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Dipl.-Inf. Eva Eibenberger

Alumnus of the Pattern Recognition Lab of the Friedrich-Alexander-Universität Erlangen-Nürnberg

Computer Vision

Reflectance Modeling in Log-Chromaticity Space


Despite the strengths and popularity of the log-chromaticity space (LCS), there is still a significant amount of concern regarding its narrow-band assumption (NBA). Though not always necessary, this assumption is relatively common, as it leads to elegant formulations. We present a scheme for evaluating whether a deviation from the NBA will have an impact on the expected LCS values. We also introduce two metrics for measuring the divergence from the expected behavior under the NBA in LCS. Lastly, we empirically analyze how different types of reflectance spectra are affected in varying degrees by this assumption. For example, experiments with real and synthetic data show that the violation of the NBA typically has insignificant impact on bright unsaturated colors.


The violation of the narrow-band assumption causes a deviation of the log-chromaticity values from their ideal position. A theoretical and quantitative analysis reveals that the introduced error differs for different surface reflectances.


My research is funded by the International Max Planck Research School (IMPRS) for Optics and Imaging and the Erlangen Graduate School in Advanced Optical Technologies (SAOT) by the German National Science Foundation (DFG) in the framework of the excellence initiative.