View source for Publications/randrianasoa.21.softx

{{Publication
| published = true
| date = 2021-12-17
| authors = Jimmy Francky Randrianasoa, Camille Kurtz, Éric Desjardin, Nicolas Passat
| title = AGAT: Building and Evaluating Binary Partition Trees for Image Segmentation
| journal = SoftwareX
| volume = 16
| number = 100855
| publisher = Elsevier
| lrdeprojects = Olena
| lrdenewsdate = 2021-12-17
| abstract = AGAT is a Java library dedicated to the constructionhandling 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 handit 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 visionimage/data analysis and machine learning.
| type = article
| id = randrianasoa.21.softx
| identifier = doi:10.1016/j.softx.2021.100855
| bibtex = 
@Article<nowiki>{</nowiki>	  randrianasoa.21.softx,
  author	= <nowiki>{</nowiki>Jimmy Francky Randrianasoa and Camille Kurtz and \'Eric
		  Desjardin and Nicolas Passat<nowiki>}</nowiki>,
  title		= <nowiki>{</nowiki><nowiki>{</nowiki>AGAT<nowiki>}</nowiki>: <nowiki>{</nowiki>B<nowiki>}</nowiki>uilding and Evaluating Binary Partition Trees
		  for Image Segmentation<nowiki>}</nowiki>,
  journal	= <nowiki>{</nowiki>SoftwareX<nowiki>}</nowiki>,
  year		= 2021,
  volume	= <nowiki>{</nowiki>16<nowiki>}</nowiki>,
  number	= <nowiki>{</nowiki>100855<nowiki>}</nowiki>,
  month		= dec,
  publisher	= <nowiki>{</nowiki>Elsevier<nowiki>}</nowiki>,
  abstract	= <nowiki>{</nowiki>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.<nowiki>}</nowiki>,
  doi		= <nowiki>{</nowiki>10.1016/j.softx.2021.100855<nowiki>}</nowiki>
<nowiki>}</nowiki>

}}