Computing von Kries Illuminant Changes by Piecewise Inversion of Cumulative Color Histograms

We present a linear algorithm for the computation of the illuminant change occurring between two color pictures of a scene. We model the light variations with the von Kries diagonal transform and we estimate it by minimizing a dissimilarity measure between the piecewise inversions of the cumulative color histograms of the considered images. We also propose a method for illuminant invariant image recognition based on our von Kries transform estimate

Illuminant and Device Invariance Using Histogram Equalisation

In this paper we propose a new device and illumination invariant image representation based on an existing grey-scale image enhancement technique: histogram equalisation. Our method is based on the premise that the rank ordering of sensor responses is preserved across a change in imaging conditions (lighting or device). We set out the theoretical conditions under which this premise is true and we present empirical evidence which demonstrates that rank ordering is maintained in practice for a wide range of illuminants and imaging devices. We then show how we can exploit this rank invariance using histogram equalisation to derive an invariant image representation. Device and illuminant invariance are important in many imaging applications and in this paper we demonstrate the practical benefits of our new method in one such situation: the problem of image retrieval. We show that using the new invariant image representation to index into a database of images taken with a variety of devices under different lights provides very good indexing performance across all imaging conditions

Illuminant and Device Invariance Using Histogram Equalisation

In this paper we propose a new device and illumination invariant image representation based on an existing greyscale image enhancement technique: histogram equalisation. Our method is based on the premise that the rank ordering of sensor responses is preserved across a change in imaging conditions (lighting or device). We set out the theoretical conditions under which this premise is true and we present empirical evidence which demonstrates that rank ordering is maintained in practice for a wide range of illuminants and imaging devices. We then show how we can exploit this rank invariance using histogram equalisation to derive an invariant image representation. Device and illuminant invariance are important in many imaging applications and in this paper we demonstrate the practical benefits of our new method in one such situation: the problem of image retrieval. We show that using the new invariant image representation to index into a database of images taken with a variety of devices under different lights provides very good indexing performance across all imaging conditions. 1