Non-Linear Scale-Space based on Fuzzy Contrast Enhancement: Theoretical results

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Non-Linear Scale-Space based on Fuzzy Contrast Enhancement: Theoretical results

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Non-Linear Scale-Space based on Fuzzy Contrast Enhancement: Theoretical results

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

FUZZY SET SYST

ISSN

0165-0114

e-ISSN

1872-6801

Svazek periodika

421

Číslo periodika v rámci svazku

SI

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

25

Strana od-do

133-157

Kód UT WoS článku

000685648500007

EID výsledku v databázi Scopus

2-s2.0-85103242018