Source: http://www.egmont-petersen.nl/nn-review.html
Timestamp: 2019-04-23 12:06:34+00:00

Document:
M. Egmont-Petersen, D. de Ridder, H. Handels. "Image processing with neural networks - a review," Pattern Recognition, Vol. 35, No. 10, pp. 2279-2301, 2002.
Read the Abstract or download the Reprint.
Some of the references can be obtained on-line from the following site.
1. H.M. Abbas, M.M. Fahmy, Neural networks for maximum likelihood clustering, Signal Processing 36 (1) (1994) 111-126.
2. A. Adler, R. Guardo, A neural network image reconstruction technique for electrical impedance tomography, IEEE Transactions on Medical Imaging 13 (4) (1994) 594-600.
3. M.N. Ahmed, A.A. Farag, Two-stage neural network for volume segmentation of medical images, Pattern Recognition Letters 18 (11-13) (1997) 1143-1151.
4. C. Alippi, Real-time analysis of ships in radar images with neural networks, Pattern Recognition 28 (12) (1995) 1899-1913.
5. C. Amerijckx, M. Verleysen, P. Thissen et al., Image compression by self-organized Kohonen map, IEEE Transactions on Neural Networks 9 (3) (1998) 503-507.
6. R. Anand, K. Mehrotra, C.K. Mohan et al., Analyzing images containing multiple sparse patterns with neural networks, Pattern Recognition 26 (11) (1993) 1717-1724.
7. D. Anguita, G. Parodi, R. Zunino, Associative structures for vision, Multidimensional Systems and Signal Processing 5 (1) (1994) 75-96.
8. N. Ansari, Z.Z. Zhang, Generalised adaptive neural filters, IEE Electronics Letters 29 (4) (1993) 342-343.
9. M. Antonucci, B. Tirozzi, N.D. Yarunin et al., Numerical simulation of neural networks with translation and rotation invatiant pattern recognition, International Journal of Modern Physics B 8 (11-12) (1994) 1529-1541.
10. H. Arnarson, L.F. Pau, PDL-HM: Morphological and syntactic shape classification algorithm, Machine Vision and Applications 7 (2) (1994) 59-68.
11. Armed Forces Communications and Electronics Association, DARPA neural network study, AFCEA, Fairfax, 1988.
12. J.J. Atick, P.A. Griffin, A.N. Redlich, Statistical approach to shape from shading - reconstruction of 3-D face surfaces from single 2-D images, Neural Computation 8 (6) (1996) 1321-1340.
13. N. Babaguchi, K. Yamada, K. Kise et al., Connectionist model binarization, International Journal of Pattern Recognition and Artificial Intelligence 5 (4) (1991) 629-644.
14. G.P. Babu, M.N. Murty, Optimal thresholding using multi state stochastic connectionist approach, Pattern Recognition Letters 16 (1) (1995) 11-18.
15. R.R. Bailey, M. Srinath, Orthogonal moment features for use with parametric and non-parametric classifiers, IEEE Transactions on Pattern Analysis and Machine Intelligence 18 (4) (1996) 389-399.
16. R. Bajaj, Chaudhury, S., Signature verification using multiple neural classifiers, Pattern Recognition 30 (1) (1997) 1-7.
17. P. Baldi, J. Hornik, Neural networks and principal component analysis: learning from examples without local minima, Neural Networks 2 (1) (1989) 53-58.
18. J. Basak, B. Chanda, D.D. Majumder, On edge and line linking in graylevel images with connectionist models, IEEE Transactions on Systems, Man and Cybernetics 24 (3) (1994) 413-428.
19. J. Basak, S.K. Pal, PsyCOP - A psychologically motivated connectionist system for object perception, IEEE Transactions on Neural Networks 6 (6) (1995) 1337-1354.
20. J. Basak, S.K. Pal, A connectionist system for learning and recognition of structures - application to handwritten characters, Neural Networks 8 (4) (1995) 643-657.
21. L. Bedini, A. Tonazzini, Image restoration preserving discontinuities: the Bayesian approach and neural networks, Image and Vision Computing 10 (2) (1992) 108-118.
22. Y. Bengio, P. Simard, P. Frasconi, Learning long-term dependencies with gradient descent is difficult, IEEE Transactions on Neural Networks 5 (2) (1994) 157-166.
23. E. Bertin, H. Bischof, P. Bertolino, Voronoi pyramids controlled by Hopfield neural networks, Computer Vision and Image Understanding 63 (3) (1996) 462-475.
24. C.M. Bishop, Neural networks for pattern recognition, Oxford University Press, Oxford, 1995.
25. W.E. Blanz, S.L. Gish, A real time image segmentation system using a connectionist classifier architecture, International Journal of Pattern Recognition and Artificial Intelligence 5 (4) (1991) 603-617.
26. A.G. Bors, I. Pitas, Optical flow estimation and moving object segmentation based on median radial basis function network, IEEE Transactions on Image Processing 7 (5) (1998) 693-702.
27. G.M.T. Brake, N. Karssemeijer, Single and multiscale detection of masses in digital mammograms, IEEE Transactions on Medical Imaging 18 (7) (1999) 628-639.
28. G.M.T. Brake, N. Karssemeijer, J.H.C.L. Hendriks, An automatic method to discriminate malignant masses from normal tissue in digital mammograms, Physics in Medicine and Biology 45 (10) (2000) 2843-2857.
29. R.W. Brause, M. Rippl, Noise suppressing sensor encoding and neural signal orthonormalization, IEEE Transactions on Neural Networks 9 (4) (1998) 613-628.
30. J. Buhmann, J. Lange, C.v.d. Malsburg et al., Object recognition with Gabor functions in the dynamic link architecture - parallel implementation on a transputer network, in: Neural networks for signal processing, B. Kosko, ed., 1992, Prentice-Hall, Englewood Cliffs, NJ, pp. 121-160.
31. C. Busch, M. Groß, Interactive neural network texture analysis and visualization for surface reconstruction in medical imaging, Proc. EUROGRAPHICS 1993, Barcelona, 1993, pp. C49-C60.
32. G.A. Carpenter, S. Grossberg, A massively parallel architecture for a self organizing neural pattern recognition machine, Computer Vision, Graphics and Image Processing 37 (1) (1987) 54-115.
33. G.A. Carpenter, W.D. Ross, ART-EMAP: A neural network architecture for object recognition by evidence accumulation, IEEE Transactions on Neural Networks 6 (4) (1995) 805-818.
34. G.A. Carpenter, S. Grossberg, G.W. Lesher, The what-and-where filter - a spatial mapping neural network for object recognition and image understanding, Computer Vision and Image Understanding 69 (1) (1998) 1-22.
35. C.A. Carson, J.M. Keller, K.K. McAdoo et al., Escherichia coli O157:H7 Restriction pattern recognition by artificial neural network, Journal of Clinical Microbiology 33 (11) (1995) 2894-2898.
36. D. Casasent, L.M. Neiberg, M.A. Sipe, Feature space trajectory distorted object representation for classification and pose estimation, Optical Engineering 37 (3) (1998) 914-920.
37. M. Cenci, C. Nagar, A. Vecchione, PAPNET-assisted primary screening of conventional cervical smears, Anticancer Research 20 (5C) (2000) 3887-3889.
38. B.-B. Chai, Huang, T., Zhuang, X., Zhao, Y., Sklansky, J., Piecewise linear classifiers using binary tree structure and genetic algorithm, Pattern Recognition 29 (11) (1996) 1905-1917.
39. H.-P. Chan, S.-C.B. Lo, B. Sahiner et al., Computer-aided detection of mammographic microcalcifications: Pattern recognition with an artificial neural network, Medical Physics 22 (10) (1995) 1555-1567.
40. V. Chandrasekaran, M. Palaniswami, T.M. Caelli, Range image segmentation by dynamic neural network architecture, Pattern Recognition 29 (2) (1996) 315-329.
41. S.H. Chang, G.H. Han, J.M. Valverde et al., Cork quality classification system using a unified image processing and fuzzy-neural network methodology, IEEE Transactions on Neural Networks 8 (4) (1997) 964-974.
42. R.F. Chang, W.J. Kuo, D.R. Chen et al., Computer-aided diagnosis for surgical office-based breast ultrasound, Archives of Surgery 135 (6) (2000) 696-699.
43. C.H. Chen, On the relationships between statistical pattern recognition and artificial neural networks, International Journal of Pattern Recognition and Artificial Intelligence 5 (4) (1991) 655-661.
44. C.T. Chen, E.C. Tsao, W.C. Lin, Medical image segmentation by a constraint satisfaction neural network, IEEE Transactions on Nuclear Science 38 (2) (1991) 678-686.
45. T.W. Chen, W.C. Lin, A neural network approach to CSG-based 3-D object recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence 16 (7) (1994) 719-726.
46. Q. Chen, M. Defrise, F. Deconinck, Symmetric phase-only matched filtering of Fourier-Mellin transforms for image registration and recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence 16 (12) (1994) 1156-1168.
47. K.S. Cheng, J.S. Lin, C.W. Mao, The application of competitive Hopfield neural network to medical image segmentation, IEEE Transactions on Medical Imaging 15 (4) (1996) 560-567.
48. J. Chey, S. Grossberg, E. Mingolla, Neural dynamics of motion grouping - from aperture ambiguity to object speed and direction [review], Journal of the Optical Society of America A-Optics and Image Science 14 (10) (1997) 2570-2594.
49. G.I. Chiou, J.N. Hwang, A neural network based stochastic active contour model (NNS- SNAKE) for contour finding of distinct features, IEEE Transactions on Image Processing 4 (10) (1995) 1407-1416.
50. C. Chong, J. Jia, Assessments of neural network classifier output codings using variability of Hamming distance, Pattern Recognition Letters 17 (8) (1996) 811-818.
51. S.S. Christensen, A.W. Andersen, T.M. Jørgensen et al., Visual guidance of a pig evisceration robot using neural networks, Pattern Recognition Letters 17 (4) (1996) 345-355.
52. W.J. Christmas, J. Kittler, M. Petrou, Analytical approaches to the neural net architecture design, Proc. Pattern Recognition in Practice IV, Vlieland, 1994, pp. 325-335.
53. W. Chua, L. Yang, Cellular networks: theory, IEEE Transactions on Circuits and Systems 35 (10) (1988) 1257-1272.
54. W. Chua, L. Yang, Cellular networks: applications, IEEE Transactions on Circuits and Systems 35 (10) (1988) 1273-1290.
55. P.C. Chung, C.T. Tsai, E.L. Chen et al., Polygonal approximation using a competitive Hopfield neural network, Pattern Recognition 27 (11) (1994) 1505-1512.
56. J. Cornfield, Statistical classification methods, Proc. 2nd Conference on the diagnostic process, computer diagnosis and diagnostic methods, Chicago, 1972, pp. 108-130.
57. G.H. Cottet, M. Elayyadi, A Volterra type model for image processing, IEEE Transactions on Image Processing 7 (3) (1998) 292-303.
58. S.M. Courtney, L.H. Finkel, G. Buchsbaum, A multistage neural network for color constancy and color induction, IEEE Transactions on Neural Networks 6 (4) (1995) 972-985.
59. J.M. Cruz, G. Pajares, J. Aranda et al., Stereo matching technique based on the perceptron criterion function, Pattern Recognition Letters 16 (9) (1995) 933-944.
60. J.M. Cruz, G. Pajares, J. Aranda, A neural network model in stereovision matching, Neural Networks 8 (5) (1995) 805-813.
61. W.R.M. Dassen, M. Egmont-Petersen, R.G.A. Mulleneers, Artificial neural networks in cardiology; a review, in: Cardiac Arrhythmias, Pacing & Electrophysiology, P.E. Vardas, ed., 1998, Kluwer Academic Publishers, London, pp. 205-211.
62. J.G. Daugman, Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression, IEEE Transactions on Acoustics, Speech and Signal Processing 36 (7) (1988) 1169-1179.
63. D. de Ridder, R.P.W. Duin, P.W. Verbeek et al., The applicability of neural networks to non-linear image processing, Pattern Analysis and Applications 2 (2) (1999) 111-128.
64. D. DeKruger, Hunt, B.R., Image processing and neural networks for recognition of cartographic area features, Pattern Recognition 27 (4) (1994) 461-483.
65. A. Delopoulos, A. Tirakis, S. Kollias, Invariant image classification using triple-correlation-based neural networks, IEEE Transactions on Neural Networks 5 (3) (1994) 392-408.
66. D. DeMers, G.W. Cottrell, Non-linear dimensionality reduction, Proc. Advances in Neural Information Processing Systems, 1993, pp. 580-587.
67. J. Desachy, L. Roux, E.-H. Zahzah, Numeric and symbolic data fusion: A soft computing approach to remote sensing images analysis, Pattern Recognition Letters 17 (13) (1996) 1361-1378.
68. S. Deschênes, Y. Sheng, P.C. Chevrette, Three-dimensional object recognition from two-dimensional images using wavelet transforms and neural networks, Optical Engineering 37 (3) (1998) 763-770.
69. P.A. Devijver, J. Kittler, Pattern recognition: a statistical approach, Englewood Cliffs, London, 1982.
70. J. DeVilliers, E. Barnard, Backpropagation neural nets with one and two hidden layers, IEEE Transactions on Neural Networks 4 (1) (1993) 136-141.
71. W.P. Dewaard, Neural techniques and postal code detection, Pattern Recognition Letters 15 (2) (1994) 199-205.
72. E. do Valle Simões, L.F. Uebel, D.A.C. Barone, Hardware implementation of RAM neural networks, Pattern Recognition Letters 17 (4) (1996) 421-429.
73. R.D. Dony, S. Haykin, Optimally adaptive transform coding, IEEE Transactions on Image Processing 4 (10) (1995) 1358-1370.
74. R.D. Dony, S. Haykin, Neural network approaches to image compression, Proceedings of the IEEE 83 (2) (1995) 288-303.
75. I.E. Dror, M. Zagaeski, C.F. Moss, 3-D target recognition via sonar - a neural network model, Neural Networks 8 (1) (1995) 149-160.
76. J.M.H. Du Buf, M. Kardan, M. Spann, Texture feature performance for image segmentation, Pattern Recognition 23 (3-4) (1990) 291-309.
77. M. Egmont-Petersen, J.L. Talmon, J. Brender et al., On the quality of neural net classifiers, Artificial Intelligence in Medicine 6 (5) (1994) 359-381.
78. M. Egmont-Petersen, J.L. Talmon, A. Hasman, Robustness metrics for measuring the influence of additive noise on the performance of statistical classifiers, International Journal of Medical Informatics 46 (2) (1997) 103-112.
79. M. Egmont-Petersen, J.L. Talmon, A. Hasman et al., Assessing the importance of features for multi-layer receptrons, Neural Networks 11 (4) (1998) 623-635.
80. M. Egmont-Petersen, W.R.M. Dassen, C.J.H.J. Kirchhof et al., An explanation facility for a neural network trained to predict arterial fibrillation directly after cardiac surgery, Proc. Computers in Cardiology 1998, Cleveland, 1998, pp. 489-492.
81. M. Egmont-Petersen, E. Pelikan, Detection of bone tumours in radiographs using neural networks, Pattern Analysis and Applications 2 (2) (1999) 172-183.
82. M. Egmont-Petersen, T. Arts, Recognition of radiopaque markers in X-ray images using a neural network as nonlinear filter, Pattern Recognition Letters 20 (5) (1999) 521-533.
83. M. Egmont-Petersen, W.R.M. Dassen, J.H.C. Reiber, Sequential selection of discrete features for neural networks - a Bayesian approach to building a cascade, Pattern Recognition Letters 20 (11-13) (1999) 1439-1448.
84. M. Egmont-Petersen, U. Schreiner, S.C. Tromp et al., Detection of leukocytes in contact with the vessel wall from in vivo microscope recordings using a neural network, IEEE Transactions on Biomedical Engineering 47 (7) (2000) 941-951.
85. A.J. Einstein, J. Barba, P.D. Unger et al., Nuclear diffuseness as a measure of texture: definition and application to the computer-assisted diagnosis of parathyroid adenoma and carcinoma, Journal of Microscopy 176 (2) (1994) 158-166.
86. F. Ercal, A. Chawla, W.V. Stoecker et al., Neural network diagnosis of malignant melanoma from color images, IEEE Transactions on Biomedical Engineering 41 (9) (1994) 837-845.
87. W.-C. Fang, B.J. Sheu, O.T.-C. Chen et al., A VLSI neural processor for image data compression using self-organization networks, IEEE Transactions on Neural Networks 3 (3) (1992) 506-518.
88. M.A.T. Figueiredo, J.M.N. Leitao, Sequential and parallel image restoration: Neural network implementations, IEEE Transactions on Image Processing 3 (6) (1994) 789-801.
89. L.M.J. Florack, B.M. ter Haar Romeny, J.J. Koenderink et al., Scale and the differential structure of images, Image and Vision Computing 10 (6) (1992) 376-388.
90. L.M.J. Florack, The syntactical structure of scalar images, thesis, Image Sciences Institute, Utrecht University, Utrecht, 1993.
91. D.B. Fogel, An information criterion for optimal neural network selection, IEEE Transactions on Neural Networks 2 (5) (1991) 490-497.
92. G.L. Foresti, G. Pieroni, Exploiting neural trees in range image understanding, Pattern Recognition Letters 19 (9) (1998) 869-878.
93. M. Franzke, H. Handels, Topologische Merkmalskarten zur automatischen Mustererkennung in medizinischen Bilddaten, in: Informatik Aktuell, Mustererkennung 1992, 14. DAGM-Symposium, S. Fuchs and R. Hoffmann, eds., 1992, Springer Verlag, Heidelberg, pp. 329-334.
94. M. Fukumi, S. Omatu, F. Takeda et al., Rotation-invariant neural pattern-recognition system with application to coin recognition, IEEE Transactions on Neural Networks 3 (2) (1992) 272-279.
95. M. Fukumi, S. Omatu, Y. Nishikawa, Rotation-invariant neural pattern recognition system estimating a rotation angle, IEEE Transactions on Neural Networks 8 (3) (1997) 568-581.
96. K. Fukunaga, Introduction to statistical pattern recognition, 2nd ed., Academic Press, New York, 1990.
97. K. Fukushima, S. Miyake, T. Ito, Neocognitron: a neural model for a mechanism of visual pattern recognition, IEEE Transactions on Systems, Man and Cybernetics 13 (5) (1983) 826-834.
98. K. Fukushima, Neocognitron: a hierarchical neural network capable of visual pattern recognition, Neural Networks 1 (2) (1988) 119-130.
99. K.-I. Funahashi, On the approximate realization of continuous mappings by neural networks, Neural Networks 2 (3) (1989) 183-192.
100. K.-I. Funahashi, Multilayer neural networks and Bayes decision theory, Neural Networks 11 (2) (1998) 209-213.
101. M.D. Garris, C.L. Wilson, J.L. Blue, Neural network-based systems for handprint OCR applications, IEEE Transactions on Image Processing 7 (8) (1998) 1097-1112.
102. S. Geman, E. Bienenstock, R. Doursat, Neural networks and the bias/variance dilemma, Neural Computation 4 (1) (1992) 1-58.
103. S. Ghosal, R. Mehrotra, Range surface characterization and segmentation using neural networks, Pattern Recognition 28 (5) (1995) 711-727.
104. A. Ghosh, N.R. Pal, S.K. Pal, Image segmentation using a neural network, Biological Cybernetics 66 (2) (1991) 151-158.
105. A. Ghosh, S.K. Pal, Neural network, self-organization and object extraction, Pattern Recognition Letters 13 (5) (1992) 387-397.
106. A. Ghosh, N.R. Pal, S.K. Pal, Object background classification using Hopfield type neural network, International Journal of Pattern Recognition and Artificial Intelligence 6 (5) (1992) 989-1008.
107. A. Ghosh, N.R. Pal, S.K. Pal, Self-organization for object extraction using multilayer neural network and fuzziness measures, IEEE Transactions on Fuzzy Systems 1 (1) (1993) 54-68.
108. A. Ghosh, Use of fuzziness measures in layered networks for object extraction: a generalization, Fuzzy Sets and Systems 72 (3) (1995) 331-348.
109. G.L. Giles, T. Maxwell, Learning, invariance and generalization in higher-order neural networks, Applied Optics 26 (23) (1987) 4972-4978.
110. J.O. Glass, W.E. Reddick, Hybrid artificial neural network segmentation and classification of dynamic contrast-enhanced MR imaging (DEMRI) of osteosarcoma, Magnetic Resonance Imaging 16 (9) (1998) 1075-1083.
111. C. Goerick, D. Noll, M. Werner, Artificial neural networks in real-time car detection and tracking applications, Pattern Recognition Letters 17 (4) (1996) 335-343.
112. L.S. Goodenday, K.J. Cios, I. Shin, Identifying coronary stenosis using an image-recognition neural network, IEEE Engineering in Medicine and Biology 16 (5) (1997) 139-144.
113. M. Gori, F. Scarselli, A. Chung Tsoi, On the closure of the set of functions that can be realized by a given multilayer perceptron, IEEE Transactions on Neural Networks 9 (6) (1998) 1086-1098.
114. H.P. Graf, C.R. Nohl, J. Ben, Image recognition with an analog neural net chip, Machine Vision and Applications 8 (2) (1995) 131-140.
115. D. Greenhil, E.R. Davies, Relative effectiveness of neural networks for image noise suppression, Proc. Pattern Recognition in Practice IV, Vlieland, 1994, pp. 367-378.
116. S. Grossberg, E. Mingolla, Neural dynamics of surface perception: boundary webs, illuminants, and shape from shading, Computer Vision, Graphics, and Image Processing 37 (1) (1987) 116-165.
117. S. Grossberg, N.P. Mcloughlin, Cortical dynamics of three-dimensional surface perception - binocular and half-occluded scenic images, Neural Networks 10 (9) (1997) 1583-1605.
118. M. Groß, F. Seibert, Visualization of multidimensional data sets using a neural network, The Visual Computer 10 (3) (1993) 145-159.
119. L. Guan, J.A. Anderson, J.P. Sutton, A network of networks processing model for image regularization, IEEE Transactions on Neural Networks 8 (1) (1997) 169-174.
120. A. Hakulinen, J. Hakkarainen, A neural network approach to quality control of padlock manufacturing, Pattern Recognition Letters 17 (4) (1996) 357-362.
121. L.O. Hall, A.M. Bensaid, L.P. Clarke et al., A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain, IEEE Transactions on Neural Networks 3 (5) (1992) 672-682.
122. E.R. Hancock, J. Kittler, A Bayesian interpretation for the Hopfield network, Proc. IEEE Conference on Neural Networks, San Francisco, CA, 1993, pp. 341-346.
123. H. Handels, C. Busch, J. Encarnacao et al., KAMEDIN: A telemedicine system for computer supported cooperative work and remote image analysis in radiology, Computer Methods and Programs in Biomedicine 52 (3) (1997) 175-183.
124. H. Handels, Medizinische Bildverarbeitung, Teubner Verlag, Stuttgart, 2000.
125. H. Hanek, N. Ansari, Speeding up the generalized adaptive neural filters, IEEE Transactions on Image Processing 5 (5) (1996) 705-712.
126. S. Haring, M.A. Viergever, J.N. Kok, Kohonen networks for multiscale image segmentation, Image and Vision Computing 12 (6) (1994) 339-344.
127. T. Hastie, Principal curves and surfaces, thesis, Dept. of Statistics, Stanford University, 1984.
128. T. Hastie, W. Stuetzle, Principal curves, Journal of the American Statistical Association 84 (406) (1989) 502-516.
129. G. Hauske, A self organizing map approach to image quality, Biosystems 40 (1-2) (1997) 93-102.
130. S. Haykin, Neural networks: a comprehensive foundation, Macmillan College Publishing Co., New York, 1994.
131. R. Hecht-Nielsen, Nearest matched filter classification of spatio-temporal patterns, Applied Optics 26 (10) (1987) 1892-1899.
132. J. Heikkonen, A computer vision approach to air flow analysis, Pattern Recognition Letters 17 (4) (1996) 369-385.
133. J. Heikkonen, M. Mäntynen, A computer vision approach to digit recognition on pulp bales, Pattern Recognition Letters 17 (4) (1996) 413-419.
134. J. Hertz, A. Krogh, R.G. Palmer, Introduction to the theory of neural computation, Addison-Wesley, Reading, 1991.
135. M. Heywood, P. Noakes, A framework for improved training of Sigma-Pi networks, IEEE Transactions on Neural Networks 6 (4) (1995) 893-903.
136. F.S. Hiller, G.J. Lieberman, Introduction to operations research, 6'th ed., McGraw-Hill, New York, 1995.
137. G.E. Hinton, P. Dayan, M. Revow, Modelling the manifolds of images of handwritten digits, IEEE Transactions on Neural Networks 8 (1) (1997) 65-74.
138. S.W. Hong, Y.H. Chan, W.C. Siu, A new approach for real-time reduction of blocking effect, Signal Processing 65 (3) (1998) 337-346.
139. J.J. Hopfield, Neural networks and physical systems with emergent collective computational abilities, Proceedings of the National Academy of Sciences of the U.S.A. 81 (1982) 3088-3092.
140. J.J. Hopfield, D.W. Tank, Neural computation of decisions in optimization problems, Biological Cybernetics 52 (3) (1985) 141-152.
141. K. Hornik, M. Stinchcombe, H. White, Multilayer feedforward networks are universal approximators, Neural Networks 2 (5) (1989) 359-366.
142. K. Hornik, M. Stinchcombe, H. White, Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks, Neural networks 3 (5) (1990) 551-560.
143. K. Hornik, Approximation capabilities of multilayer feedforward networks, Neural Networks 4 (2) (1991) 251-257.
146. Q. Huang, B. Dom, Quantitative methods of evaluating image segmentation, Proc. International Conference on Image Processing (ICIP'95), 1995, pp. 53-56.
147. K. Huang, H. Yan, Off-line signature verification based on geometric feature extraction and neural network classification, Pattern Recognition 30 (1) (1997) 9-17.
148. T.L. Huntsberger, Biologically motivated cross-modality sensory fusion systems for automatic target recognition, Neural Networks 8 (7-8) (1995) 1215-1226.
149. R.E. Hurst, R.B. Bonner, K. Ashenayi et al., Neural net-based identification of cells expressing the p300 tumor-related antigen using fluorescence image analysis, Cytometry 27 (1) (1997) 36-42.
150. K.M. Iftekharuddin, T.D. Schechinger, K. Jemili et al., Feature-based neural wavelet optical character recognition system, Optical Engineering 34 (11) (1995) 3193-3199.
151. K. Itoh, ID number recognition of X-ray films by a neural network, Computer Methods and Programs in Biomedicine 43 (1-2) (1994) 15-18.
152. A.K. Jain, K. Karu, Automatic filter design for texture discrimination, Proc. 12th IAPR International Conference on Pattern Recognition, Jerusalem, 1994, pp. 454-458.
153. A.K. Jain, D. Zongker, Feature selection: Evaluation, application, and small sample performance, IEEE Transactions on Pattern Analysis and Machine Intelligence 19 (2) (1997) 153-158.
155. B. Javidi, Q. Tang, Optical implementation of neural networks by the use of nonlinear joint transform correlators, Applied Optics 34 (20) (1995) 3950-3962.
156. B. Jähne, Digital image processing. Concepts, algorithms and scientific applications, Springer Verlag, Berlin, 1995.
157. T.M. Jørgensen, S.S. Christensen, A.W. Andersen, Detecting danger labels with RAM-based neural networks, Pattern Recognition Letters 17 (4) (1996) 399-412.
158. P. Juell, R. Marsh, A hierarchical neural network for human face detection, Pattern Recognition 29 (5) (1996) 781-787.
159. H. Kai, H. Yan, Off-line signature verification based on geometric feature extraction and neural network classification, Pattern Recognition 30 (1) (1997) 9-17.
160. H.J. Kappen, W. Wiegerinck, T. Morgan et al., Stimulation initiative for european neural applications (SIENA), in: vol. 8, Neural networks: Best practice in Europe, B. Kappen and S. Gielen, eds., 1997, World Scientific, Singapore, pp. 1-8. (Http://www.mbfys.kun.nl/snn/Research/siena/index.html).
161. N. Karssemeijer, G.M.T. Brake, Detection of stellate distortions in mammograms, IEEE Transactions on Medical Imaging 15 (5) (1996) 611-619.
162. V. Karthaus, H. Thygesen, M. Egmont-Petersen et al., User-requirements driven learning, Computer Methods and Programs in Biomedicine 48 (1-2) (1995) 39-44.
163. N.K. Kasabov, S.I. Israel, B.J. Woodford, Adaptive, evolving, hybrid connectionist systems for image pattern recognition, in: Soft computing for image processing, S.K. Pal, A. Ghosh, and M.K. Kundu, eds., 2000, Physica-Verlag, Heidelberg.
164. V.Z. Këpuska, S.O. Mason, A hierarchical neural network system for signalized point recognition in aerial photographs, Photogrammetric Engineering & Remote Sensing 61 (7) (1995) 917-925.
165. A. Khotanzad, J.H. Lu, Classification of invariant image representations using a neural network, IEEE Transactions on Acoustics, Speech and Signal Processing 38 (6) (1990) 1028-1038.
166. E.-K. Kim, J.-T. Wu, S. Tamura et al., Comparison of neural network and k-NN classification methods in vowel and patellar subluxation image recognitions, International Journal of Pattern Recognition and Artificial Intelligence 7 (4) (1993) 775-782.
167. H.J. Kim, H.S. Yang, A neural network capable of learning and inference for visual pattern recognition, Pattern Recognition 27 (10) (1994) 1291-1302.
168. J.H. Kim, H.S. Cho, Neural network-based inspection of solder joints using a circular illumination, Image and Vision Computing 13 (6) (1995) 479-490.
169. H. Kim, K. Nam, Object recognition of one-dof tools by back-propagation neural net, IEEE Transactions on Neural Networks 6 (2) (1995) 484-487.
170. H.M. Kim, B. Kosko, Motion estimation and compensation with neural fuzzy systems, in: Soft computing for image processing, S.K. Pal, A. Ghosh, and M.K. Kundu, eds., 2000, Physica-Verlag, Heidelberg.
171. J. Kittler, M. Hatef, R.P.W. Duin et al., On combining classifiers, IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (3) (1998) 226 -239.
172. J. Koh, M.S. Suk, S.M. Bhandarkar, A multilayer self organizing feature map for range image segmentation, Neural Networks 8 (1) (1995) 67-86.
173. T. Kohonen, Self-organized formation of topologically correct feature maps, Biological Cybernetics 43 (1) (1982) 59-69.
174. T. Kohonen, Clustering, taxonomy and topological maps of patterns, Proc. 6th IAPR International Conference on Pattern Recognition, München, 1982, pp. 114-128.
175. T. Kohonen, Self-organizing maps, vol. 30, Springer Series in Information Sciences, Springer Verlag, Berlin, 1995.
176. B. Kosko, Adaptive bidirectional associative memories, Applied Optics 26 (23) (1987) 4947-4960.
177. C. Kotropoulos, X. Magnisalis, I. Pitas et al., Nonlinear ultrasonic image processing based on signal-adaptive filters and self-organizing neural networks, IEEE Transactions on Image Processing 3 (1) (1994) 65-77.
178. M. Kramer, Nonlinear principal component analysis using autoassociative neural networks, American Institute of Chemical Engineers Journal 37 (2) (1991) 223-243.
179. A.D. Kulkari, Artificial neural networks for image understanding, Van Nostrand Reinhold, New York, NY, 1994.
180. S.Y. Kung, J.S. Taur, Decision based neural networks with signal image classification applications, IEEE Transactions on Neural Networks 6 (1) (1995) 170-181.
181. A. Laine, J. Fan, Texture classification by wavelet packet signatures, IEEE Transactions on Pattern Analysis and Machine Intelligence 15 (11) (1993) 1186-1191.
182. J. Lampinen, E. Oja, Distortion tolerant pattern recognition based on self-organizing feature extraction, IEEE Transactions on Neural Networks 6 (3) (1995) 539-547.
183. J. Lampinen, S. Smolander, Self-organizing feature extraction in recognition of wood surface defects and color images, International Journal of Pattern Recognition and Artificial Intelligence 10 (2) (1996) 97-113.
184. J. Lampinen, E. Oja, Pattern recognition, in: Neural network systems, techniques and applications, vol. 5, Image processing and pattern recognition, C.T. Leondes, ed., 1998, Academic Press, pp. 1-59.
185. S. Lawrence, C.L. Giles, A.C. Tsoi et al., Face recognition - a convolutional neural-network approach, IEEE Transactions on Neural Networks 8 (1) (1997) 98-113.
186. D.X. Le, G.R. Thoma, H. Wechsler, Classification of binary document images into textual or nontextual data blocks using neural network models, Machine Vision and Applications 8 (5) (1995) 289-304.
187. Y. LeCun, L.D. Jackel, B. Boser et al., Handwritten digit recognition - applications of neural network chips and automatic learning, IEEE Communications Magazine 27 (11) (1989) 41-46.
188. Y. LeCun, B. Boser, J.S. Denker et al., Backpropagation applied to handwritten zip code recognition, Neural Computation 1 (4) (1989) 541-551.
189. C.C. Lee, J.P. Degyvez, Color image processing in a cellular neural-network environment, IEEE Transactions on Neural Networks 7 (5) (1996) 1086-1098.
190. S.K. Lee, C.S. Lo, C.M. Wang et al., A computer-aided design mammography screening system for detection and classification of microcalcifications, International Journal of Medical Informatics 60 (1) (2000) 29-57.
191. B. Lerner, Toward a completely automatic neural-network-based human chromosome analysis, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 28 (4) (1998) 544-552.
192. X.H. Li, S. Bhide, M.R. Kabuka, Labelling of MR brain images using Boolean neural network, IEEE Transactions on Medical Imaging 15 (5) (1996) 628-638.
193. W.-C. Lin, E.C.-K. Tsao, C.-T. Chen, Constraint satisfaction neural networks for image segmentation, Pattern Recognition 25 (7) (1992) 679-693.
194. J.S. Lin, S.C.B. Lo, A. Hasegawa et al., Reduction of false positives in lung nodule detection using a two-level neural classification, IEEE Transactions on Medical Imaging 15 (2) (1996) 206-217.
195. S.C.B. Lo, H.P. Chan, J.S. Lin et al., Artificial convolution neural network for medical image pattern recognition, Neural Networks 8 (7-8) (1995) 1201-1214.
196. S. Lu, A. Szeto, Improving edge measurements on noisy images by hierarchical neural networks, Pattern Recognition Letters 12 (3) (1991) 155-164.
197. S. Lu, A. Szeto, Hierarchical artificial neural networks for edge enhancement, Pattern Recognition 26 (8) (1993) 1149-1163.
198. S.W. Lu, H. Xu, Textured image segmentation using autoregressive model and artificial neural network, Pattern Recognition 28 (12) (1995) 1807-1817.
199. S.P. Luttrell, Image compression using a multilayer neural network, Pattern Recognition Letters 10 (1) (1989) 1-7.
200. R.J. Machado, V.C. Barbosa, P.A. Neves, Learning in the combinatorial neural model, IEEE Transactions on Neural Networks 9 (5) (1998) 831-847.
201. B.S. Manjunath, T. Simchony, R. Chellappa, Stochastic and deterministic networks for texture segmentation, IEEE Transactions on Acoustics, Speech and Signal Processing 38 (6) (1990) 1039-1049.
202. J.A. Marshall, Self-organizing neural networks for perception of visual motion, Neural Networks 3 (1) (1990) 45-74.
203. J.A. Marshall, Adaptive perceptual pattern recognition by self organizing neural networks: Context, uncertainty, multiplicity, and scale, Neural Networks 8 (3) (1995) 335-362.
204. T. Matsumoto, H. Kobayashi, Y. Togawa, Spatial versus temporal stability issues in image processing neuro chips, IEEE Transactions on Neural Networks 3 (4) (1992) 540-569.
205. M.F. McNittgray, H.K. Huang, J.W. Sayre, Feature selection in the pattern classification problem of digital chest radiograph segmentation, IEEE Transactions on Medical Imaging 14 (3) (1995) 537-547.
206. M.R.J. McQuoid, Neural ensembles: Simultaneous recognition of multiple 2-D visual objects, Neural Networks 6 (7) (1993) 970-917.
207. R.R. Meyer, E. Heindl, Reconstruction of off-axis electron holograms using a neural net, Journal of Microscopy 191 (1) (1998) 52-59.
208. A. Mitiche, J.K. Aggarwal, Pattern category assignment by neural networks and nearest neighbours rule: A synopsis and a characterization, International Journal of Pattern Recognition and Artificial Intelligence 10 (5) (1996) 393-408.
209. S. Mitra, S.Y. Yang, High fidelity adaptive vector quantization at very low bit rates for progressive transmission of radiographic images, Journal of Electronic Imaging 8 (1) (1999) 23-35.
210. S. Mitra, R. Castellanos, S.Y. Yang et al., Adaptive clustering for efficient segmentation and vector quantization of images, in: Soft computing for image processing, S.K. Pal, A. Ghosh, and M.K. Kundu, eds., 2000, Physica-Verlag, Heidelberg.
211. D.A. Mitzias, Mertzios, B.G., Shape recognition with a neural network classifier based on a fast polygon approximation technique, Pattern Recognition 27 (5) (1994) 627-636.
212. J. Moh, F.Y. Shih, A general purpose model for image operations based on multilayer perceptrons, Pattern Recognition 28 (7) (1995) 1083-1090.
213. R.J.T. Morris, L.D. Rubin, H. Tirri, Neural network techniques for object orientation detection: solution by optimal feedforward network and learning vector quantization approaches, IEEE Transactions on Pattern Analysis and Machine Intelligence 12 (11) (1990) 1107-1115.
214. M. Mougeot, R. Azencott, B. Angeniol, Image compression with back propagation: Improvement of the visual restoration using different cost functions, Neural Networks 4 (4) (1991) 467-476.
215. N. Murata, S. Yoshizawa, S. Amari, Network information criterion - determining the number of hidden units for an artificial neural network model, IEEE Transactions on Neural Networks 5 (6) (1994) 865-872.
216. N.M. Nasrabadi, W. Li, Object recognition by a Hopfield neural network, IEEE Transactions on Systems, Man and Cybernetics 21 (6) (1991) 1523-1535.
217. N.M. Nasrabadi, C.Y. Choo, Hopfield network for stereo correspondence, IEEE Transactions on Neural Networks 3 (1) (1992) 5-13.
218. H. Navabi, A. Agarwal, Adaptive response organizer network for space-time patterns in low level vision, Neural Networks 11 (5) (1998) 825-836.
219. C. Neubauer, Evaluation of convolutional neural networks for visual recognition, IEEE Transactions on Neural Networks 9 (4) (1998) 685-696.
220. S.C. Ngan, X. Hu, Analysis of functional magnetic resonance imaging data using self-organizing mapping with spatial connectivity, Magnetic Resonance in Medicine 41 (5) (1999) 939-946.
221. H. Niemann, J.K. Wu, Neural network adaptive image coding, IEEE Transactions on Neural Networks 4 (4) (1993) 615-627.
223. E. Oja, A simplified neuron model as a principal component analyzer, Journal of Mathematical Biology 15 (3) (1982) 267-273.
224. E. Oja, Subspace methods of pattern recognition, J. Wiley, New York, NY, 1983.
225. E. Oja, Neural networks, principal components, and subspaces, International Journal of Neural Systems 1 (1) (1989) 61-68.
226. E. Oja, Data compression, feature extraction, and autoassociation in feed-forward neural networks, Proc. International Conference on Artificial Neural Networks, Helsinki, Finland, 1991, pp. 737-745.
227. R. Opara, F. Worgotter, Using visual latencies to improve image segmentation, Neural Computation 8 (7) (1996) 1493-1520.
228. M. Ozkan, B.M. Dawant, R.J. Maciunas, Neural-network-based segmentation of multi-modal medical images - a comparative and prospective study, IEEE Transactions on Medical Imaging 12 (3) (1993) 534-544.
229. J.K. Paik, A.K. Katsaggelos, Image restoration using a modified Hopfield network, IEEE Transactions on Image Processing 1 (1) (1992) 49-63.
230. N.R. Pal, S.K. Pal, A review on image segmentation techniques, Pattern Recognition 26 (9) (1993) 1277-1294.
231. S.K. Pal, A. Ghosh, Neuro-fuzzy computing for image processing and pattern recognition, International Journal of Systems Science 27 (12) (1996) 1179-1193.
232. Y. Pan, A note on efficient parallel algorithms for the computation of 2D image moments, Pattern Recognition 24 (9) (1991) 917.
233. T.N. Pappas, An adaptive clustering algorithm for image segmentation, IEEE Transactions on Signal Processing 40 (4) (1992) 901-914.
234. E. Paquet, Rioux, M., Arsenault, H.H., Invariant pattern recognition for range images using the phase Fourier transform and a neural network, Optical Engineering 34 (4) (1994) 1178-1183.
235. Y. Park, A comparison of neural net classifiers and linear tree classifiers: their similarities and differences, Pattern Recognition 27 (11) (1994) 1493-1503.
236. G. Pasquariello, G. Satalino, V.l. Forgia et al., Automatic target recognition for naval traffic control using neural networks, Image and Vision Computing 16 (2) (1998) 67-73.
237. D. Patel, E.R. Davies, I. Hannah, The use of convolution operators for detecting contaminants in food images, Pattern Recognition 29 (6) (1996) 1019-1029.
238. M.G. Penedo, M.J. Carreira, A. Mosquera et al., Computer-aided diagnosis: A neural-network-based approach to lung nodule detection, IEEE Transactions on Medical Imaging 17 (6) (1998) 872-880.
239. L.I. Perlovsky, W.H. Schoendorf, B.J. Burdick et al., Model-based neural network for target detection in SAR images, IEEE Transactions on Image Processing 6 (1) (1997) 203-216.
240. L.I. Perlovsky, Conundrum of combinatorial complexity, IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (6) (1998) 666-670.
241. D.T. Pham, E.J. Bayro-Corrochano, Neural computing for noise filtering, edge detection and signature extraction, Journal of Systems Engineering 2 (2) (1992) 111-222.
242. V.V. Phoha, W.J.B. Oldham, Image recovery and segmentation using competitive learning in a layered network, IEEE Transactions on Neural Networks 7 (4) (1996) 843-856.
243. T. Poggio, C. Koch, Ill-posed problems in early vision: from computational theory to analogue networks, Proceedings of the Royal Society London B (226) (1985) 303-323.
244. W.K. Pratt, Digital image processing, 2nd ed., John Wiley & Sons, New York, 1991.
245. J.C. Principe, M. Kim, J.W. Fisher, Target discrimination in synthetic aperture radar using artificial neural networks, IEEE Transactions on Image Processing 7 (8) (1998) 1136-1149.
246. P. Pudil, J. Novovicová, J. Kittler, Floating search methods in feature selection, Pattern Recognition Letters 15 (11) (1994) 1119-1125.
247. R.H. Pugmire, R.M. Hodgson, R.I. Chaplin, The properties and training of a neural network based universal window filter developed for image processing tasks, in: Brain-like computing and intelligent information systems, S. Amari and N. Kasabov, eds., 1998, Springer-Verlag, Singapore, pp. 49-77.
248. W. Qian, M. Kallergi, L.P. Clarke, Order statistic-neural network hybrid filters for gamma-camera-bremsstrahlung image restoration, IEEE Transactions on Medical Imaging 12 (1) (1993) 58-64.
249. P.P. Raghu, R. Poongodi, B. Yegnanarayana, A combined neural network approach for texture classification, Neural Networks 8 (6) (1995) 975-987.
250. P.P. Raghu, B. Yegnanarayana, Multispectral image classification using gabor filters and stochastic relaxation neural network, Neural Networks 10 (3) (1997) 561-572.
251. P.P. Raghu, R. Poongodi, B. Yegnanarayana, Unsupervised texture classification using vector quantization and deterministic relaxation neural network, IEEE Transactions on Image Processing 6 (10) (1997) 1376-1387.
252. S. Ramanan, R.S. Petersen, T.G. Clarkson et al., pRAM nets for detection of small targets in sequences of infra-red images, Neural Networks 8 (7-8) (1995) 1227-1237.
253. A. Ravichandran, B. Yegnanarayana, Studies on object recognition from degraded images using neural networks, Neural Networks 8 (3) (1995) 481-488.
254. W.E. Reddick, J.O. Glass, E.N. Cook et al., Automated segmentation and classification of multispectral magnetic resonance images of brain using artificial neural networks, IEEE Transactions on Medical Imaging 16 (6) (1997) 911-918.
255. W.R. Reinus, A.J. Wilson, B. Kalman et al., Diagnosis of focal bone lesions using neural networks, Investigative Radiology 29 (6) (1994) 606-611.
256. M.D. Richard, R.P. Lippmann, Neural network classifiers estimate bayesian posterior probabilities, Neural Computation 3 (4) (1991) 461-483.
257. H. Ritter, T. Martinez, K. Schulten, Neuronale Netze, Addison-Wesley, Bonn, 1991.
258. S.A. Rizvi, L.C. Wang, N.M. Nasrabadi, Nonlinear vector prediction using feed-forward neural networks, IEEE Transactions on Image Processing 6 (10) (1997) 1431-1436.
259. F. Ros, S. Guillaume, G. Rabatel et al., Recognition of overlapping particles in granular product images using statistics and neural networks, Food Control 6 (1) (1995) 37-43.
260. R.G. Rosandich, Havnet - a new neural network architecture for pattern recognition, Neural Networks 10 (1) (1997) 139-151.
261. M.W. Roth, Survey of neural network technology for automatic target recognition, IEEE Transactions of Neural Networks 1 (1) (1990) 28-43.
262. M.W. Roth, Survey of neural network technology for automatic target recognition, IEEE Transactions on Neural Networks 1 (1) (1990) 28-43.
263. S. Rout, S.P. Srivastava, J. Majumdar, Multi-modal image segmentation using a modified Hopfield neural network, Pattern Recognition 31 (6) (1998) 743-50.
264. Y. Ruichek, J.-G. Postaire, A neural matching algorithm for 3-D reconstruction from stereo pairs of linear images, Pattern Recognition Letters 17 (4) (1996) 387-398.
265. J. Ruiz-del-Solar, M. Köppen, Sewage pipe image segmentation using a neural based architecture, Pattern Recognition Letters 17 (4) (1996) 363-368.
266. D.E. Rumelhart, G.E. Hinton, R.J. Williams, Learning internal representations by error propagation, in: vol. I, Parallel Distributed Processing: Explorations in the microstructure of Cognition, D.E. Rumelhart and J.L. McClelland, eds., 1986, MIT Press, Cambridge, pp. 319-362.
267. F. Russo, Hybrid neuro-fuzzy filter for impulse noise removal, Pattern Recognition 32 (11) (1999) 1843-1855.
268. F. Russo, Image filtering using evolutionary neural fuzzy systems, in: Soft computing for image processing, S.K. Pal, A. Ghosh, and M.K. Kundu, eds., 2000, Physica-Verlag, Heidelberg, pp. 23-43.
269. R. Rutledge, Injury severity and probability of survival assessment in trauma patients using a predictive hierarchical network model derived from ICD-9 codes, Journal of Trauma-Injury Infection and Critical Care 38 (4) (1995) 590-601.
270. B. Sahiner, H.P. Chan, N. Petrick et al., Classification of mass and normal breast tissue - a convolution neural network classifier with spatial domain and texture images, IEEE Transactions on Medical Imaging 15 (5) (1996) 598-610.
271. P. Sajda, C.D. Spence, S. Hsu et al., Integrating neural networks with image pyramids to learn target context, Neural Networks 8 (7-8) (1995) 1143-1152.
272. H. Sako, M. Whitehouse, A. Smith et al., Real-time facial-feature tracking based on matching techniques and its applications, Proc. 12th IAPR International Conference on Pattern Recognition, Jerusalem, 1994, pp. 320-324.
273. T. Sams, J.L. Hansen, Implications of physical symmetries in adaptive image classifiers, Neural Networks 13 (6) (2000) 565-570.
274. T. Sanger, Optimal unsupervised learning in a single layer feedforward neural network, Neural Networks 2 (7) (1989) 459-473.
275. J. Scharcanski, J.K. Hovis, H.C. Shen, Representing the color aspect of texture images, Pattern Recognition Letters 15 (2) (1994) 191-197.
276. A.J. Schofield, P.A. Mehta, T.J. Stonham, A system for counting people in video images using neural networks to identify the background scene, Pattern Recognition 29 (8) (1996) 1421-1428.
277. F.A. Schreiber, R.C. Wolfler, Use of neural networks to estimate the number of nodes of an edge quadtree, Graphical Models and Image Processing 59 (2) (1997) 61-72.
278. A. Schuler, P. Nachbar, J.A. Nossek et al., Learning state space trajectories in cellular neural networks, Proc. IEEE International Workshop on Cellular Neural Networks and their applications (CNNA '92), München, 1992, pp. 68-73.
279. T. Sejnowski, G.E. Hinton, Separating figure from ground with a Boltzmann machine, in: Vision, Brain and Cooperative Computation, M.A. Arbib and A.R. Hansen, eds., 1987, MIT Press, Cambridge, MA, pp. 703-724.
280. S.B. Serpico, L. Bruzzone, F. Roli, An experimental comparison of neural and statistical non-parametric algorithms for supervised classification of remote-sensing images, Pattern Recognition Letters 17 (13) (1996) 1331-1341.
281. J.-Y. Shen, Y.-X. Zhang, G.-G. Mu, Optical pattern recognition system based on a winner-take-all model of a neural network, Optical Engineering 32 (5) (1992) 1053-1056.
282. D. Shen, H.H.S. Ip, A Hopfield neural network for adaptive image segmentation: an active surface paradigm, Pattern Recognition Letters 18 (1) (1997) 37-48.
283. F.Y. Shih, J. Moh, F.-C. Chang, A new ART-based neural architecture for pattern classification and image enhancement without prior knowledge, Pattern Recognition 25 (5) (1992) 533-542.
284. A. Shustorovich, A subspace projection approach to feature extraction - the 2-D Gabor transform for character recognition, Neural Networks 7 (8) (1994) 1295-1301.
285. R.H. Silverman, A.S. Noetzel, Image processing and pattern recognition in ultrasonograms by backpropagation, Neural Networks 3 (5) (1990) 593-604.
286. R.H. Silverman, Segmentation of ultrasonic images with neural networks, International Journal of Pattern Recognition and Artificial Intelligence 5 (1991) 619-628.
287. P. Simard, Y. LeCun, J. Denker, Efficient pattern recognition using a new transformation distance, Proc. Advances in Neural Information Processing Systems, 1994, pp. 50-58.
288. W. Skarbek, A. Cichocki, Robust image association by recurrent neural subnetworks, Neural Processing Letters 3 (1996) 131-138.
289. J. Sklansky, M. Vriesenga, Genetic selection and neural modelling of piecewise-linear classifiers, International Journal of Pattern Recognition and Artificial Intelligence 10 (5) (1996) 587-612.
290. M. Sonka, V. Hlavac, R. Boyle, Image processing, Analysis, and Machine Vision, 2nd ed., PWS Publishing, Pacific Grove, 1999.
291. E.D. Sontag, Feedback stabilization using two-hidden-layer nets, IEEE Transactions on Neural Networks 3 (6) (1992) 981-990.
292. F.F. Soulie, E. Viennet, B. Lamy, Multi-modular neural network architectures for pattern recognition: applications in optical character recognition and human face recognition, International Journal of Pattern Recognition and Artificial Intelligence 7 (4) (1993) 487-498.
293. L. Spirkovska, M.B. Reid, Coarse-coded higher-order neural networks for PRSI object recognition, IEEE Transactions on Neural Networks 4 (2) (1993) 276-283.
294. L. Spirkovska, M.B. Reid, Higher-order neural networks applied to 2D and 3D object recognition, Machine Learning 15 (2) (1994) 169-199.
295. V. Srinivasan, Y.K. Han, S.H. Ong, Image reconstruction by a Hopfield neural network, Image and Vision Computing 11 (5) (1993) 278-282.
296. V. Srinivasan, P. Bhatia, S.H. Ong, Edge detection using a neural network, Pattern Recognition 27 (12) (1994) 1653-1662.
297. J. Stark, A neural network to compute the Hutchinson metric in fractal image processing, IEEE Transactions on Neural Networks 2 (1) (1991) 156-158.
298. A. Stassopoulou, M. Petrou, J. Kittler, Bayesian and neural networks for geographic information processing, Pattern Recognition Letters 17 (13) (1996) 1325-1330.
299. C. Strouthopoulos, N. Papamarkos, Text identification for document image analysis using a neural network, Image and Vision Computing 16 (12-13) (1998) 879-896.
300. P.N. Suganthan, E.K. Teoh, D.P. Mital, Pattern recognition by homomorphic graph matching using Hopfield neural networks, Image and Vision Computing 13 (1) (1995) 45-60.
301. P.N. Suganthan, E.K. Teoh, D.P. Mital, Pattern recognition by graph matching using the Potts MFT neural networks, Pattern Recognition 28 (7) (1995) 997-1009.
302. P.N. Suganthan, E.K. Teoh, D.P. Mital, Optimal mapping of graph homomorphism onto self organising hopfield network, Image and Vision Computing 15 (9) (1997) 679-694.
303. P.N. Suganthan, H. Yan, Recognition of handprinted Chinese characters by constrained graph matching, Image and Vision Computing 16 (3) (1998) 191-201.
304. M.B. Sukhaswami, A.K. Pujari, Restoration of geometrically aberrated images using a self- organising neural network, Pattern Recognition Letters 17 (1) (1996) 1-10.
305. L. Sukissian, S. Kollias, Y. Boutalis, Adaptive classification of textured images using linear prediction and neural networks, Signal Processing 36 (2) (1994) 209-232.
306. Y. Sun, S.-Y. Yu, An eliminating highest error IEHE criterion in Hopfield neural networks for bilevel image restoration, Pattern Recognition Letters 14 (6) (1993) 471-474.
307. Y.L. Sun, S. Yu, Improvement on performance of modified Hopfield neural network for image restoration, IEEE Transactions on Image Processing 4 (5) (1995) 683-692.
308. B. Takác, L. Sadovnik, Three-dimensional target recognition and tracking using neural networks trained on optimal views, Optical Engineering 37 (3) (1998) 819-828.
309. S. Tamura, H. Kawai, H. Mitsumoto, Male female identification from 8x6 very low resolution face images by neural network, Pattern Recognition 29 (2) (1996) 331-335.
310. H.W. Tang, V. Srinivasan, S.H. Ong, Invariant object recognition using a neural template classifier, Image and Vision Computing 14 (7) (1996) 473-483.
312. D.M.J. Tax, R.P.W. Duin, Support vector domain description, Pattern Recognition Letters 20 (11-13) (1999) 1191-1199.
313. S. Thirumalai, N. Ahuja, Parallel distributed detection of feature trajectories in multiple discontinuous motion image sequences, IEEE Transactions on Neural Networks 7 (3) (1996) 594-603.
314. A.B. Tickle, R. Andrews, M. Golea et al., The truth will come to light: directions and challenges in extracting the knowledge embedded within trained artificial neural networks, IEEE Transactions on Neural Networks 9 (6) (1998) 1057-1068.
315. A. Timmermans, Automatic sorting of pot plants with a neural network classifier, in: Neural networks: Artificial intelligence and industrial applications. Proceedings of the 3rd annual SNN symposium on neural networks, B. Kappen and S. Gielen, eds., 1995, Springer-Verlag, London.
316. D.L. Toulson, J.F. Boyce, Segmentation of MR images using neural nets, Image and Vision Computing 10 (5) (1992) 324 - 328.
317. C.-T. Tsai, Y.-N. Sun, P.-C. Chung et al., Endocardial boundary detection using a neural network, Pattern Recognition 26 (7) (1993) 1057-1068.
318. D.-M. Tsai, R.-Y. Tsai, Use neural networks to determine matching order for recognizing overlapping objects, Pattern Recognition Letters 17 (10) (1996) 1077-1088.
319. E.C.-K. Tsao, W.-C. Lin, C.-T. Chen, Constraint satisfaction neural networks for image recognition, Pattern Recognition 26 (4) (1993) 553-567.
320. Y.H. Tseng, J.N. Hwang, F.H. Sheehan, 3-D object representation and invariant recognition using continuous distance transform neural networks, IEEE Transactions on Neural Networks 8 (1) (1997) 141-147.
321. M. Turner, J. Austin, N.M. Allinson et al., Chromosome location and feature extraction using neural networks, Image and Vision Computing 11 (4) (1993) 235-239.
322. D. Tzovaras, M.G. Strintzis, Use of nonlinear principal component analysis and vector quantization for image coding, IEEE Transactions on Image Processing 7 (8) (1998) 1218-1223.
323. S. Usui, S. Nakauchi, M. Nakano, Internal color representation acquired by a five-layer neural network, Proc. International Conference on Artificial Neural Networks, Helsinki, Finland, 1991, pp. 867-872.
324. D. Valentin, H. Abdi, A.J. O'Toole et al., Connectionist models of face processing - a survey, Pattern Recognition 27 (9) (1994) 1209-1230.
325. M.M. van Hulle, T. Tollenaere, A modular artificial neural network for texture processing, Neural Networks 6 (1) (1993) 7-32.
326. V.N. Vapnik, Statistical learning theory, John Wiley & Sons, New York, 1998.
327. A. Verikas, K. Malmqvist, L. Bergman, Colour image segmentation by modular neural network, Pattern Recognition Letters 18 (2) (1997) 173-185.
328. D.L. Vilarino, V.M. Brea, D. Cabello et al., Discrete-time CNN for image segmentation by active contours, Pattern Recognition Letters 19 (8) (1998) 721-734.
329. V.V. Vinod, S. Chaudhury, J. Mukherjee et al., A connectionist approach for clustering with applications in image analysis, IEEE Transactions on Systems Man and Cybernetics 24 (3) (1994) 365-383.
330. J. Waldemark, An automated procedure for cluster analysis of multivariate satellite data, International Journal of Neural Systems 8 (1) (1997) 3-15.
331. W.G. Waller, A.K. Jain, On the monotonicity of the performance of a Bayesian classifier, IEEE Transactions on Information Theory 24 (3) (1978) 392-394.
332. T. Wang, X. Zhuang, X. Xing, Robust segmentation of noisy images using a neural network model, Image and Vision Computing 10 (4) (1992) 233-240.
333. J.Y. Wang, F.S. Cohen, 3-D Object recognition and shape estimation from image contours using b-splines, shape invariant matching, and neural network.2, IEEE Transactions on Pattern Analysis and Machine Intelligence 16 (1) (1994) 13-23.
334. Y.M. Wang, F.M. Wahl, Vector-entropy optimization-based neural-network approach to image reconstruction from projections, IEEE Transactions on Neural Networks 8 (5) (1997) 1008-1014.
335. D.L. Wang, D. Terman, Image segmentation based on oscillatory correlation, Neural Computation 9 (4) (1997) 805-836.
336. L.C. Wang, S.Z. Der, N.M. Nasrabadi, Automatic target recognition using a feature-decomposition and data-decomposition modular neural network, IEEE Transactions on Image Processing 7 (8) (1998) 1113-1121.
337. Y. Wang, T. Adali, S.Y. Kung et al., Quantification and segmentation of brain tissues from MR images - a probabilistic neural network approach, IEEE Transactions on Image Processing 7 (8) (1998) 1165-1181.
338. L.C. Wang, S.A. Rizvi, N.M. Nasrabadi, A modular neural network vector predictor for predictive image coding, IEEE Transactions on Image Processing 7 (8) (1998) 1198-1217.
339. L. Wang, S. Der, N. Nasrabadi, Composite classifiers for automatic target recognition, Optical Engineering 37 (3) (1998) 858-868.
340. A.M. Waxman, M.C. Seibert, A. Gove et al., Neural processing of targets in visible multispectral IR and SAR imagery, Neural Networks 8 (7-8) (1995) 1029-1051.
341. A.M. Waxman, A.N. Gove, D.A. Fay et al., Color night vision - opponent processing in the fusion of visible and ir imagery, Neural Networks 10 (1) (1997) 1-6.
342. A. Weingessel, H. Bischof, K. Hornik et al., Adaptive combination of PCA and VQ networks, IEEE Transactions on Neural Networks 8 (5) (1997) 1208-1211.
343. G. Wells, C. Venaille, C. Torras, Promising research: vision-based robot positioning using neural networks, Image and Vision Computing 14 (10) (1996) 715-732.
344. C.K.I. Williams, M. Revow, G.E. Hinton, Instantiating deformable models with a neural net, Computer Vision and Image Understanding 68 (1) (1997) 120-126.
345. C. Wohler, A time delay neural network algorithm for estimating image-pattern shape and motion, Image and Vision Computing 17 (3-4) (1999) 281-294.
346. J. Wood, Invariant pattern recognition: a review, Pattern Recognition 29 (1) (1996) 1-17.
347. A.J. Worth, D.N. Kennedy, Segmentation of magnetic resonance brain images using analogue constraint satisfaction neural networks, Image and Vision Computing 12 (6) (1994) 345-354.
348. Y.C. Wu, K. Doi, M.L. Giger et al., Reduction of false positives in computerized detection of lung nodules in chest radiographs using artificial neural networks, discriminant analysis, and a rule-based scheme, Journal of Digital Imaging 7 (4) (1994) 196-207.
349. L.Q. Xu, D.C. Hogg, Neural networks in human motion tracking - an experimental study, Image and Vision Computing 15 (8) (1997) 607-615.
350. H. Yan, J. Wu, Character and line extraction from color map images using a multi-layer neural network, Pattern Recognition Letters 15 (1) (1994) 97-103.
351. B. Yoshua, Y.L. Cun, Word normalization for on-line handwritten word recognition, Proc. 12th IAPR International Conference on Pattern Recognition, Jerusalem, 1994, pp. 409-413.
352. S.S. Young, P.D. Scott, N.M. Nasrabadi, Object recognition using multilayer Hopfield neural network, IEEE Transactions on Image Processing 6 (3) (1997) 357-372.
353. S.S. Young, P.D. Scott, C. Bandera, Foveal automatic target recognition using a multiresolution neural network, IEEE Transactions on Image Processing 7 (8) (1998) 1122-1135.
354. S.-S. Yu, W.-H. Tsai, Relaxation by the Hopfield neural network, Pattern Recognition 25 (2) (1992) 197-210.
355. M. Zamparelli, Genetically trained cellular neural networks, Neural Networks 10 (6) (1997) 1143-1151.
356. Z.Z. Zhang, N. Ansari, Structure and properties of generalized adaptive neural filters for signal enhancement, IEEE Transactions on Neural Networks 7 (4) (1996) 857-868.
357. Y.J. Zhang, A survey on evaluation methods for image segmentation, Pattern Recognition 29 (8) (1996) 1335-1346.
358. Y. Zheng, J.F. Greenleaf, J.J. Gisvold, Reduction of breast biopsies with a modified self-organizing map, IEEE Transactions on Neural Networks 8 (6) (1997) 1386-1396.
359. Y.T. Zhou, R. Chellapa, A. Vaid et al., Image restoration using a neural network, IEEE Transactions on Acoustics, Speech and Signal Processing 36 (7) (1988) 1141-1151.
360. Z.G. Zhu, S.Q. Yang, G.Y. Xu et al., Fast road classification and orientation estimation using omni-view images and neural networks, IEEE Transactions on Image Processing 7 (8) (1998) 1182-1197.
361. T. Ziemke, Radar image segmentation using recurrent artificial neural networks, Pattern Recognition Letters 17 (4) (1996) 319-334.

References: V. 
 V. 
 V. 
 V. 
 V. 
 V.