Enhancement of dronogram aid to visual interpretation of target objects via intuitionistic fuzzy hesitant sets
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F19%3A10242377" target="_blank" >RIV/61989100:27240/19:10242377 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0020025519304864?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0020025519304864?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.ins.2019.05.069" target="_blank" >10.1016/j.ins.2019.05.069</a>
Alternative languages
Result language
angličtina
Original language name
Enhancement of dronogram aid to visual interpretation of target objects via intuitionistic fuzzy hesitant sets
Original language description
In this paper, we address the hesitant information in enhancement task often caused by differences in image contrast. Enhancement approaches generally use certain filters which generate artifacts or are unable to recover all the objects details in images. Typically, the contrast of an image quantifies a unique ratio between the amounts of black and white through a single pixel. However, contrast is better represented by a group of pixels. We have proposed a novel image enhancement scheme based on intuitionistic hesitant fuzzy sets (IHFSs) for drone images (dronogram) to facilitate better interpretations of target objects. First, a given dronogram is divided into foreground and background areas based on an estimated threshold from which the proposed model measures the amount of black/white intensity levels. Next, we fuzzify both of them and determine the hesitant score indicated by the distance between the two areas for each point in the fuzzy plane. Finally, a hyperbolic operator is adopted for each membership grade to improve the photographic quality leading to enhanced results via defuzzification. The proposed method is tested on a large drone image database. Results demonstrate better contrast enhancement, improved visual quality, and better recognition compared to the state-of-the-art methods. (C) 2019 The Authors. Published by Elsevier Inc.
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
10200 - Computer and information sciences
Result continuities
Project
<a href="/en/project/EF16_027%2F0008463" target="_blank" >EF16_027/0008463: Science without borders</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Information sciences
ISSN
0020-0255
e-ISSN
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Volume of the periodical
500
Issue of the periodical within the volume
October 2019
Country of publishing house
US - UNITED STATES
Number of pages
20
Pages from-to
67-86
UT code for WoS article
000478711700005
EID of the result in the Scopus database
2-s2.0-85066277144