AGAT: Building and evaluating binary partition trees for image segmentation

AGAT is a Java library dedicated to the construction, handling and evaluation of binary partition trees, a hierarchical data structure providing multiscale partitioning of images and, more generally, of valued graphs. On the one hand, this library offers functionalities to build binary partition tre...

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Autores principales:	Jimmy Francky Randrianasoa, Camille Kurtz, Éric Desjardin, Nicolas Passat
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Binary partition tree Hierarchical modeling Image/graph processing Quality evaluation Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/fedb51a5d2284e1c90a7ca4fbbaebaa3
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id	oai:doaj.org-article:fedb51a5d2284e1c90a7ca4fbbaebaa3
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spelling	oai:doaj.org-article:fedb51a5d2284e1c90a7ca4fbbaebaa32021-11-14T04:34:23ZAGAT: Building and evaluating binary partition trees for image segmentation2352-711010.1016/j.softx.2021.100855https://doaj.org/article/fedb51a5d2284e1c90a7ca4fbbaebaa32021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352711021001308https://doaj.org/toc/2352-7110AGAT is a Java library dedicated to the construction, handling and evaluation of binary partition trees, a hierarchical data structure providing multiscale partitioning of images and, more generally, of valued graphs. On the one hand, this library offers functionalities to build binary partition trees in the usual way, but also to define multifeature trees, a novel and richer paradigm of binary partition trees built from multiple images and/or several criteria. On the other hand, it also allows one to manipulate the binary partition trees, for instance by defining/computing tree cuts that can be interpreted in particular as segmentations when dealing with image modeling. In addition, some evaluation tools are also provided, which allow one to evaluate the quality of different binary partition trees for such segmentation tasks. AGAT can be easily handled by various kinds of users (students, researchers, practitioners). It can be used as is for experimental purposes, but can also form a basis for the development of new methods and paradigms for construction, use and intensive evaluation of binary partition trees. Beyond the usual imaging applications, its underlying structure also allows for more general developments in graph-based analysis, leading to a wide range of potential applications in computer vision, image/data analysis and machine learning.Jimmy Francky RandrianasoaCamille KurtzÉric DesjardinNicolas PassatElsevierarticleBinary partition treeHierarchical modelingImage/graph processingQuality evaluationComputer softwareQA76.75-76.765ENSoftwareX, Vol 16, Iss , Pp 100855- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Binary partition tree Hierarchical modeling Image/graph processing Quality evaluation Computer software QA76.75-76.765
spellingShingle	Binary partition tree Hierarchical modeling Image/graph processing Quality evaluation Computer software QA76.75-76.765 Jimmy Francky Randrianasoa Camille Kurtz Éric Desjardin Nicolas Passat AGAT: Building and evaluating binary partition trees for image segmentation
description	AGAT is a Java library dedicated to the construction, handling and evaluation of binary partition trees, a hierarchical data structure providing multiscale partitioning of images and, more generally, of valued graphs. On the one hand, this library offers functionalities to build binary partition trees in the usual way, but also to define multifeature trees, a novel and richer paradigm of binary partition trees built from multiple images and/or several criteria. On the other hand, it also allows one to manipulate the binary partition trees, for instance by defining/computing tree cuts that can be interpreted in particular as segmentations when dealing with image modeling. In addition, some evaluation tools are also provided, which allow one to evaluate the quality of different binary partition trees for such segmentation tasks. AGAT can be easily handled by various kinds of users (students, researchers, practitioners). It can be used as is for experimental purposes, but can also form a basis for the development of new methods and paradigms for construction, use and intensive evaluation of binary partition trees. Beyond the usual imaging applications, its underlying structure also allows for more general developments in graph-based analysis, leading to a wide range of potential applications in computer vision, image/data analysis and machine learning.
format	article
author	Jimmy Francky Randrianasoa Camille Kurtz Éric Desjardin Nicolas Passat
author_facet	Jimmy Francky Randrianasoa Camille Kurtz Éric Desjardin Nicolas Passat
author_sort	Jimmy Francky Randrianasoa
title	AGAT: Building and evaluating binary partition trees for image segmentation
title_short	AGAT: Building and evaluating binary partition trees for image segmentation
title_full	AGAT: Building and evaluating binary partition trees for image segmentation
title_fullStr	AGAT: Building and evaluating binary partition trees for image segmentation
title_full_unstemmed	AGAT: Building and evaluating binary partition trees for image segmentation
title_sort	agat: building and evaluating binary partition trees for image segmentation
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/fedb51a5d2284e1c90a7ca4fbbaebaa3
work_keys_str_mv	AT jimmyfranckyrandrianasoa agatbuildingandevaluatingbinarypartitiontreesforimagesegmentation AT camillekurtz agatbuildingandevaluatingbinarypartitiontreesforimagesegmentation AT ericdesjardin agatbuildingandevaluatingbinarypartitiontreesforimagesegmentation AT nicolaspassat agatbuildingandevaluatingbinarypartitiontreesforimagesegmentation
_version_	1718429995762712576

AGAT: Building and evaluating binary partition trees for image segmentation

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