Search Results for author:
Found 8 papers, 2 papers with code
Illumination Estimation Challenge: experience of past two years
The main advantage of testing a method on a challenge over testing in on some of the known datasets is the fact that the ground-truth illuminations for the challenge test images are unknown up until the results have been submitted, which prevents any potential hyperparameter tuning that may be biased.
TVOR: Finding Discrete Total Variation Outliers among Histograms
Pearson's chi-squared test can detect outliers in the data distribution of a given set of histograms.
Methodology Discrete Mathematics
The Cube++ Illumination Estimation Dataset
In this paper, a new illumination estimation dataset is proposed that aims to alleviate many of the mentioned problems and to help the illumination estimation research.
CroP: Color Constancy Benchmark Dataset Generator
For a given camera sensor it enables generation of any number of realistic raw images taken in a subset of the real world, namely images of printed photographs.
The Past and the Present of the Color Checker Dataset Misuse
The pipelines of digital cameras contain a part for computational color constancy, which aims to remove the influence of the illumination on the scene colors.
Green Stability Assumption: Unsupervised Learning for Statistics-Based Illumination Estimation
In this paper it is first shown that the accuracy of statistics-based methods reported in most papers was not obtained by means of the necessary cross-validation, but by using the whole benchmark datasets for both training and testing.
Unsupervised Learning for Color Constancy
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.
Using the Random Sprays Retinex Algorithm for Global Illumination Estimation
Therefore we propose a method for estimating global illumination estimation based on local RSR results.
