Skip to main content
Log in

Morphological Scale Spaces and Associative Morphological Memories: Results on Robustness and Practical Applications

  • Published:
Journal of Mathematical Imaging and Vision Aims and scope Submit manuscript

Abstract

Associative Morphological Memories are the analogous construct to Linear Associative Memories defined on the lattice algebra ℝ, +, ∨, ∧). They have excellent recall properties for noiseless patterns. However they suffer from the sensitivity to specific noise models, that can be characterized as erosive and dilative noise. To improve their robustness to general noise we propose a construction method that is based on the extrema point preservation of the Erosion/Dilation Morphological Scale Spaces. Here we report on their application to the tasks of face localization in grayscale images and appearance based visual self-localization of a mobile robot.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. A. Adorni, S. Cagnoni, S. Enderle, G.K. Kraetzschmar, M. Mordonini, M. Plagge, M. Ritter, S. Sablatnog, and A. Zell, “Vision-based localization for mobile robots,” Robotics and Autonomous Systems, Vol. 36, pp. 103–119, 2001.

    Google Scholar 

  2. C. Balkenius and L. Kopp, “Robust self-localization using elastic templates,” in Proc. Swedish Symp. on Image Analysis, T. Lindberg (Ed.), 1997.

  3. G.N. DeSouza and A.C. Kak, “Vision for mobile robot navigation: A survey,” IEEE Trans. on Patt. Anal. Mach. Int., Vol. 24, No. 2, pp. 237–267, 2002.

    Google Scholar 

  4. R. Féraud, O.J. Bernier, J.-E. Viallet, and M. Collobert, “A fast and accurate face detector based on neural networks,” IEEE Trans. on Patt. Anal. Mach. Int., Vol. 23, No. 1, pp. 42–53, 2001.

    Google Scholar 

  5. D. Fox, “Markov, localization: A probabilistic framework for mobile robot localization and navigation,” Ph.D. Thesis, University of Bonn, Germany, December 1998.

    Google Scholar 

  6. C.R. Giardina and E.R. Dougherty, Morphological Methods in Image and Signal Processing, Prentice Hall: Englewood Cliffs, NJ, 1988.

    Google Scholar 

  7. R.C. Gonzalez and R.E. Woods, Digital Image Processing, Addison-Wesley, Reading, MA, 1992.

    Google Scholar 

  8. M. Graña and B. Raducanu, “On the application of morphological heteroassociative neural networks,” in Proc. Int. Conf. on Image Processing (ICIP), I. Pitas (Ed.), Thessaloniki, Greece, October 2001, pp. 501–504.

  9. J.J. Hopfield, “Neural networks and physical systems with emergent collective computational abilities,” in Proc. Nat. Acad. Sciences, Vol. 79, pp. 2554–2558, 1982.

    Google Scholar 

  10. P.T. Jackway and M. Deriche, “Scale-space properties of the multiscale morphological dilation-erosion,” IEEE Trans. on Patt. Anal. and Mach. Int., Vol. 18, No. 1, pp. 38–51, 1996.

    Google Scholar 

  11. T. Kohonen, “Correlation matrix memory,” IEEE trans. Computers, Vol. 21, pp. 353–359, 1972.

    Google Scholar 

  12. S.H. Lin, S.Y. Kung, and L.J. Lin, “Face recognition/detection by probabilistic decision-based neural network,” IEEE Trans. on Neural Networks, Vol. 8, No. 1, pp. 114–132, 1997.

    Google Scholar 

  13. S. Livatino and C. Madsen, “Optimization of robot self-localization accuracy by automatic visual-landmark selection,” in Proc. of the 11th Scandinavian Conf. on Image Analysis (SCIA), 1999, pp. 501–506

  14. A.M. Martinez and J. Vitria, Clustering in image space for place recognition and visual annotations for human-robot interaction,” IEEE Trans. Sys. Man Cyb. B, Vol. 31, No. 5, pp. 669–682.

  15. C.F. Olson, “Mobile robot self-localization by iconic matching of range maps,” in Proc. of the 8th Int. Conf. on Advanced Robotics, 1997, pp. 447–452.

  16. B. Raducanu, M. Graña, and P. Sussner, “Morphological neural networks for vision based self-localization,” in Proc. of ICRA2001, Int. Conf. on Robotics and Automation, Seoul,Korea, May 2001, pp. 2059–2064.

  17. J. Reuter, “Mobile robot self-localization using PDAB,” in Proc. of Int. Conf. on Robotics and Automation (ICRA'2000), 2000.

  18. G.X. Ritter, J.L. Diaz-de-Leon, and P. Sussner, “Morphological bidirectional associative memories,” Neural Networks, Vol. 12, pp. 851–867, 1999.

    Google Scholar 

  19. G.X. Ritter, P. Sussner, and J.L. Diaz-de-Leon, “Morphological associative memories,” IEEE Trans. on Neural Networks, Vol. 9, No. 2, pp. 281–292, 1998.

    Google Scholar 

  20. G.X. Ritter, G. Urcid, and L. Iancu, “Reconstruction of patterns from moisy inputs using morphological associative memories,” J. Math. Imag. Vision, 2002 submitted.

  21. G.X. Ritter and J.N. Wilson, Handbook of Computer Vision Algorithms in Image Algebra, CRC Press: Boca Raton, FL.

  22. H.A. Rowley, S. Baluja, and T. Kanade, “Neural network-based face detection,” IEEE Trans. on Patt. Anal. and Mach. Int., Vol. 20, No. 1, pp. 23–38, 1998.

    Google Scholar 

  23. A. Saffiotti and L.P. Wesley, “Perception-based self-localization using fuzzy location,” in Lecture Notes in Artificial Intelligence 1093, L. Dorst, M. van Lambalgen, and F. Voorbraak (Eds.), Springer-Verlag, 1996, pp. 368–385.

  24. J. Serra, Image Analysis and Mathematical Morphology, Academic Press: London, 1982.

    Google Scholar 

  25. P. Soille, Morphological Image Analysis. Principles and Applications, Springer Verlag: Berlin, 1999.

    Google Scholar 

  26. K.K. Sung and T. Poggio, “Example-based learning for view-based human face detection,” IEEE Trans. on Patt. Anal. and Mach. Int., Vol. 20, No. 1, pp. 39–50, 1998.

    Google Scholar 

  27. P. Sussner, “Observations on morphological associative memories and the kernel method,” Neurocomputing, Vol. 31, pp. 167–183, 2000.

    Google Scholar 

  28. M. Turk and A. Pentland, “Eigenfaces for recognition,” Journal of Cognitive Neuroscience, Vol. 3, No. 1, pp. 71–86, 1991.

    Google Scholar 

  29. J.-G. Wangand E. Sung, “Frontal-view face detection and facial feature extraction using color and morphological operators,” Patt. Recog. Letters, Vol. 20, No. 10, pp. 1053–1068, 1999.

    Google Scholar 

  30. J. Wang and T. Tan, “A new face detection method based on shape information,” Patt. Recog. Letters, Vol. 21, Nos. 6/7, pp. 463–471, 2000.

    Google Scholar 

  31. Y. Won, P.D. Gader, and P.C. Coffield, “Morphological shared-weight neural network with applications to automatic target detection,” IEEE Trans. Neural Networks, Vol. 8, No. 5, pp. 1195–1203, 1997.

    Google Scholar 

  32. T.-W. Yoo and I.-S. Oh, “A fast algorithm for tracking human faces based on chromatic histograms,” Patt. Recog. Letters, Vol. 20, No. 10, pp. 967–978, 1999.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Raducanu, B., Graña, M. & Albizuri, F.X. Morphological Scale Spaces and Associative Morphological Memories: Results on Robustness and Practical Applications. Journal of Mathematical Imaging and Vision 19, 113–131 (2003). https://doi.org/10.1023/A:1024725414204

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1024725414204

Navigation