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Image segmentation based on dynamic trees and neural networks

Grant number: 19/11349-0
Support type:Scholarships in Brazil - Scientific Initiation
Effective date (Start): June 01, 2019
Effective date (End): September 14, 2020
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Principal Investigator:Alexandre Xavier Falcão
Grantee:Jordão Okuma Barbosa Ferraz Bragantini
Home Institution: Instituto de Computação (IC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Associated research grant:14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?, AP.TEM
Associated scholarship(s):19/21734-9 - Interactive Co-Segmentation with projection learning, BE.EP.IC


Recently, several areas of computing are being dominated by methods based on neural networks, which allow for incredible results, especially where data is non-structured, such as images, texts, and audios. However, most of these methodologies disregard previously developed techniques that were close to solving the problems given the needs of that period. Therefore, this project aims to develop techniques, in the context of images and videos, combining existing methodologies based on graphs and neural networks.

Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
BRAGANTINI, JORDAO; MOURA, BRUNO; FALCAO, ALEXANDRE X.; CAPPABIANCO, FABIO A. M. Grabber: A tool to improve convergence in interactive image segmentation. PATTERN RECOGNITION LETTERS, v. 140, p. 267-273, DEC 2020. Web of Science Citations: 0.

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