Gradient extraction operators for discrete interval-valued data
Carlos Lopez-Molina, Cedric Marco-Detchart, Juan Cerron, Humberto Bustince, Bernard De Baets
Available Online June 2015.
- 10.2991/ifsa-eusflat-15.2015.118How to use a DOI?
- Image processing, interval-valued information, edge detection, canny method.
Digital images are generally created as discrete measurements of light, as performed by dedicated sensors. Consequently, each pixel contains a discrete approximation of the light inciding in a sensor element. The nature of this measurement implies certain uncertainty due to discretization matters. In this work we propose to model such uncertainty using intervals, further leading to the generation of so-called interval-valued images. Then, we study the partial differentiation of such images, putting a spotlight on antisymmetric convolution operators for such task. Finally, we illustrate the utility of the interval-valued images by studying the behaviour of an extended version of the well-known Canny edges detection method.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Carlos Lopez-Molina AU - Cedric Marco-Detchart AU - Juan Cerron AU - Humberto Bustince AU - Bernard De Baets PY - 2015/06 DA - 2015/06 TI - Gradient extraction operators for discrete interval-valued data BT - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology PB - Atlantis Press SP - 836 EP - 843 SN - 1951-6851 UR - https://doi.org/10.2991/ifsa-eusflat-15.2015.118 DO - 10.2991/ifsa-eusflat-15.2015.118 ID - Lopez-Molina2015/06 ER -