Learning image-to-image transformations

• Grant number: 18/00477-5
• Support Opportunities: Scholarships in Brazil - Master
• Start date: October 01, 2018
• End date: August 31, 2020
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Agreement: Coordination of Improvement of Higher Education Personnel (CAPES)
• Principal Investigator: Nina Sumiko Tomita Hirata
• Grantee: Augusto César Monteiro Silva
• Host Institution: Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated research grant: 15/22308-2 - Intermediate representations in Computational Science for knowledge discovery, AP.TEM
• Associated scholarship(s): 19/07361-5 - Structural information in image-to-image transformation learning processes, BE.EP.MS

Abstract

Transforms or operators that map input images to output images can be applied to several areas of study, such as on the analysis of medical images or of document images. The manual creation of operators that perform desired transformations is a task that demands considerable time and effort. Thus, more recently, methods that seek automatic design of these operators and that are based on deep learning techniques are gaining attention. Among such approaches, some are local and seek learning of local characteristic functions that can be seen as individual pixel classifiers, while others are global and based on end-to end, i.e. image-to-image, training methods. Both have distinct advantages and disadvantages. The goal of this project is to study and compare the two approaches, and to create a new technique that joins the interesting features of the local and global approaches. The studied approaches will be applied on two real problems. One of the problems consists of vessel segmentation in retina images, with largely used public datasets, and the second is related to a collaboration with the Medical School of USP.

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)

MONTEIRO SILVA, AUGUSTO CESAR; HIRATA, NINA S. T.; JIANG, XIAOYI; IEEE COMP SOC. Skeletal Similarity based Structural Performance Evaluation for Document Binarization. 2020 17TH INTERNATIONAL CONFERENCE ON FRONTIERS IN HANDWRITING RECOGNITION (ICFHR 2020), v. N/A, p. 6-pg., 2020-01-01. (15/22308-2, 19/07361-5, 18/00477-5, 17/25835-9)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

SILVA, Augusto César Monteiro. Comparative analysis of image-to-image transformation learning approaches. 2020. Master's Dissertation - Universidade de São Paulo (USP). Instituto de Matemática e Estatística (IME/SBI) São Paulo.