MATEC Web Conf.
Volume 292, 201923rd International Conference on Circuits, Systems, Communications and Computers (CSCC 2019)
|Number of page(s)||9|
|Published online||24 September 2019|
- S. Roman, Lattices and Ordered Sets (Springer Verlag, New York, NY, 2008). [Google Scholar]
- G. Grätzer, Lattice Theory: Foundation (Birkhäuser-Springer Basel, Berlin, 2011). [Google Scholar]
- G. X. Ritter, P. Sussner, and J. L. Diaz de Leon, “Morphological associative memories,” IEEE Transactions on Neural Networks, 9 (2), 281–293 (1998). [CrossRef] [Google Scholar]
- R. Cuninghame-Green, “Minimax Algebra and Applications,” in Advances in Imaging and Electron Physics 90, P. Hawkes (Academic Press, New York, NY, 1995), 1–121. [Google Scholar]
- G. X. Ritter, “Lattice algebra” and “Minimax algebra,” in Image Algebra, unpublished manuscript available via anonymous ftp from https://www.cise.ufl.edu/Ȉjnw/CVAIIA/ (University of Florida, CCVV/CISE Department, Gainesville, FL, 1999) 121–135. [Google Scholar]
- G. X. Ritter and P. Gader, “Fixed Points of Lattice Transforms and Lattice Associative Memories,” in Advances in Imaging and Electron Physics 144, P. Hawkes (Elsevier, 2006), 165–242. [CrossRef] [Google Scholar]
- P. Sussner, “Observations on morphological associative memories and the kernel method,” Neurocomputing, 31(1)-(4), 167–183 (2000). [CrossRef] [Google Scholar]
- P. Sussner, “Associative morphological memories based on variations of the kernel and dual kernel methods,” Neural Networks, 16, 625–632 (2003). Jul. 2003. [CrossRef] [Google Scholar]
- G. X. Ritter, G. Urcid, and L. Iancu, “Reconstruction of noisy patterns using morphological associative memories,” Journal of Mathematical Imaging and Vision, 19 (5), 95–111 (2003). [CrossRef] [Google Scholar]
- G. Urcid, “Kernel computation in morphological associative memories for grayscale image recollection,” IASTED Proceedings 5th International Conference on Signal and Image Processing, 450–455 (2003). [Google Scholar]
- P. Sussner, “Recall of patterns using binary and gray-scale autoassociative morphological memories,” Proceedings of SPIE: Mathematical Methods in Pattern and Image Analysis, 5916, 59160M:1–10 (2005). [Google Scholar]
- G. Urcid and G. X. Ritter, “Noise masking for pattern recall using a single lattice matrix auto-associative memory,” Proceedings 2006 IEEE International Conference on Fuzzy Systems, 187–194 (2006). [CrossRef] [Google Scholar]
- G. Urcid and G. X. Ritter, Noise Masking for Pattern Recall using a Single Lattice Matrix Associative Memory,” in Studies in Computational Intelligence, 67 (Springer, Berlin, 2007), 81–100. [Google Scholar]
- G. Urcid, J.A. Nieves-V., A. Garcia-A., and J.C. Valdiviezo-N., “Robust image retrieval from noisy inputs using lattice associative memories,” Proceedings of SPIE-IS&T Electronic Imaging: Image Processing, Algorithms and Systems VII, 7245, 724517: 1–12 (2009). [Google Scholar]
- M. E. Valle, “An introduction to the max-plus projection autoassociative morphological memory and some of its variations,” Proceedings 2014 IEEE International Conference on Fuzzy Systems, 53–60 (2014). [CrossRef] [Google Scholar]
- A. S. Santos and M. E. Valle, “A fast and robust max C projection fuzzy autoassociative memory with application for face recognition,” IEEE Proceedings 2017 Brasilian Conference on Intelligent Systems, 306–311 (2017). [Google Scholar]
- A. S. Santos and M. E. Valle, “Max C and Min D projection autoassociative fuzzy morphological memories: theory and applications for face recognition,” arXiv:1902.04144v (Preprint submitted to Elsevier), 1–33 (2019). [Google Scholar]
- Y. Wang, “Model Selection,” Ch. 16 in Handbook of Computational Statistics: Concepts and Methods, 2nd Ed., J. E. Gentle, W. K. Härdle, Y. Mori (Springer Berlin, Heidelberg 2012), 469–497. [CrossRef] [Google Scholar]
- M. Wang and S. Chen, “Enhanced fuzzy morphological auto-associative memory based on empirical kernel map,” IEEE Transactions on Neural Networks, 16 (3), 557–564 (2005). [CrossRef] [Google Scholar]
- P. Sussner and M.E. Valle, “Gray-scale morphological associative memories,” IEEE Transactions on Neural Networks, 17 (3), 559–570 (2006). [CrossRef] [Google Scholar]
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