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Interactive Co-Segmentation with projection learning

Grant number: 19/21734-9
Support type:Scholarships abroad - Research Internship - Scientific Initiation
Effective date (Start): December 01, 2019
Effective date (End): March 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
Supervisor abroad: Laurent Najman
Home Institution: Instituto de Computação (IC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Local de pesquisa : École Supérieure d'Ingénieurs en Électrotechnique et Électronique (ESIEE), France  
Associated to the scholarship:19/11349-0 - Image segmentation based on dynamic trees and neural networks, BP.IC

Abstract

Image segmentation is one of the most studied topics in the computer visioncommunity today. In this context, weakly supervised methods are starting togain attention because of the vast amount of labeled data necessary for al-gorithms with standard training procedure. Another approach to tackle thisproblem is to improve the existing methods for image annotation, such as in-teractive image segmentation methods, facilitating data annotation. Therefore,in this project, the goal is to combine the existing graph-based image segmen-tation methods, researched on the student previous project, FAPESP Project2018/08951-8, with broader modeling techniques to perform multiple imagessegmentation with minimal interaction, while enabling user interaction outsideof the image domain, in this case, in the projection space.