Efficient Hierarchical Multi-Object Segmentation in Layered Graphs
Autor: | Leissi Margarita Castaneda Leon, Paulo A. V. Miranda, Krzysztof Ciesielski |
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Rok vydání: | 2021 |
Předmět: |
medical image segmentation
Computer science business.industry multi-object segmentation 02 engineering and technology Object (computer science) 030218 nuclear medicine & medical imaging METODOLOGIA E TÉCNICAS DE COMPUTAÇÃO 03 medical and health sciences 0302 clinical medicine hierarchical image segmentation on graphs QA1-939 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Segmentation Artificial intelligence business Mathematics |
Zdroj: | Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual) Universidade de São Paulo (USP) instacron:USP Mathematical Morphology, Vol 5, Iss 1, Pp 21-42 (2021) |
ISSN: | 2353-3390 |
Popis: | We propose a novel efficient seed-based method for the multi-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, with each node in the tree representing an object. Each tree node may contain different individual high-level priors of its corresponding object 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, on medical, natural, and synthetic images, indicate promising results comparable to the related baseline methods that include structural information, but with lower computational complexity. Compared to the 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. |
Databáze: | OpenAIRE |
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