MIDaC: mining of images and complex data to medical and agrometeorological data analysis

• Grant number: 10/00594-0
• Support Opportunities: Regular Research Grants
• Start date: April 01, 2010
• End date: September 30, 2012
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Marcela Xavier Ribeiro
• Grantee: Marcela Xavier Ribeiro
• Host Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil

Abstract

Vision is the human sense that most captures information per unit of time. Thus, the image and visualization analysis have become essential components in many research areas such as medicine and agrometeorology. This research project aims at developing techniques of image mining and visualization for complex data analysis applied to explore medical and agrometeorological data. The analysis of such data requires sophisticated computational tools for processing, mining and visualization. This project proposes to develop a set of techniques and algorithms that combine visual data mining and image mining, in order to increase the potential of computational analysis of the complex data. The goal is that the developed techniques helps on the understanding of data semantics. Because of its wide applicability and its prominent results in recent research, the main data mining task to be employed in this project is the association rule mining. This project also aims at integrating visual and image mining to increase the analysis of complex data, promoting the development of computational tools, more robust than the current state of the art, to support the decision-making process. (AU)

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)

DIAS, RAFAEL L.; MPINDA, STEVE ATAKY T.; BUENO, RENATO; RIBEIRO, MARCELA X.; BANISSI, E; BANNATYNE, MWM; BOUALI, F; SOARESDATIA, NM; GRINSTEIN, G; GROTH, D; et al. Visual-Interactive k-NDN Method (VIK): A Novel Approach to Visualize and Interact with Content-Based Image Retrieval Systems Regarding Similarity and Diversity. 2017 21ST INTERNATIONAL CONFERENCE INFORMATION VISUALISATION (IV), v. N/A, p. 6-pg., 2017-01-01. (11/01447-3, 10/00594-0)