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Development of robust methods for edge delineation in images using graphs

Abstract

Delineating the edges of objects in images is important in the context of several knowledge areas, including medicine and remote sensing. The segmentation of organs and tissues of the human brain, for instance, is a necessary step in the study of the etiology, diagnosis, and treatment of diseases such as Alzheimer's and schizophrenia. Also, the classification of types of terrains helps preventing deforestation and measuring water levels enables issuing flood or drought alerts. Nevertheless, existing automatic and semi-automatic tools for borders delineation still suffers major flaws in dealing with discontinuities, noise, color, intensity, and texture variation. This project aims at studying alternatives to the current border delineation methodologies using graphs being: live-wire, riverbed and lazy walk. We will study mechanisms to enhance the interaction of the user in semi-automated tools for bi- and tri-dimensional delineation, the choice of functions for edge weight generation, the selection of path propagation functions in graphs, and supervised learning techniques so that the most relevant edges are identified with less effort and accuracy for each specific application. (AU)

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VEICULO: TITULO (DATA)

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; 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, DEC 2020. Web of Science Citations: 0.
BEJAR, HANS H. C.; FERZOLI GUIMARAES, SILVIO JAMIL; MIRANDA, V, PAULO A. Efficient hierarchical graph partitioning for image segmentation by optimum oriented cuts. PATTERN RECOGNITION LETTERS, v. 131, p. 185-192, MAR 2020. Web of Science Citations: 0.
BRAZ, CAIO DE MORAES; MIRANDA, PAULO A. V.; CIESIELSKI, KRZYSZTOF CHRIS; CAPPABIANCO, FABIO A. M. Optimum Cuts in Graphs by General Fuzzy Connectedness with Local Band Constraints. Journal of Mathematical Imaging and Vision, v. 62, n. 5, SI MAR 2020. Web of Science Citations: 0.

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