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Efficient hierarchical layered graph approach for multi-region segmentation

Leon, Leissi Margarita Castaneda

Biblioteca Digital de Teses e Dissertações da USP; Universidade de São Paulo; Instituto de Matemática e Estatística 2019-03-15

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  • Título:
    Efficient hierarchical layered graph approach for multi-region segmentation
  • Autor: Leon, Leissi Margarita Castaneda
  • Orientador: Miranda, Paulo Andre Vechiatto de
  • Assuntos: Superpixels; Segmentação Interativa; Segmentação Hierárquica De Imagens; Segmentação De Múltiplos Objetos; Segmentação De Imagens Médicas; Segmentação De Imagens Baseada Em Grafos; Multiple Object Segmentation; Medical Image Segmentation; Interactive Segmentation; Image Segmentation Based On Graphs; Superpixels; Hierarchical Image Segmentation
  • Notas: Tese (Doutorado)
  • Descrição: Image segmentation refers to the process of partitioning an image into meaningful regions of interest (objects) by assigning distinct labels to their composing pixels. Images are usually composed of multiple objects with distinctive features, thus requiring distinct high-level priors for their appropriate modeling. In order to obtain a good segmentation result, the segmentation method must attend all the individual priors of each object, as well as capture their inclusion/exclusion relations. However, many existing classical approaches do not include any form of structural information together with different high-level priors for each object into a single energy optimization. Consequently, they may be inappropriate in this context. We propose a novel efficient seed-based method for the multiple object segmentation of images based on graphs, named Hierarchical Layered Oriented Image Foresting Transform (HLOIFT). It uses a tree of the relations between the image objects, being each object represented by a node. Each tree node may contain different individual high-level priors and defines a weighted digraph, named as layer. The layer graphs are then integrated into a hierarchical graph, considering the hierarchical relations of inclusion and exclusion. A single energy optimization is performed in the hierarchical layered weighted digraph leading to globally optimal results satisfying all the high-level priors. The experimental evaluations of HLOIFT and its extensions, on medical, natural and synthetic images, indicate promising results comparable to the state-of-the-art methods, but with lower computational complexity. Compared to hierarchical segmentation by the min cut/max-flow algorithm, our approach is less restrictive, leading to globally optimal results in more general scenarios, and has a better running time.
  • DOI: 10.11606/T.45.2019.tde-12092019-110342
  • Editor: Biblioteca Digital de Teses e Dissertações da USP; Universidade de São Paulo; Instituto de Matemática e Estatística
  • Data de criação/publicação: 2019-03-15
  • Formato: Adobe PDF
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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