Extensions of Hopfield Neural Networks for Solving of Stereo-Matching Problem

1. Abe, S.: Global convergence and suppression of spurious states of the hopfield neural networks. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications 40(4), 246–257 (1993)

   Article  MathSciNet  Google Scholar 

2. Al-askar, H., Lamb, D., Hussain, A.J., Al-Jumeily, D., Randles, M., Fergus, P.: Predicting financial time series data using artificial immune system-inspired neural networks. Journal of Artifcial Intelligence and Soft Computing Research 5(1), 45–68 (2015)

   Article  Google Scholar 

3. Alvarez, L., Weickert, J., Sanchez, J., Deriche, R.: Dense disparity map estimation respecting image discontinuities: a PDE and scale-space based approach. Technical Report RR-INRIA/RR-3874-FR+ENG-3874, INRIA, Rocquencourt (2000)

   Google Scholar 

4. Anand, K., Raman, S., Subramanian, K.: Implementing a neuro fuzzy expert system for optimising the performance of chemical recovery boiler. Journal of Artifcial Intelligence and Soft Computing Research 4(2/3), 249–263 (2014)

   Article  Google Scholar 

5. Bali, S., Jha, D., Kumar, D., Pham, H.: Fuzzy multi-objective build-or-buy approach for component selection of fault tolerant software system under consensus recovery block scheme with mandatory redundancy in critical modules. Journal of Artifcial Intelligence and Soft Computing Research 4(2/3), 98–119 (2014)

   Article  Google Scholar 

6. Cierniak, R.: New neural network algorithm for image reconstruction from fan-beam projections. Neurocomputing 72(13-15), 3238–3244 (2009), Hybrid Learning Machines (HAIS 2007)/Recent Developments in Natural Computation (ICNC 2007)

   Google Scholar 

7. Cpalka, K.: A new method for design and reduction of neuro fuzzy classification systems. IEEE Transactions on Neural Networks 20(4), 701–714 (2009)

   Article  Google Scholar 

8. Cpalka, K., Rutkowski, L.: Flexible takagi sugeno neuro fuzzy structures for nonlinear approximation. WSEAS Transactions on Systems 5, 1450–1458 (2005)

   MATH  Google Scholar 

9. Das, P., Pettersson, F., Dutta, S.: Pruned-bimodular neural networks for modelling of strength-ductility balance of hsla steel plates. Journal of Artifcial Intelligence and Soft Computing Research 4(4), 354–372 (2014)

   Article  Google Scholar 

10. Dunk, A., Haffegee, A., Alexandrov, V.N.: Selection methods for interactive creation and management of objects in 3d immersive environments. In: Sloot, P., van Albada, D., Dongarra, J. (eds.) ICCS. Procedia Computer Science, vol. 1, pp. 2609–2617. Elsevier (2010)

    Google Scholar 

11. El-Laithy, K., Bogdan, M.: Synchrony state generation: an approach using stochastic synapses. Journal of Artificial Intelligence and Soft Computing Research 1, 17–25 (2011)

    Google Scholar 

12. Fleet, D., Jepson, A.: Computation of component image velocity from local phase information. International Journal of Computer Vision 5(1), 77–104 (1990)

    Article  Google Scholar 

13. Gabryel, M., Korytkowski, M., Scherer, R., Rutkowski, L.: Object detection by simple fuzzy classifiers generated by boosting. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part I. LNCS, vol. 7894, pp. 540–547. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

14. Grimson, E.: Computational experiments with a feature based stereo algorithm (1984)

    Google Scholar 

15. Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press (2004) ISBN: 0521540518

    Google Scholar 

16. Hopfield, J., Feinstein, D., Palmer, R.: Unlearning has a stabilizing effect in collective memories. Nature 304, 158–159 (1983)

    Article  Google Scholar 

17. Hopfield, J., Tank, D.: Neural computation of decisions in optimization problems. Biological Cybernetics 52(3), 141–152 (1985)

    MathSciNet  MATH  Google Scholar 

18. Hopfield, J., Tank, D.: Computing with neural circuits: A model. Science 233, 624–633 (1986)

    Article  Google Scholar 

19. Katiyar, R., Pathak, V.K., Arya, K.: Human gait recognition system based on shadow free silhouettes using truncated singular value decomposition transformation model. Journal of Artifcial Intelligence and Soft Computing Research 4(4), 283–301 (2014)

    Article  Google Scholar 

20. Korytkowski, M., Nowicki, R., Rutkowski, L., Scherer, R.: AdaBoost ensemble of DCOG rough–neuro–fuzzy systems. In: Jędrzejowicz, P., Nguyen, N.T., Hoang, K. (eds.) ICCCI 2011, Part I. LNCS, vol. 6922, pp. 62–71. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

21. Korytkowski, M., Rutkowski, L., Scherer, R.: From ensemble of fuzzy classifiers to single fuzzy rule base classifier. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 265–272. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

22. Laskowski, Ł.: Hybrid-maximum neural network for depth analysis from stereo-image. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010, Part II. LNCS, vol. 6114, pp. 47–55. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

23. Laskowski, Ł.: Objects auto-selection from stereo-images realised by self-correcting neural network. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part I. LNCS, vol. 7267, pp. 119–125. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

24. Laskowski, L.: A novel hybrid-maximum neural network in stereo-matching process. Neural Computing and Applications 23(7-8), 2435–2450 (2013)

    Article  Google Scholar 

25. Laskowski, L., Jelonkiewicz, J.: Self-correcting neural network for stereo-matching problem solving. Fundamenta Informaticae 138, 1–26 (2015)

    Article  MathSciNet  Google Scholar 

26. Lee, J.J., Shim, J.C., Ha, Y.H.: Stereo correspondence using the hopfield neural network of a new energy function. Pattern Recognition 27(11), 1513–1522 (1994)

    Article  Google Scholar 

27. Lee, S., Park, J.: Dual-mode dynamics neural network for combinatorial optimization. Neurocomputing 8(3), 283–304 (1995), Optimization and Combinatorics, Part I-III

    Google Scholar 

28. Nishihara, H.K.: Readings in computer vision: Issues, problems, principles, and paradigms, pp. 63–72. Morgan Kaufmann Publishers Inc., San Francisco (1987)

    Google Scholar 

29. Nowicki, R., Pokropińska, A.: Information criterions applied to neuro-fuzzy architectures design. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 332–337. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

30. Nowicki, R., Rutkowska, D.: Neuro–fuzzy systems based on Gödel and Sharp implication. In: Proceedings of Intern. Conference Application of Fuzzy Systems and Soft Computing — ICAFS 2000, Siegen, Germany, pp. 232–237 (June 2000)

    Google Scholar 

31. Nowicki, R., Scherer, R., Rutkowski, L.: A method for learning of hierarchical fuzzy systems. In: Sincak, P., et al. (eds.) Intelligent Technologies – Theory and Applications, pp. 124–129. IOS Press, Amsterdam (2002)

    Google Scholar 

32. Pajares, G., Cruz, J., Aranda, J.: Relaxation by hopfield network in stereo image matching. Pattern Recognition 31(5), 561–574 (1998)

    Article  Google Scholar 

33. Paragios, N., Deriche, R.: Geodesic active regions and level set methods for motion estimation and tracking. Computer Vision and Image Understanding 97(3), 259–282 (2005)

    Article  Google Scholar 

34. Redi, J., Gastaldo, P., Zunino, R.: A two-layer neural system for reduced-reference visual quality assessment. Journal of Artificial Intelligence and Soft Computing Research 1, 27–41 (2011)

    Google Scholar 

35. Rutkowski, L., Przybył, A., Cpałka, K., Er, M.J.: Online speed profile generation for industrial machine tool based on neuro-fuzzy approach. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010, Part II. LNCS (LNAI), vol. 6114, pp. 645–650. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

36. Rygał, J., Najgebauer, P., Nowak, T., Romanowski, J., Gabryel, M., Scherer, R.: Properties and structure of fast text search engine in context of semantic image analysis. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part I. LNCS, vol. 7267, pp. 592–599. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

37. Scherer, R.: Neuro-fuzzy relational systems for nonlinear approximation and prediction. Nonlinear Analysis 71, e1420–e1425 (2009)

    Google Scholar 

38. Scherer, R., Rutkowski, L.: A fuzzy relational system with linguistic antecedent certainty factors. In: Rutkowski, Kacprzyk (eds.) Proceedings of the Sixth International Conference on Neural Network and Soft Computing, Zakopane, Poland, June 11-15, 2002. Advances in Soft Computing, pp. 563–569. Springer, Physica-Verlag (2003)

    Google Scholar 

39. Scherer, R., Rutkowski, L.: Neuro-fuzzy relational classifiers. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 376–380. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

40. Singh, M., Hassan, M.: Hierarchical optimisation for non-linear dynamical systems with non-separable cost functions. Automatica 14(1), 99–101 (1978)

    Article  Google Scholar 

41. Starczewski, J., Scherer, R., Korytkowski, M., Nowicki, R.: Modular type-2 neuro-fuzzy systems. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Wasniewski, J. (eds.) PPAM 2007. LNCS, vol. 4967, pp. 570–578. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

42. Sun, C.: Fast algorithms for stereo matching and motion estimation. In: Australia-Japan Advanced Workshop on Computer Vision, September 9-11 (2003)

    Google Scholar 

43. Sun, C., Jones, R., Talbot, H., Wu, X., Cheong, K., Beare, R., Buckley, M., Berman, M.: Measuring the distance of vegetation from powerlines using stereo vision. ISPRS Journal of Photogrammetry and Remote Sensing 60(4), 269–283 (2006)

    Article  Google Scholar 

44. Takefuji, Y., Lee, K.C., Aiso, H.: An artificial maximum neural network: a winner-take-all neuron model forcing the state of the system in a solution domain. Biological Cybernetics 67(3), 243–251 (1992)

    Article  Google Scholar 

Download references