Exploring Deep Learning Methods for Computer Vision Applications across Multiple Sectors: Challenges and Future Trends

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Exploring Deep Learning Methods for Computer Vision Applications across Multiple Sectors: Challenges and Future Trends

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Exploring Deep Learning Methods for Computer Vision Applications across Multiple Sectors: Challenges and Future Trends

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20301 - Mechanical engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

CMES-COMPUTER MODELING IN ENGINEERING &amp; SCIENCES

ISSN

1526-1492

e-ISSN

1526-1506

Svazek periodika

139

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

39

Strana od-do

03-141

Kód UT WoS článku

001109078200001

EID výsledku v databázi Scopus

—