Advanced search
1 file | 245.72 KB
Author
Organization
Abstract
Differentiation of interval-valued functions is an intricate problem, since it cannot be defined as a direct generalization of differentiation of scalar ones. Literature on interval arithmetic contains proposals and definitions for differentiation, but their semantic is unclear for the cases in which intervals represent the ambiguity due to hesitancy or lack of knowledge. In this work we analyze the needs, tools and goals for interval-valued differentiation, focusing on the case of interval-valued images. This leads to the formulation of a differentiation schema inspired by bilateral filters, which allows for the accommodation of most of the methods for scalar image differentiation, but also takes support from interval-valued arithmetic. This schema can produce area-, segment-and vector-valued gradients, according to the needs of the image processing task it is applied to. Our developments are put to the test in the context of edge detection.
Keywords
EDGE-DETECTION, ANISOTROPIC DIFFUSION, FUZZY-LOGIC, COMPRESSION, MORPHOLOGY

Downloads

  • (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 245.72 KB

Citation

Please use this url to cite or link to this publication:

Chicago
Lopez-Molina, Carlos, Cédric Marco-Detchart, Laura De Miguel, Humberto Bustince, Javier Fernandez, and Bernard De Baets. 2016. “A Bilateral Schema for Interval-valued Image Differentiation.” In IEEE International Fuzzy Systems Conference Proceedings, 516–523. New York, NY, USA: IEEE.
APA
Lopez-Molina, Carlos, Marco-Detchart, C., De Miguel, L., Bustince, H., Fernandez, J., & De Baets, B. (2016). A bilateral schema for interval-valued image differentiation. IEEE International Fuzzy Systems Conference Proceedings (pp. 516–523). Presented at the 2016 IEEE International conference on Fuzzy Systems (FUZZ-IEEE), New York, NY, USA: IEEE.
Vancouver
1.
Lopez-Molina C, Marco-Detchart C, De Miguel L, Bustince H, Fernandez J, De Baets B. A bilateral schema for interval-valued image differentiation. IEEE International Fuzzy Systems Conference Proceedings. New York, NY, USA: IEEE; 2016. p. 516–23.
MLA
Lopez-Molina, Carlos, Cédric Marco-Detchart, Laura De Miguel, et al. “A Bilateral Schema for Interval-valued Image Differentiation.” IEEE International Fuzzy Systems Conference Proceedings. New York, NY, USA: IEEE, 2016. 516–523. Print.
@inproceedings{8553266,
  abstract     = {Differentiation of interval-valued functions is an intricate problem, since it cannot be defined as a direct generalization of differentiation of scalar ones. Literature on interval arithmetic contains proposals and definitions for differentiation, but their semantic is unclear for the cases in which intervals represent the ambiguity due to hesitancy or lack of knowledge. In this work we analyze the needs, tools and goals for interval-valued differentiation, focusing on the case of interval-valued images. This leads to the formulation of a differentiation schema inspired by bilateral filters, which allows for the accommodation of most of the methods for scalar image differentiation, but also takes support from interval-valued arithmetic. This schema can produce area-, segment-and vector-valued gradients, according to the needs of the image processing task it is applied to. Our developments are put to the test in the context of edge detection.},
  author       = {Lopez-Molina, Carlos and Marco-Detchart, C{\'e}dric and De Miguel, Laura and Bustince, Humberto and Fernandez, Javier and De Baets, Bernard},
  booktitle    = {IEEE International Fuzzy Systems Conference Proceedings},
  isbn         = {9781509006267},
  issn         = {1544-5615},
  keyword      = {EDGE-DETECTION,ANISOTROPIC DIFFUSION,FUZZY-LOGIC,COMPRESSION,MORPHOLOGY},
  language     = {eng},
  location     = {Vancouver, BC, Canada},
  pages        = {516--523},
  publisher    = {IEEE},
  title        = {A bilateral schema for interval-valued image differentiation},
  url          = {http://dx.doi.org/10.1109/fuzz-ieee.2016.7737730},
  year         = {2016},
}

Altmetric
View in Altmetric
Web of Science
Times cited: