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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Incremental bit-quads count in component trees: Theory, algorithms, and optimization

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Silva, Dennis J. [1] ; Alves, Wonder A. L. [2] ; Hashimoto, Ronaldo Fumio [1]
Total Authors: 3
[1] Univ Sao Paulo, Inst Matemat & Estat, Rua Matao, BR-1010 Sao Paulo, SP - Brazil
[2] Univ Nove Julho, Informat & Knowledge Management Grad Program, Sao Paulo, SP - Brazil
Total Affiliations: 2
Document type: Journal article
Source: PATTERN RECOGNITION LETTERS; v. 129, p. 33-40, JAN 2020.
Web of Science Citations: 0

Component tree is a full image representation which encodes all connected components from upper (resp. lower) level sets of a given image through the inclusion relation. Information from this representation can be used in many image processing and computational vision applications, e.g. connected filtering, image segmentation, feature extraction, among others. In general, each node of a component tree represents a connected component of a level set and stores attributes which describes features of this connected component. This paper presents a review of a previously published method to compute attributes such as area, perimeter, and number of Euler by incrementally counting patterns while traversing nodes of a component tree. This method foundation is further detailed in this paper by presenting a novel theoretical background and algorithm correctness intuition. We also present a novel approach for this algorithm showing improvements for run-time execution and precision analysis. (C) 2019 Elsevier B.V. All rights reserved. (AU)

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
FAPESP's process: 15/01587-0 - Storage, modeling and analysis of dynamical systems for e-Science applications
Grantee:João Eduardo Ferreira
Support Opportunities: Research Grants - eScience and Data Science Program - Thematic Grants