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

Author(s)

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

• Document type: Journal article
• Source: PATTERN RECOGNITION LETTERS; v. 163, p. 8-pg., 2022-10-01.

Abstract

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)

• FAPESP's process: 15/22308-2 - Intermediate representations in Computational Science for knowledge discovery
• Grantee: Roberto Marcondes Cesar Junior
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 18/15652-7 - Image segmentation based on shape constraints through the ultimate levelings
• Grantee: Wonder Alexandre Luz Alves
• Support Opportunities: Regular Research Grants