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Image segmentation based on dynamic trees and neural networks

Grant number: 19/11349-0
Support Opportunities:Scholarships in Brazil - Scientific Initiation
Start date: June 01, 2019
End date: 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
Host 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

Abstract

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.

News published in Agência FAPESP Newsletter about the scholarship:
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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; FALCAO, ALEXANDRE X.; NAJMAN, LAURENT. Rethinking interactive image segmentation: Feature space annotation. PATTERN RECOGNITION, v. 131, p. 12-pg., . (19/21734-9, 14/12236-1, 19/11349-0)
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, . (14/12236-1, 19/11349-0, 16/21591-5)