Generalized morphological components based on interval descriptors and n-ary aggregation functions

Autor(es)

Sussner, Peter ^[1] ; Caro Contreras, David Ernesto ^[1]

Afiliação do(s) autor(es)

^[1] Univ Estadual Campinas, Dept Appl Math, BR-13083859 Campinas, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: INFORMATION SCIENCES; v. 583, p. 14-32, JAN 2022.
• Citações Web of Science: 1

Resumo

Morphological perceptrons (MPs) can be characterized as feedforward morphological neural networks (MNNs) with applications in classification and regression. The neuronal aggregation functions of current MP versions are drawn from gray-scale mathematical morphology (MM) that can be described in terms of matrix products in a lattice algebra called minimax algebra. Specifically, MPs have components each of which computes a pair-wise infimum of an erosion and an anti-dilation that can be expressed in terms of products of matrices with entries in a complete l-group extension. In this paper, we use the novel concept of an interval descriptor and an n-ary aggregation function on a bounded poset in order to generalize existing gray-scale and fuzzy morphological components (MCs) of morphological and hybrid morphological/linear perceptrons (HMLPs). In addition, we present several other examples of generalized morphological components (GMCs) that can and will be incorporated as computational units into shallow and deep artificial neural networks. (c) 2021 Elsevier Inc. All rights reserved. (AU)

• Processo FAPESP: 18/13657-1 - Algumas abordagens de computação em reticulados a inteligência computacional, processamento e análise de imagens
• Beneficiário: Peter Sussner
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 20/09838-0 - BI0S - Brazilian Institute of Data Science
• Beneficiário: João Marcos Travassos Romano
• Modalidade de apoio: Auxílio à Pesquisa - Programa Centros de Pesquisa em Engenharia