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Type: Artigo
Title: An extension of the differential image foresting transform and its application to superpixel generation
Author: Condori, Marcos A.T.
Cappabianco, Fábio A.M.
Falcão, Alexandre X.
Miranda, Paulo A.V.
Abstract: The Image Foresting Transform (IFT) is a graph-based framework to develop image operators based on optimum connectivity between a root set and the remaining nodes, according to a given path-cost function. Its applications involve a variety of tasks, such as segmentation, boundary tracking, skeletonization, filtering, among others. The Differential Image Foresting Transform (DIFT) allows multiple IFT executions for different root sets and a same monotonically incremental path-cost function, making the processing time proportional to the number of modified nodes. In this paper, we extend the DIFT algorithm for non-monotonically incremental functions with root-based increases. This proposed extension, called Generalized DIFT (GDIFT), has been successfully used as the core part of some modern superpixels methods with state-of-the-art results. Experimental results show considerable efficiency gains over the sequential flow of IFTs for the generation of superpixels, also avoiding inconsistencies in image segmentation, which could occur with the regular DIFT algorithm
Subject: Transformada imagem-floresta
Country: Reino Unido
Editor: Elsevier
Rights: Fechado
Identifier DOI: 10.1016/j.jvcir.2019.102748
Date Issue: 2020
Appears in Collections:IC - Artigos e Outros Documentos

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