The Cube++ Illumination Estimation Dataset

Stabilizing Illumination Chromaticity Estimation Using the Illumination Line Segment

We propose a method to stabilize illumination color estimation by using the idea of an “Illumination line segment”. It is a finite line where every illumination color exists in the color space. By restricting the estimated illumination colors to be on this line, the method succeeds in making the estimation more robust and accurate. The method is based on our past work, while it uses direct calc...

Estimation of illumination characteristics

The description of the relation between the one-parameter subgroups of a group and the differential operators in the Lie-algebra of the group is one of the major topics in Lie-theory. We use this framework to derive a partial differential equation which describes the relation between the time-change of the spectral characteristics of the illumination source and the change of the color pixels in...

Rank-Based Illumination Estimation

A new two-stage illumination estimation method based on the concept of rank is presented. The method first estimates the illuminant locally in subwindows using a ranking of digital counts in each color channel and then combines local subwindow estimates again based on a ranking of the local estimates. The proposed method unifies the MaxRGB and Grayworld methods. Despite its simplicity, the perf...

Global Illumination via Density Estimation

Accuracy Estimation With Clustered Dataset

If the dataset available to machine learning results from cluster sampling (e.g. patients from a sample of hospital wards), the usual cross-validation error rate estimate can lead to biased and misleading results. An adapted cross-validation is described for this case. Using a simulation, the sampling distribution of the generalization error rate estimate, under cluster or simple random samplin...