Structural information in image-to-image transformation learning processes

• Grant number: 19/07361-5
• Support Opportunities: Scholarships abroad - Research Internship - Master's degree
• Start date: August 01, 2019
• End date: January 31, 2020
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Nina Sumiko Tomita Hirata
• Grantee: Augusto César Monteiro Silva
• Supervisor: Xiaoyi Jiang
• Host Institution: Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Institution abroad: University of Munster, Germany
• Associated to the scholarship: 18/00477-5 - Learning image-to-image transformations, BP.MS

Abstract

Image processing is an important step in many image analysis tasks. With deep neural networks, and particularly with the fully convolutional neural networks, one can learn image-to-image transformations from pairs of input-output training images. Although treating individual pixels as independent samples has shown impressive results, there is an understanding that models that take into consideration the spatial relationships between pixels are able to better capture structural information of image content. For instance, semantic segmentation networks in conjunction with conditional random fields(CRF) have shown state-of-the-art performance in image segmentation tasks. The aim of this project is to study such models, their optimization within the network optimization process, and develop improvements and new models to be integrated into general image-to-image transformation learning processes.

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)