Pregled bibliografske jedinice broj: 964446
Green Stability Assumption: Unsupervised Learning for Statistics-Based Illumination Estimation
Green Stability Assumption: Unsupervised Learning for Statistics-Based Illumination Estimation // Journal of Imaging, 4 (2018), 11; 357915, 11 doi:10.3390/jimaging4110127 (međunarodna recenzija, članak, znanstveni)
CROSBI ID: 964446 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Green Stability Assumption: Unsupervised Learning for Statistics-Based Illumination Estimation
Autori
Banić, Nikola ; Lončarić, Sven
Izvornik
Journal of Imaging (2313-433X) 4
(2018), 11;
357915, 11
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
chromaticity ; color constancy ; gray-edge ; gray-world ; green ; illumination estimation ; shades-of-gray ; standard deviation ; unsupervised learning ; white balancing
Sažetak
In the image processing pipeline of almost every digital camera, there is a part for removing the influence of illumination on the colors of the image scene. Tuning the parameter values of an illumination estimation method for maximal accuracy requires calibrated images with known ground-truth illumination, but creating them for a given sensor is time-consuming. In this paper, the green stability assumption is proposed that can be used to fine-tune the values of some common illumination estimation methods by using only non-calibrated images. The obtained accuracy is practically the same as when training on calibrated images, but the whole process is much faster since calibration is not required and thus time is saved. The results are presented and discussed. The source code website is provided in Section Experimental Results.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo
POVEZANOST RADA
Projekti:
HRZZ-IP-2016-06-2092 - Metode i algoritmi za poboljšanje slika u boji u stvarnom vremenu (PerfectColor) (Lončarić, Sven, HRZZ ) ( CroRIS)
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb
