Color Constancy

With the advancement in sensor technology, use of multispectral imaging is gaining wide popularity for computer vision applications. Multispectral imaging is used to achieve better discrimination between the radiance spectra, as compared to the color images. However, it is still sensitive to illumination changes. This study evaluates the potential evolution of illuminant estimation models from color to multispectral imaging. We first present a state of the art on computational color constancy and then extend a set of algorithms to use them in multispectral imaging. We investigate the influence of camera spectral sensitivities and number of channels. Experiments are performed on simulations over hyperspectral data. The outcomes indicate that extension of computational color constancy algorithms from color to spectral, give promising results and may have the potential to lead towards efficient and stable representation across illuminants. However, it is highly dependant on spectral sensitivities and noise. We believe that the development of illuminant invariant multispectral imaging systems will be a key enabler for further use of this technology.

In this paper, we propose the concept of manifold of color perception based on an observation that the perceived color in a set of similar color images defines a manifold in the high dimensional space. Such a manifold representation can be learned from a few images of similar color characteristics. This learned manifold can then be used as a basis for color correction of the images having different color perception to the previously learned color. To learn the manifold for color perception, we propose a novel learning algorithm based on a recurrent neural network. Unlike the conventional recurrent neural network model in which the memory is stored in an attractive fixed point at discrete locations in the state space, the dynamics of the proposed learning algorithm represents memory as a line of attraction. The region of convergence at the line of attraction is defined by the statistical characteristics of the training data. We demonstrate experimentally how we can use the proposed manifold to color-balance the common lighting variations in the environment.

Abstract In this paper, we propose a manifold-based methodology for color constancy. It is observed that the center surround information of an image creates a manifold in color space. The relationship between the points in the manifold is modeled as a line. The human ...

We compare an objective and a subjective performance measure for color constancy algorithms. Eight hyper-spectral images were rendered under a neutral reference illuminant and four chromatic illuminants (Red, Green, Yellow, Blue). The scenes rendered under the chromatic illuminants were color corrected by 5 color constancy algorithms that are based on zero-, first- and second-order image statistics. The angular ...

As the color of an object partly depends on the color of the illumination, computer vision systems that rely on color features are likely to benefit from a property that most hu- mans have naturally: color constancy. Color constancy is the ability to recognize colors of objects, invariant to the color of the light source. In this paper, an new