Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/2379
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Örnek, Ahmet Haydar | - |
dc.contributor.author | Çelik, Mustafa | - |
dc.contributor.author | Alper, Ozan Can | - |
dc.date.accessioned | 2022-05-23T20:07:30Z | - |
dc.date.available | 2022-05-23T20:07:30Z | - |
dc.date.issued | 2021 | - |
dc.identifier.isbn | 9781665442312 | - |
dc.identifier.uri | https://doi.org/10.1109/ICECET52533.2021.9698252 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/2379 | - |
dc.description | 2021 International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021 -- 9 December 2021 through 10 December 2021 -- -- 177176 | en_US |
dc.description.abstract | In order to realize real-time computer vision projects we need to avoid time consuming operations such as more inference for deep learning. Our current application uses face images to decide whether there is a mask on the face so as to prevent unhealthy situations in view of epidemic. Since frames are sequentially coming it is necessary to eliminate similar frames to avoid more inference. We show how to measure a similarity between two frames by comparing traditional and deep learning based methods in this study. This study shows that deep learning based method is more efficient than traditional methods when comparing images. © 2021 IEEE. | en_US |
dc.description.sponsorship | This study was supported by ”Epidemic Prevention System” project of Huawei Turkey R&D Center. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.relation.ispartof | International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021 | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | deep learning | en_US |
dc.subject | image similarity | en_US |
dc.subject | real-world applications | en_US |
dc.subject | Deep learning | en_US |
dc.subject | 'current | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Face images | en_US |
dc.subject | Image similarity | en_US |
dc.subject | Learning-based methods | en_US |
dc.subject | Real-time computer vision | en_US |
dc.subject | Real-world | en_US |
dc.subject | Real-world application | en_US |
dc.subject | Image analysis | en_US |
dc.title | Comparing Image Similarity Methods for Face Images | en_US |
dc.type | Conference Object | en_US |
dc.identifier.doi | 10.1109/ICECET52533.2021.9698252 | - |
dc.identifier.scopus | 2-s2.0-85127068598 | en_US |
dc.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü | en_US |
dc.identifier.wos | WOS:000814669100269 | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 57210593918 | - |
dc.authorscopusid | 57457105000 | - |
dc.authorscopusid | 57551207200 | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | embargo_20300101 | - |
item.languageiso639-1 | en | - |
item.openairetype | Conference Object | - |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
Appears in Collections: | Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections |
Files in This Item:
File | Size | Format | |
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Comparing_Image_Similarity_Methods_for_Face_Images.pdf Until 2030-01-01 | 1.81 MB | Adobe PDF | View/Open Request a copy |
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