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Methods of computational intelligence and image processing based on mathematical morphology and lattice algebra


Lattice theory represents the appropriate theoretical structure to conduct mathematical morphology in a general setting. In the last few years, various researchers have employed concepts of mathematical morphology and lattice theory to develop approaches towards computational intelligence. With the advent of granular computing, the role of computational intelligence approaches based on lattice theory should increase even more since many classes of information granules constitute lattices.In this project, we intend to perform research regarding the following issues: 1. Advances in mathematical morphology in complete lattices; 2. Extensions of fuzzy mathematical morphology with applications in image processing; 3. Fuzzy morphological associative memories (FMAMs) and fuzzy relational equations with applications in prediction; 4. Extensions of FMAMs and applications in granular computing; 5. Extensions and improvements of other models of morphological neural networks such as the "Kosko-Subsethood Fuzzy Associative Memory" (KS-FAM) and the "Morphological Perceptron with Competitive Learning" (MP/CL). (AU)

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
ESMI, ESTEVAO; SUSSNER, PETER; BUSTINCE, HUMBERTO; FERNANDEZ, JAVIER. Theta-Fuzzy Associative Memories (Theta-FAMs). IEEE TRANSACTIONS ON FUZZY SYSTEMS, v. 23, n. 2, p. 313-326, APR 2015. Web of Science Citations: 11.
VALLE, MARCOS EDUARDO; SUSSNER, PETER. Quantale-based autoassociative memories with an application to the storage of color images. PATTERN RECOGNITION LETTERS, v. 34, n. 14, SI, p. 1589-1601, OCT 15 2013. Web of Science Citations: 12.

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