Mr. Ulrich Eisemann Profile

Ulrich Eisemann

at RWTH Aachen

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Proceedings Article | 28 December 2001 Paper

Refinement of a model for predicting perceived brightness

Ulrich Eisemann

Proceedings Volume 4663, (2001) https://doi.org/10.1117/12.453012

KEYWORDS: Data modeling, Image segmentation, Eye, Image classification, Visual process modeling, Eye models, Image processing, Bismuth, Image filtering, Feature extraction

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In this paper, an updated version of a previously proposed model for the prediction of perceived brightness is presented. The model is not only applicable to simple spatial configurations but also to complex scenes and relies entirely on physical and colorimetric data. These are derived from a complex description of the entire scene which eliminates the need for a priori knowledge like the popular reference white concept and others. The model includes an extensive preprocessing stage consisting of a central projection to transform the scene description into a pixel-oriented image, a simple pixel classification to identify the stimulus region and extensive histogram calculations to extract quantiles as characteristic features. Based on the quantiles, which form the output of the preprocessing stage and represent the distribution of luminance levels within the scene, a map has been implemented to calculate a value characterizing the perceived brightness. The development of the model structure was inspired by a series of haploscopic brightness matching experiments, whose experimental data were also used to train and test the model. The results are quite encouraging because the differences between experimental and model-predicted brightness values rarely exceed the range of the natural inter-observer deviations.

Proceedings Article | 21 December 2000 Paper

Influence of spatial luminance interactions on perceived brightness

Ulrich Eisemann

Proceedings Volume 4300, (2000) https://doi.org/10.1117/12.410789

KEYWORDS: Data modeling, Neural networks, Visual process modeling, Feature extraction, RGB color model, Visualization, CRTs, Optimization (mathematics), Eye, Databases

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Most color appearance models that have been published so far require a simplified description of the viewing field which is subdivided into a small number of homogeneous regions. The tristimulus values and luminance levels of these regions serve as input parameters for the models. The purpose of this paper however is to study brightness perception in a complex, achromatic surround using a more detailed description of the entire viewing field. Therefore, a number of psycho physical experiments were carried out using a CRT display on which relatively complex images were presented. Several observers were asked to judge the perceived brightness by adjusting the luminance level of a reference grey for a perfect brightness match. All thereby obtained psycho physical data were used to develop a new brightness appearance model that takes all objects in the entire visual field into account. The model includes an object feature extraction stage, in which object properties like area and position are extracted, a stage in which the characteristic object data are sorted into the appropriate classes of a histogram and a multivariate map in the form of a feedforward neural network to calculate the prediction of the perceived brightness.

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