Naslov
Unsupervised Learning for Color Constancy

Autori
Banić, Nikola ; Lončarić, Sven

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP 2018) / - , 2018, 181-188

Skup
13th International Joint Conference on Computer Vision Theory and Applications (VISAPP 2018)

Mjesto i datum
Funchal, Portugal, 27.01.2018. - 29.01.2018

Vrsta sudjelovanja
Poster

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Clustering, Color Constancy, Illumination Estimation, Unsupervised Learning, White Balancing

Sažetak
Most digital camera pipelines use color constancy methods to reduce the influence of illumination and camera sensor on the colors of scene objects. The highest accuracy of color correction is obtained with learning-based color constancy methods, but they require a significant amount of calibrated training images with known ground- truth illumination. Such calibration is time consuming, preferably done for each sensor individually, and therefore a major bottleneck in acquiring high color constancy accuracy. Statistics-based methods do not require calibrated training images, but they are less accurate. In this paper an unsupervised learning-based method is proposed that learns its parameter values after approximating the unknown ground-truth illumination of the training images, thus avoiding calibration. In terms of accuracy the proposed method outperforms all statistics-based and many state-of-the-art learning-based methods. The results are presented and discussed.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA