Pregled bibliografske jedinice broj: 1205425
Illuminant estimation error detection for outdoor scenes using transformers
Illuminant estimation error detection for outdoor scenes using transformers // Proceedings of the 12th International Symposium on Image and Signal Processing and Analysis
Zagreb: Institute of Electrical and Electronics Engineers (IEEE), 2021. str. 276-281 doi:10.1109/ISPA52656.2021.9552045 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1205425 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Illuminant estimation error detection for outdoor scenes using transformers
Autori
Vršnak, Donik ; Domislović, Ilija ; Subašić, Marko ; Lončarić, Sven
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of the 12th International Symposium on Image and Signal Processing and Analysis
/ - Zagreb : Institute of Electrical and Electronics Engineers (IEEE), 2021, 276-281
ISBN
978-1-6654-2639-8
Skup
12th International Symposium on Image and Signal Processing and Analysis (ISPA 2021)
Mjesto i datum
Zagreb, Hrvatska, 13.09.2021. - 15.09.2021
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Image segmentation ; Image color analysis ; Convolution ; Computational modeling ; Pipelines ; Lighting ; Computer architecture
Sažetak
Color constancy is an important property of the human visual system that allows us to recognize the colors of objects regardless of the scene illumination. Computational color constancy is an unavoidable part of all modern camera image processing pipelines. However, most modern computational color constancy methods focus on the estimation of only one illuminant per scene, even though the scene may have multiple illuminations, such as very common outdoor scenes illuminated by sunlight. In this work, we address this problem by creating a deep learning model for image segmentation based on the transformer architecture, which can identify regions in outdoor scenes where the global estimation and subsequent color correction of the image is not accurate. We compare our convolution-free model to a convolutional model and a more simple baseline model and achieve excellent results.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb