 2-d invariant object recognition using distributed associative memory. IEEE Transactions on Pattern Analysis and Machine Intelligence, 10(6):811– 821, November 1988. DAM, Complex-Log Mapping, Fourier Transform, Laplacian.

[YYL91] F. T. S. Yu, X. Yang, and T. Lu. Application of moment invariant pattern recognition to optical neural net. Optik, 89(2):55–58, 1991. Moment Invariants.

[Zee88] A. Zee. Some quantitative issues in the theory of perception. In Y. S. Lee, editor, Evolution, Learning, and Cognition, pages 183–215. World Scientific, Singapore, 1988. Visual Perception.

[Zer34] F. Zernike. Beugungstheorie des schneideverfahrens und seiner verbesserten form der phasenkontrastmethode. pages 689–704. 1934. Zernike Polynome, orhtogonale Kreisflächen Polynome.