The choice of a colour space is of great importance for many computer vision algorithms (e.g. edge detection and object recognition). It induces the equivalence classes to the actual algorithms. Since there are many colour spaces available, the problem is how to automatically select the weighting to integrate the colour spaces in order to produce the best result for a particular task. In this paper we propose a method to learn these weights, while exploiting the non-perfect correlation between colour spaces of features through the principle of diversification. As a result an optimal trade-off is achieved between repeatability and distinctiveness. The resulting weighting scheme will ensure maximal feature discrimination.
The method is experimentally verified for three feature detection tasks: Skin colour detection, edge detection and corner detection. In all three tasks the method achieved an optimal trade-off between (colour) invariance (repeatability) and discriminative power (distinctiveness).
We consider the well-known problem of segmenting a color image into foreground-background pixels. Such result can be obtained by segmenting the red, green and blue channels directly. Alternatively, the result may be obtained through the transformation of the color image into other color spaces, such as HSV or normalized colors. The problem then is how to select the color space or color channel that produces the best segmentation result. Furthermore, if more than one channels are equally good candidates, the next problem is how to combine the results. In this article, we investigate if the principles of the formal model for diversification of Markowitz (1952) can be applied to solve the problem. We verify, in theory and in practice, that the proposed diversification model can be applied effectively to determine the most appropriate combination of color spaces for the application at hand.
It is known that the transformation of RGB color space to the normalized color space is invariant to changes in the scene geometry. The transformation to the hue color space is additionally invariant to highlights. However, due to sensor noise, the transforms become unstable at many RGB values. This effect is usually overcome by ad hoc thresholding, for example if the RGB coordinates are located near the achromatic axis then the corresponding hue value is rejected. To arrive at a principled way to deal with the unstabilities that result from these color space transforms, the contribution of this report is as follows. Uncertainties in the measured RGB values are caused by photon noise, which arises from the statistical nature of photon production. Using a theoretical camera model, we determine the number of photons required to cause a color value transition. Based on the associated uncertainty according to the Poisson distribution, we then derive theoretical models that propagate this uncertainty to the uncertainty in the transformed color coordinates. We propose a histogram construction method based on Parzen estimators that incorporates this theoretical reliability. As a result, we overcome the need for thresholding of the transformed color values.
The problem of color constancy for discounting illumination color to obtain the apparent color of the object has been the topic of much research in computer vision. By assuming the neutral interface reflection and dichromatic reflection with highlights (i.e. highlights have the same color as the illuminant) various methods have been proposed aiming at recovering the illuminant color from color highlight analysis. In general, these methods are based on three color stimuli to approximate color. In this contribution, we estimate the spectral distribution from surface reflection using spectral information obtained by a spectrograph. The imaging spectrograph provides a spectral range at each pixel covering the visible wavelength range. Our method differ from existing methods by using a robust clustering technique to obtain the body and surface components in a multi-spectral space. These components determine the direction of the illumination spectral color. Then, we recover the illumination spectral power distribution by using principal component analysis for all wavelengths. To obtain the most reliable estimate of the spectral power distribution of the illuminant, all possible combinations of wavelengths are used to generate the optimal averaged estimation of the spectral power distribution of the scene illuminant. Our method is restricted to images containing a substantial amount of body reflection and highlights.