REGION HOMOGENEITY IN THE LOGARITHMIC IMAGE PROCESSING FRAMEWORK: APPLICATION TO REGION GROWING ALGORITHMS

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  • Additional Information
    • Publication Information:
      Slovenian Society for Stereology and Quantitative Image Analysis, 2019.
    • Publication Date:
      2019
    • Collection:
      LCC:Medicine (General)
      LCC:Mathematics
    • Abstract:
      In order to create an image segmentation method robust to lighting changes, two novel homogeneity criteria of an image region were studied. Both were defined using the Logarithmic Image Processing (LIP) framework whose laws model lighting changes. The first criterion estimates the LIP-additive homogeneity and is based on the LIP-additive law. It is theoretically insensitive to lighting changes caused by variations of the camera exposure-time or source intensity. The second, the LIP-multiplicative homogeneity criterion, is based on the LIP-multiplicative law and is insensitive to changes due to variations of the object thickness or opacity. Each criterion is then applied in Revol and Jourlin’s (1997) region growing method which is based on the homogeneity of an image region. The region growing method becomes therefore robust to the lighting changes specific to each criterion. Experiments on simulated and on real images presenting lighting variations prove the robustness of the criteria to those variations. Compared to a state-of the art method based on the image component-tree, ours is more robust. These results open the way to numerous applications where the lighting is uncontrolled or partially controlled.
    • File Description:
      electronic resource
    • ISSN:
      1580-3139
      1854-5165
    • Relation:
      https://www.ias-iss.org/ojs/IAS/article/view/2038; https://doaj.org/toc/1580-3139; https://doaj.org/toc/1854-5165
    • Accession Number:
      10.5566/ias.2038
    • Rights:
      Journal Licence: CC BY-NC
    • Accession Number:
      edsdoj.58f7f8512294daa96f2e0f8dd084952
  • Citations
    • ABNT:
      GUILLAUME NOYEL; MICHEL JOURLIN. Region Homogeneity in the Logarithmic Image Processing Framework: Application to Region Growing Algorithms. Image Analysis and Stereology, [s. l.], n. 1, p. 43, 2019. Disponível em: . Acesso em: 14 nov. 2019.
    • AMA:
      Guillaume Noyel, Michel Jourlin. Region Homogeneity in the Logarithmic Image Processing Framework: Application to Region Growing Algorithms. Image Analysis and Stereology. 2019;(1):43. doi:10.5566/ias.2038.
    • APA:
      Guillaume Noyel, & Michel Jourlin. (2019). Region Homogeneity in the Logarithmic Image Processing Framework: Application to Region Growing Algorithms. Image Analysis and Stereology, (1), 43. https://doi.org/10.5566/ias.2038
    • Chicago/Turabian: Author-Date:
      Guillaume Noyel, and Michel Jourlin. 2019. “Region Homogeneity in the Logarithmic Image Processing Framework: Application to Region Growing Algorithms.” Image Analysis and Stereology, no. 1: 43. doi:10.5566/ias.2038.
    • Harvard:
      Guillaume Noyel and Michel Jourlin (2019) ‘Region Homogeneity in the Logarithmic Image Processing Framework: Application to Region Growing Algorithms’, Image Analysis and Stereology, (1), p. 43. doi: 10.5566/ias.2038.
    • Harvard: Australian:
      Guillaume Noyel & Michel Jourlin 2019, ‘Region Homogeneity in the Logarithmic Image Processing Framework: Application to Region Growing Algorithms’, Image Analysis and Stereology, no. 1, p. 43, viewed 14 November 2019, .
    • MLA:
      Guillaume Noyel, and Michel Jourlin. “Region Homogeneity in the Logarithmic Image Processing Framework: Application to Region Growing Algorithms.” Image Analysis and Stereology, no. 1, 2019, p. 43. EBSCOhost, doi:10.5566/ias.2038.
    • Chicago/Turabian: Humanities:
      Guillaume Noyel, and Michel Jourlin. “Region Homogeneity in the Logarithmic Image Processing Framework: Application to Region Growing Algorithms.” Image Analysis and Stereology, no. 1 (2019): 43. doi:10.5566/ias.2038.
    • Vancouver/ICMJE:
      Guillaume Noyel, Michel Jourlin. Region Homogeneity in the Logarithmic Image Processing Framework: Application to Region Growing Algorithms. Image Analysis and Stereology [Internet]. 2019 [cited 2019 Nov 14];(1):43. Available from: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsdoj&AN=edsdoj.58f7f8512294daa96f2e0f8dd084952&custid=s8280428