Nalazite se na CroRIS probnoj okolini. Ovdje evidentirani podaci neće biti pohranjeni u Informacijskom sustavu znanosti RH. Ako je ovo greška, CroRIS produkcijskoj okolini moguće je pristupi putem poveznice www.croris.hr
izvor podataka: crosbi !

Blue Shift Assumption: Improving Illumination Estimation Accuracy for Single Image from Unknown Source (CROSBI ID 673461)

Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija

Banić, Nikola ; Lončarić, Sven Blue Shift Assumption: Improving Illumination Estimation Accuracy for Single Image from Unknown Source // Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP 2019). 2019. str. 191-197

Podaci o odgovornosti

Banić, Nikola ; Lončarić, Sven

engleski

Blue Shift Assumption: Improving Illumination Estimation Accuracy for Single Image from Unknown Source

Color constancy methods for removing the influence of illumination on object colors are divided into statistics-based and learning-based ones. The latter have low illumination estimation error, but only on images taken with the same sensor and in similar conditions as the ones used during training. For an image taken with an unknown sensor, a statistics-based method will often give higher accuracy than an untrained or specifically trained learning-based method because of its simpler assumptions not bounded to any specific sensor. The accuracy of a statistics-based method also depends on its parameter values, but for an image from an unknown source these values can be tuned only blindly. In this paper the blue shift assumption is proposed, which acts as a heuristic for choosing the optimal parameter values in such cases. It is based on real-world illumination statistics coupled with the results of a subjective user study and its application outperforms blind tuning in terms of accuracy. The source code is available at http://www.fer.unizg.hr/ipg/resources/color_constancy/.

Chromaticity, Color Constancy, Blue, Illumination Estimation, White Balancing

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

Podaci o prilogu

191-197.

2019.

objavljeno

Podaci o matičnoj publikaciji

Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP 2019)

978-989-758-354-4

Podaci o skupu

International Conference on Computer Vision Theory and Applications (VISAPP)

poster

25.02.2019-27.02.2019

Prag, Češka Republika

Povezanost rada

Računarstvo

Poveznice