Exploring Deep Learning Methods for Computer Vision Applications across Multiple Sectors: Challenges and Future Trends
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F24%3A10253549" target="_blank" >RIV/61989100:27230/24:10253549 - isvavai.cz</a>
Result on the web
<a href="https://www.techscience.com/CMES/v139n1/55098" target="_blank" >https://www.techscience.com/CMES/v139n1/55098</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.32604/cmes.2023.028018" target="_blank" >10.32604/cmes.2023.028018</a>
Alternative languages
Result language
angličtina
Original language name
Exploring Deep Learning Methods for Computer Vision Applications across Multiple Sectors: Challenges and Future Trends
Original language description
Computer vision (CV) was developed for computers and other systems to act or make recommendations based on visual inputs, such as digital photos, movies, and other media. Deep learning (DL) methods are more successful than other traditional machine learning (ML) methods in CV. DL techniques can produce state-of-the-art results for difficult CV problems like picture categorization, object detection, and face recognition. In this review, a structured discussion on the history, methods, and applications of DL methods to CV problems is presented. The sector-wise presentation of applications in this paper may be particularly useful for researchers in niche fields who have limited or introductory knowledge of DL methods and CV. This review will provide readers with context and examples of how these techniques can be applied to specific areas. A curated list of popular datasets and a brief description of them are also included for the benefit of readers.
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
20301 - Mechanical engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
ISSN
1526-1492
e-ISSN
1526-1506
Volume of the periodical
139
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
39
Pages from-to
03-141
UT code for WoS article
001109078200001
EID of the result in the Scopus database
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