Please use this identifier to cite or link to this item: http://www.repositorio.ufc.br/handle/riufc/12554
Title in Portuguese: Machine learning and adaptive morphological operators
Author: Almeida Filho, Magno Prudêncio de
Silva, Francisco de Assis Tavares Ferreira da
Braga, Arthur Plínio de Souza
Keywords: Reconhecimento de padrões
Morfologia matemática
Engenharia elétrica
Issue Date: 2014
Publisher: Encontro Nacional de Inteligência Artificial e Computacional
Citation: ALMEIDA FILHO, M. P. ; SILVA, F. A. T. F. ; BRAGA, A. P. S. Machine learning and adaptive morphological operators. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL, 11., 2014, São Carlos. Anais... São Carlos: ENIAC, 2014.
Abstract: This work proposes the use of machine learning methods applied to the construction of a gray level adaptive hit-or-miss morphological operator. Because they are adaptive and translation invariant, it is expected that these operators can be better utilized for the process of pattern recognition. In a first approach, the investigated adaptive model is inspired on the Vector Quantization Unsupervised Learn Rule and developed through Elementary Look-Up Tables (ELUTs) formalism of elementary morphological operators in gray level images.
URI: http://www.repositorio.ufc.br/handle/riufc/12554
metadata.dc.type: Artigo de Periódico
Appears in Collections:DEEL - Trabalhos apresentados em eventos

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