CITATION.cff

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title: >-
  Exploratory Graph-based Semi-supervised Image Segmentation
  (EGSIS)
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - name-suffix: Neto
    given-names: Manoel
    family-names: Vilela Machado
    email: manoelnt0@gmail.com
    orcid: 'https://orcid.org/0009-0005-5294-1675'
    affiliation: 'Instituto Tecnológico de Aeronáutica '
repository-code: 'https://www.github.com/ryukinix/egsis'
url: 'https://www.github.com/ryukinix/egsis'
repository-artifact: 'https://pypi.org/project/egsis/'
abstract: >
  Image segmentation is a technique that divides the image
  into regions

  of interest, such as objects in a landscape. Image
  segmentation

  algorithms present variations in their types of learning,
  including

  unsupervised, supervised, and semi-supervised. In the
  context of

  interactive segmentation, the challenge is to segment
  objects from the

  background with the help of initial labels provided by a

  user. Superpixels are unsupervised segmentation algorithms
  used as

  pre-segmentation for various image problems, such as
  classification

  and segmentation. Complex networks are graphs with
  non-trivial

  structures used to represent certain data domains, such as
  regions of

  an image and their neighborhoods. Collective dynamics in a
  complex

  network refer to the emergent and interactive behavior of
  various

  elements or actors within an interconnected and complex
  network, where

  the actions of one element can influence the actions of
  others. In

  this work, we propose a semi-supervised image segmentation
  algorithm

  that combines the techniques of superpixels, complex
  networks, and

  collective dynamics. The method is evaluated under various
  conditions

  through an interactive segmentation scenario.
keywords:
  - complex networks
  - image segmentation
  - superpixel
  - collective dynamics
  - interactive segmentation
license: BSD-3-Clause