Constructive morphological neural networks: Some theoretical aspects and experimental results in classification

Studies in Computational Intelligence

Volumn 258, Issue , 2009, Pages 123-144

  Sussner, Peter ^a   Esmi, Estevão Laureano ^a  

^a UNIVERSITY OF CAMPINAS   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 74049104501     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-04512-7_7     Document Type: Conference Paper

References (58)

1. Aráujo, R.A.,Madeiro, R., Sousa, R.P., Pessoa, L.F.C., Ferreira, T.A.E.: An evolutionary morphological approach for financial time series forecasting. In: Proceedings of the IEEE Congress on Evolutionary Computation, Vancouver, Canada, pp. 2467-2474 (2006)
2. Armstrong, W.W., Thomas, M.M.: Adaptive Logic Networks. In: Fiesler, E., Beale, R. (eds.) Handbook of Neural Computation, vol.C1.8, pp. 1-14. IOP Publishing, Oxford University Press, Oxford United Kingdom (1997)
3. Aráujo, R.A., Madeiro, F., Pessoa, L.F.C.:Modular morphological neural network training via adaptive genetic algorithm for design translation invariant operators. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Toulouse, France (May 2006)
4. Banon, G., Barrera, J.: Decomposition ofMappings between Complete Lattices by Mathematical Morphology, Part 1, General Lattices. Signal Processing 30(3), 299-327 (1993)
5. Birkhoff, G.: Lattice Theory, 3rd edn. American Mathematical Society, Providence (1993)
6. Asuncion, A., Newman, D.J.: UCI Repository of machine learning databases, University of California, Irvine, CA, School of Information and Computer Sciences (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
7. Braga-Neto, U., Goutsias, J.: Supremal multiscale signal analysis. SIAM Journal of Mathematical Analysis 36(1), 94-120 (2004)
8. Carpenter, G.A., Grossberg, S.: ART 3: Hierarchical search using chemical transmitters in self-organizing pattern recognition architectures. In: Neural Networks, vol.3, pp. 129-152 (1990)
9. Carŕe, B.: An algebra for network routing problems J. Inst. Math. Appl. 7, 273-294 (1971)
10. Coskun, N., Yildirim, T.: The effects of training algorithms in MLP network on image classification. Neural Networks 2, 1223-1226 (2003)
11. Cuninghame-Green, R.: Minimax Algebra and Applications. In: Hawkes, P. (ed.) Advances in Imaging and Electron Physics, vol.90, pp. 1-121. Academic Press, New York (1995)
12. Cuninghame-Green, R., Meijer, P.F.J.: An Algebra for Piecewise-Linear Minimax Problems. Discrete Applied Mathematics 2(4), 267-286 (1980)
13. Cuninghame-Green, R.: Minimax Algebra: Lecture Notes in Economics and Mathematical Systems, vol.166. Springer, New York (1979)
14. Davey, B.A., Priestley, H.A.: Introduction to lattices and order. Cambridge University Press, Cambridge (2002)
15. Gader, P.D., Khabou, M., Koldobsky, A.: Morphological Regularization Neural Networks. Pattern Recognition, Special Issue on Mathematical Morphology and Its Applications 33(6), 935-945 (2000)
16. Giffler, B.: Mathematical solution of production planning and scheduling problems. IBM ASDD, Tech. Rep (1960)
17. Graña, M., Gallego, J., Torrealdea, F.J., D'Anjou, A.: On the application of associative morphological memories to hyperspectral image analysis. In: Mira, J., Á lvarez, J.R. (eds.) IWANN 2003. LNCS, vol.2687, pp. 567-574. Springer, Heidelberg (2003)
18. Grätzer, G.A.: Lattice theory: first concepts and distributive lattices.W. H. Freeman, San Francisco (1971)
19. Haralick, R.M., Shapiro, L.G.: Computer and Robot Vision, vol.1. Addison-Wesley, New York (1992)
20. Haralick, R.M., Sternberg, S.R., Zhuang, X.: Image Analysis Using Mathematical Morphology: Part I. IEEE Transactions on Pattern Analysis and Machine Intelligence 9(4), 532-550 (1987)
21. Heijmans, H.: Morphological Image Operators. Academic Press, New York (1994)
22. Hocaoglu, A.K., Gader, P.D.: Domain learning using Choquet integral-based morphological shared weight neural networks. Image and Vision Computing 21(7), 663-673 (2003)
23. Kaburlasos, V.G., Athanasiadis, I.N., Mitkas, P.A.: Fuzzy lattice reasoning (FLR) classifier and its application for ambient ozone estimation. International Journal of aproximate reasoning 45(1), 152-188 (2003)
24. Kaburlasos, V.G., Petridis, V.: Fuzzy lattice neurocomputing (FLN) models. Neural Networks 13, 1145-1170 (2000)
25. Khabou, M.A., Gader, P.D.: Automatic target detection using entropy optimized sharedweight neural networks. IEEE Transactions on Neural Networks 11(1), 186-193 (2000)
26. Kim, C.: Segmenting a low-depth-of-field image using morphological filters and region merging. IEEE Transactions on Image Processing 14(10), 1503-1511 (2005)
27. Kwok, J.T.:Moderating the Outputs of Support Vector Machine Classifiers. IEEE Transactions on Neural Networks 10(5), 1018-1031 (1999)
28. Matheron, G., Random Sets and Integral Geometry. Wiley, New York, (1975).
29. Monteiro, A.S., Sussner, P.: A Brief Review and Comparison of Feedforward Morphological Neural Networks with Applications to Classification. In: K°urkov́a, V., Neruda, R., Koutńik, J. (eds.) ICANN 2008, Part II. LNCS, vol.5164, pp. 783-792. Springer, Heidelberg (2008)
30. Pessoa, L.F.C., Maragos, P.: Neural networks with hybrid morphological/rank/linear nodes: a unifying framework with applications to handwritten character recognition. Pattern Recognition 33, 945-960 (2000)
31. Petridis, V., Kaburlasos, V.G.: Fuzzy lattice neural network (FLNN): a hybrid model for learning. IEEE Transactions on Neural Networks 9(5), 877-890 (1998)
32. Pitas, I., Venetsanopoulos, A.N.: Morphological Shape Decomposition. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(1), 38-45 (1990)
33. Porter, R., Harvey, N., Perkins, S., Theiler, J., Brumby, S., Bloch, J., Gokhale, M., Szymanski, J.: Optimizing digital hardware perceptrons for multispectral image classification. Journal of Mathematical Imaging and Vision 19(2), 133-150 (2003)
34. Raducanu, B., Graña, M., Albizuri, X.F.: Morphological Scale Spaces and Associative Morphological Memories: Results on Robustness and Practical Applications. Journal of Mathematical Imaging and Vision 19(2), 113-131 (2003)
35. Ripley, B.D.: Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge (1996)
36. Ripley, B.D.: Datasets for Pattern Recognition and Neural Networks, Cambridge, United Kingdom (1996), http://www.stats.ox.ac.uk/pub/PRNN/
37. Ritter, G.X., Iancu, L., Urcid, G.:Morphological perceptrons with dendritic structure. In: The 12th IEEE International Conference, Fuzzy Systems, May 2003, vol.2, pp. 1296-1301 (2003)
38. Ritter, G.X., Urcid, G.: Lattice Algebra Approach to Single-Neuron Computation. IEEE Transactions on Neural Networks 14(2), 282-295 (2003)
39. Ritter, G.X.,Wilson, J.N.: Handbook of Computer Vision Algorithms in Image Algebra, 2nd edn. CRC Press, Boca Raton (2001)
40. Ritter, G.X., Sussner, P.: Morphological Perceptrons Intelligent Systems and Semiotics, Gaithersburg, Maryland (1997)
41. Ritter, G.X., Wilson, J.N., Davidson, J.L.: Image Algebra: An Overview. Computer Vision, Graphics, and Image Processing 49(3), 297-331 (1990)
42. Ronse, C.: Why Mathematical Morphology Needs Complete Lattices. Signal Processing 21(2), 129-154 (1990)
43. Segev, I.: Dendritic processing. In: Arbib, M.A. (ed.) The handbook of brain theory and neural networks, MIT Press, Cambridge (1998)
44. Serra, J.: Image Analysis and Mathematical Morphology. In: Theoretical Advances, vol.2, Academic Press, New York (1998)
45. Serra, J.: Image Analysis and Mathematical Morphology. Academic Press, London (1982)
46. Khabou, M.A., Gader, P.D., Keller, J.M.: LADAR target detection using morphological shared-weight neural networks.Machine Vision and Applications 11(6), 300-305 (2000)
47. Sobania, A., Evans, J.P.O.: Morphological corner detector using paired triangular structuring elements. Pattern Recognition 38(7), 1087-1098 (2005)
48. Soille, P.: Morphological Image Analysis. Springer, Berlin (1999)
49. Sousa, R.P., Pessoa, L.F.C., Carvalho, J.M.: Designing translation invariant operations via neural network training. In: Proceedings of the IEE International Conference on Image Processing, Vancouver, Canada, pp. 908-911 (2000)
50. Sussner, P., Valle, M.E.: Morphological and Certain Fuzzy Morphological Associative Memories with Applications in Classification and Prediction. In: Kaburlasos, V.G., Ritter, G.X. (eds.) Computational Intelligence Based on Lattice Theory. Studies in Computational Intelligence, vol.67, pp. 149-173. Springer, Heidelberg (2007)
51. Sussner, P., Valle, M.E.: Gray Scale Morphological Associative Memories. IEEE Transactions on Neural Networks 17(3), 559-570 (2006)
52. Sussner, P., Valle,M.E.: Implicative Fuzzy Associative Memories. IEEE Transactions on Fuzzy Systems 14(6), 793-807 (2006)
53. Sussner, P., Grana, M.: Guest Editorial: Special Issue on Morphological Neural Networks. Journal of Mathematical Imaging and Vision 19(2), 79-80 (2003)
54. Sussner, P.: Morphological Perceptron Learning. In: Proceedings of IEEE ISIC/CIRA/ISAS Joint Conference, Gaithersburg, MD, September 1998, pp. 477-482 (1998)
55. Valle, M.E., Sussner, P.: A General Framework for Fuzzy Morphological Associative Memories. Fuzzy Sets and Systems 159(7), 747-768 (2008)
56. Zadeh, L.A.: Fuzzy Sets. Information and Control 8(3), 338-353 (1965)
57. Zimmermann, U.: Linear amd Combinatorial Optimization of Ordered Algebraic Structures. North-Holland, Amsterdam (1981)
58. Yu, A.J., Giese, M.A., Poggio, T.: Biophysiologically Plausible Implementations of the Maximum Operation. Neural Computation 14(12), 2857-2881 (2002)