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1

Depth map color constancy

Ebner M., Hansen J.

Bio-Algorithms and Med-Systems

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2013

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Vol. 9, no. 4

167--177

EN

A human observer is able to determine the color of objects independent of the light illuminating these objects. This ability is known as color constancy. In the first stages of visual information processing, data are analyzed with respect to wavelength composition, orientation, motion, and depth. With this contribution, we investigate whether depth information can help in estimating the color of the objects. We assume that local space average color is computed in V4 through resistively coupled neurons to estimate the color of the illuminant. We show how this computational model can be extended to incorporate depth information.

2

A computational model for color perception

Ebner M.

Bio-Algorithms and Med-Systems

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2012

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Vol. 8, no. 4

387--415

EN

Color is not a physical quantity of an object. It cannot be measured. We can only measure reflectance, i.e. the amount of light reflected for each wavelength. Nevertheless, we attach colors to the objects around us. A human observer perceives colors as being approximately constant irrespective of the illuminant which is used to illuminate the scene. Colors are a very important cue in everyday life. They can be used to recognize or distinguish different objects. Currently, we do not yet know how the brain arrives at a color constant or approximately color constant descriptor, i.e. what computational processing is actually performed by the brain. What we need is a computational description of color perception in particular and color vision in general. Only if we are able to write down a full computational theory of the visual system then we have understood how the visual system works. With this contribution, a computational model of color perception is presented. This model is much simpler compared to previous theories. It is able to compute a color constant descriptor even in the presence of spatially varying illuminants. According to this model, the cones respond approximately logarithmic to the irradiance entering the eye. Cells in V1 perform a change of the coordinate system such that colors are represented along a red-green, a blue-yellow and a black-white axis. Cells in V4 compute local space average color using a resistive grid. The resistive grid is formed by cells in V4. The left and right hemispheres are connected via the corpus callosum. A color constant descriptor which is presumably used for color based object recognition is computed by subtracting local space average color from the cone response within a rotated coordinate system.

3

Combined achromatic and chromatic correction of color images

Lucchese L., Mitra S. K.

Zeszyty Naukowe. Telekomunikacja i Elektronika / Akademia Techniczno-Rolnicza w Bydgoszczy

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2006

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z. 9

25-35

EN

This paper advances a new algorithm for determining the color mapping between two images of the same object or scene taken under different illumination conditions. Yhe proposed algorithm compensates for differences in colors by separately equalizing their achromatic and chromatic components. The eqialization of the 1-D achromatic channel is carried out with a standard technique for gray-level images whereas the equalization of the 2-D chromatic channel is considered as a problem of image warping. This method can also be used to color calibrate a trichromatic sensing device, provided that a color chart is available.

PL

W artykule przedstawiono nowy algorytm służący do określenia odwzo-rowania kolorów pomiędzy dwoma obrazami tego samego obiektu lub sceny, uzyskanymi w różnych warunkach oświetleniowych. Działanie zaproponowa-nego algorytmu oparte jest na zasadzie kompensacji różnic kolorów, co wyko-nuje się niwelując różnice oddzielnie dla składowej achromatycznej i chroma-tycznej. Wyrównanie (niwelacja różnic) w achromatycznym kanale l-D jest dokonywane poprzez wykorzystanie standardowej techniki używanej w przy-padku obrazów czarno-białych, natomiast operacja wyrównania w chroma-tycznym kanale 2-D jest rozpatrywana jako problem deformacji obrazu. Przed-stawiona metoda może być również zastosowana do kalibracji kolorów czujni-ków kolorów, przy założeniu, że referencyjny rozkład kolorów jest znany.

4

LUT and multilevel brownian retinex colour correction

Rizzi A., Marini D., De Carli L.

Machine Graphics and Vision

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2002

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Vol. 11, No. 2/3

153-168

EN

Retinex, a model of human color vision suitable for unsupervised chromatic equalization, due to Land and McCann, is receiving a reneved interest after several years. Different versions have been developed so far, and it has been used for various applications. Most of the implementations skip the classical random paths approach because of the high frequency chromatic noise it introduces. To solve the noise problem, avoiding the increase of path number and witout substituting the random path approach, we present in this paper two new retinex versions: one based on lookppup table transformation, and another based on multilevel image decomposition. These versions strongly decrease the dependency of the computed pixel value on the path randomness, eliminating in this way a great part of the chromatic noise.

5

Latest results in digital color film restoration

Chambah M., Besserer B., Courtellemont P.

Machine Graphics and Vision

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2002

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Vol. 11, No. 2/3

363-395

EN

Motion picture is not only a theatrical art but also a vivid record of past life. Unfortunately, almost all color films amde since the 1950s are subject to fading that can be arrested only by storing prints at very low temperatures. Photochemical restoration of faded motion prints is impossible. Nowadays, the improvement of computer power allows us to expect digital restoration of motion pictures as acceptable rates. In this paper we present two orginal techniques for restoring faded color image sequences: an assisted and an automated one. The first method consists, in choosing a "reference image" from the sequence, after removing side absorptions introduced by the scanning process, adjusting its colors and contrast, then propagating the correction performed to the whole image sequence. The second method consists in reviving the colors of the image (color enhancement), then balancing them.