An efficient algorithm to update non-flat and incremental attributes in morphological trees

Autor(es)

Gobber, Charles F. ; Hashimoto, Ronaldo F. ; Alves, Wonder A. L.

• Tipo de documento: Artigo Científico
• Fonte: PATTERN RECOGNITION LETTERS; v. 163, p. 8-pg., 2022-10-01.

Resumo

Attribute Filters are powerful image simplification operators that have very good contour-preservation properties. They can be efficiently computed using morphological trees. The most common strategy to compute attribute filters using morphological trees is based on three steps: (i) tree construction: the tree is built along with its attributes; ( ii ) filtering: we simplify the tree by removing some of its nodes based on some attribute and a filtering rule defined on a threshold value; and ( iii ) image reconstruction: this is the final phase where the tree is converted back to an image leading to an attribute filter. However, after filtering a few attributes in tree may change (we call them as non-flat attributes) and they must be updated, when other attribute filters are applied to the simplified tree again, with either a different or the same threshold value. In this paper we present an efficient algorithm to update non-flat and incremental attributes in morphological trees with low memory consumption and fast computation.(c) 2022 Elsevier B.V. All rights reserved. (AU)

• Processo FAPESP: 15/22308-2 - Representações intermediárias em Ciência Computacional para descoberta de conhecimento
• Beneficiário: Roberto Marcondes Cesar Junior
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 18/15652-7 - Segmentação de imagens baseada em restrições de formas por meio dos últimos levelings
• Beneficiário: Wonder Alexandre Luz Alves
• Modalidade de apoio: Auxílio à Pesquisa - Regular