Color histograms computed from the normalized and hue color space are negatively affected by sensor noise due to the instability of these color space transforms at many RGB values. To suppress the effect of sensor noise, in this paper density estimations are computed using variable kernels. To that end, models are proposed for the propagation of sensor noise through the normalized and hue colors. As a result, not only the hue and normalized color values are known, but also the associated uncertainty. This twofold information is used to derive the parameterization of the variable kernel used for the density estimation. It is empirically verified that the proposed method compares favorably to the traditional histogram.