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Morphological filtering is a very important branch of Mathematical Morphology, where theoretical advances are made and new useful practical applications are discovered at a fast pace. On the other hand, adaptive-neighborhood techniques have been utilized with success for some time in the image processing field. In this work we propose the extension of a well-known class of morphological filters, the Alternating Sequential Filters (ASFs), to include the paradigm of adaptive-neighborhood image processing, leading to what we have called the Adaptive Alternating Sequential Filters (AASFs). By using synthetic and real images to which Gaussian noise was added, we demonstrate the better performance of the open-close and close-open AASFs against the correspondent ASFs, both from a quantitative and qualitative point of view.
Síntese de Imagens
Reconhecimento de Padrões
Visualização de Dados
Visão por Computador
Aplicações em Medicina
Modelagem e Visualização
Animação e Multimídia
Processamento de Imagens
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