Non-Linear Scale-Space based on Fuzzy Contrast Enhancement: Theoretical results
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F21%3AA2201W7C" target="_blank" >RIV/61988987:17610/21:A2201W7C - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0165011421000750" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0165011421000750</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.fss.2021.02.022" target="_blank" >10.1016/j.fss.2021.02.022</a>
Alternative languages
Result language
angličtina
Original language name
Non-Linear Scale-Space based on Fuzzy Contrast Enhancement: Theoretical results
Original language description
This work presents a contrast enhancement operator based on a fuzzy-numerical description of images at the pixel level; this operator is further used to construct a scale-space, whose theoretical and practical properties are reviewed. A very remarkable feature of our scale-space is that, in contrast to many other scale-spaces, it converges to non-trivial stages. Within the study of our scale-space, we present a series of theoretical results that show that the convergence of the scale-space is closely related to the signal's convexity. Specifically, we prove formally that the intensities in convex signals tend to converge to the minimum intensity. As a result, our scale-space increases the contrast in the image and homogenizes images. In addition to theoretical results, we illustrate the scale-space's behaviour in ad-hoc 1D signals and in greyscale images. Finally, to validate the potential application of this theoretical approach, we show that the proposal can be used as a preprocessing that performed before a neural network technique, increasing the accuracy in a classification task.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10102 - Applied mathematics
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
FUZZY SET SYST
ISSN
0165-0114
e-ISSN
1872-6801
Volume of the periodical
421
Issue of the periodical within the volume
SI
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
25
Pages from-to
133-157
UT code for WoS article
000685648500007
EID of the result in the Scopus database
2-s2.0-85103242018