Color science has provided a wealth of research that is useful in mapping and visualization. Cartographers use work on perceptual color systems, color vision deficiencies, surround induction, color naming, printing and display, and conversion between color systems. From this grounding, we structure our symbols to represent real-world phenomena so they can be discovered and understood by map readers. Color offers a three-dimensional structure which can be used to organize symbols for multivariate mapping. Mapmakers do not always have the color specification skills for this type of analytical design work, but color schemes offered on the Web at ColorBrewer.org provide starting sets that are structured to match the basic organizations of map data: sequential, diverging, and qualitative schemes.
The field of radiology dates back to 1896 with the first direct x-ray exposure of film by Roentgen. From the next 80 years the exposure of black-and-white film by x-rays, and later, from calculated images from CAT scans, ultrasound scans, and MRI scans would dominate the hardcopy world of medical imaging. The uses of color were largely experimental and limited until the development of real-time color Doppler imaging in the early 1980s. Since that time, the uses of color have grown rapidly as 3D visualizations and multi-modality or multi-spectral images become widely utilized. The rapid growth of imaging techniques that combine anatomical information with additional functional or molecular information is driving color to the forefront, since additional information needs to be fused in the renderings. Thus, 100 years after Roentgen's experiment, a century of monochrome imaging is giving way to an emerging need for color displays of medical images.
Natural Vision (NV) system has been developed since 1999 as a Japan's national project managed under TAO (Telecommunication and Advancement Organization of Japan), which was reorganized into NICT (National Institute of information and Communication Center) from April, 2004. As NV can reproduce images with exactly the same color when we see with our naked eyes and also simulate how objects look under different illumination when objects are recorded by a multi-spectral camera, NV is expected to contribute a lot to EC, telemedicine, digital museum, etc. These applications are usually using color images and require an accurate color reproduction. However, the conventional system hardly reproduces exact color, because attention has been mainly paid on showing color images more beautifully rather than accurately. In other words, we may say that NV physically measures the color of objects, while the conventional one provides only colored images. In order to meet these requirements a national R and D project named NV has started in 1999 and has successfully shown its effectiveness.NV is an advanced color imaging system consisting of image capture, processing, archive, transmission, display and recognition, and deals with N (N>2) primary colors depending upon application requirements. As N increases, the accuracy of the color reproduction becomes higher on one hand, but the system becomes more complicated on the other hand. The low grade of the image acquisition device is the calibrated RBG based system such as a digital camera and the high level device is the multi-channel camera, which enables us to estimate the object reflectance function with information about the illumination light. And once we know the spectral reflectance function of the objects, we can change the illumination through a computer processing and show them on the well calibrated displays.Display device also has several grades. The low grade is the conventional three color display with calibration. As is well known, three primary colors of a conventional display form a color triangle and colors within the triangle could be reproduced accurately by using NV technology. Colors outside the triangle, however, cannot be reproduced by the conventional display, unless we use more saturated primary colors with enough brightness. In order to expand the color gamut, 4 primary color LCD display and 6 primary color projector are experimentally developed in the NV project. The expanded gamut as well as the accurate reproduction of the images is highly appreciated especially in the computer graphics.The history of the broadcast tells us that it began with monochrome, color and HD (High Definition) and that it recently moved into digital broadcast. This technological revolution suggests that NV could be a next generation key technology in both broadcast and internet fields. In this point of view NV format and technologies have to be well popularized in the market and accepted as international standards. Since NV has potential possibilities to be applied to and contribute a lot to many applications, much more efforts on basic research and development should be made. Researchers are very welcome to share the NV concepts.
Two psychophysical experiments have been conducted to analyze the perception and understanding of different color representations. Experiment I is a matching experiment using method of adjustment. Three different adjustment control methods were used. The results showed that the Lightness, Chroma, Hue (LCH) and Lightness, red/green, blue/yellow (LRGYB) adjustments elicited significantly better performance than the display RGB adjustment in terms of both precision and time, but were not significantly different from each other. Expert observers have significantly better performance than naive observers in terms of precision. Experiment II is a replication and extension of Melgosa, et al.'s judgment experiment. At a 95% confidence level, the results from judging difference were significantly better than those from judging similarity. Hue and Lightness were significantly more identifiable than Chroma, R/G, and Y/B. For all observers, lightness differences were more easily detected for less chromatic pairs than for higher chromatic ones. With respect to the size of the color differences, it was found that larger hue differences were more easily identifiable than smaller ones. For experts, in the case of large color differences, constant lightness and chroma were more identifiable, while in the case of small color differences, constant hue was more identifiable. There were no significant differences found between male and female.
A method of adjustment experiment was conducted in which observers matched the lightness of a test stimulus to the lightness of a reference stimulus. The reference stimulus and background were non-uniform and consisted of a blue noise pattern. The test stimulus and background were also non-uniform and consisted of a white noise pattern. In both cases, the background patterns were comprised of black and white pixels. The stimuli consisted of gray and either black or white pixels. Fourteen observers performed the experiment on a CRT in a dark surround. We found that there is an approximately 10 percent difference in the relative luminance for the matches between the white noise and blue noise conditions, and that this difference is statistically significant at a 95% confidence level. In this abstract, we discuss these non-equivalent backgrounds in relation with previous studies and also present some preliminary results for moving test stimuli.
Color appearance models can provide predictions of the perception for color, including visual phenomena such as chromatic adaptation. Color matching of a print with a monitor is one good example in which color appearance models play a significant role. Recently the performance for more complex and higher dynamic range stimuli has come to be required. This paper focuses on the problem of the simultaneous perception of lightness in a bipartite background. The perceived lightness of a pair of stimuli on the bipartite background was measured at various luminance and contrast levels and positions. The results are analyzed by using existing color appearance models. A concept of the global and local lightness is introduced to evaluate the perceived lightness.
There are two widely held theories of color constancy based on very different mechanisms: Chromatic Adaptation and Spatial Comparisons. Chromatic Adaptation is based on the change of retinal sensitivity in response to changes in incident light. The Spatial Comparisons mechanism is insensitive to illumination changes because it uses ratios of radiance from different pixels in the image. A spatially uniform increase in long-wave light increases both the numerator and the denominator by the same factor, so that the ratio remains constant. Spatial Comparisons of all pixels in the image synthesize a constant image, when the long-, middle-, and short-wave images are processed independently.Measurements of color appearance in constancy experiments have shown that there are small consistent departures from perfect constancy. This paper measures the color and magnitude of these departures from perfect color constancy. It tests the hypothesis that these departures provide a signature of the underlying constancy mechanism. Since Chromatic Adaptation mechanism is specific for illumination, then these departures are predicted to be the same, regardless of the color of the paper. Since the Spatial Comparisons mechanism is based on the Integrated Reflectance of the paper, gray papers should show greater constancy than colored papers. In other words, the signature of Chromatic Adaptation is constant departures for each illumination, while the signature of Spatial Comparisons is variable departures for each reflectance. This paper measures the color matches for a yellow, a purple and a gray paper in 27 different illuminants.
Colour constancy is a central problem for any visual system performing a task which requires stable perception of the colour world. To solve the colour constancy problem we estimate the colour of the prevailing light and then, at the second stage, remove it.Two of the most commonly used simple techniques for estimating the colour of the light are the Grey-World and Max-RGB algorithms. In this paper we begin by observing that this two colour constancy computations will respectively return the right answer if the average scene colour is grey or the maximum is white (and conversely, the degree of failure is proportional to the extent that these assumptions hold). We go on to ask the following question: “Would we perform better colour constancy by assuming the scene average is some shade of grey?”. We give a mathematical answer to this question. Firstly, we show that Max-RGB and Grey-World are two instantiations of Minkowski norm. Secondly, that for a large calibrated dataset L6 norm colour constancy works best overall (we have improved the performance achieved by a simple normalization based approach). Surprisingly we found performance to be similar to more elaborated algorithm.
We investigate the hypothesis, recently published in Nature, that the human visual system may use some sort of luminance-redness correlation together with the scene average for illuminant estimation. We found this idea interesting but not thoroughly tested. In particular, tests on real images were limited to scenes made up artificially from hyperspectral data, spectral power distributions of various daylight illuminants, and the human cone sensitivity functions. The Ruderman database of hyperspectral images is also quite peculiar because it consists of a small number of images of mostly foliage. Our experiments show that for scenes composed from a more diversified hyperspectral database combined with real illuminant spectra, the predicted correlation turns out to be very weak. For actual digital camera images, the luminance-redness correlation fails completely.
The technique of support vector regression is applied to the problem of estimating the chromaticity of the light illuminating a scene from a color histogram of an image of the scene. Illumination estimation is fundamental to white balancing digital color images and to understanding human color constancy. Under controlled experimental conditions, the support vector method is shown to perform better than the neural network and color by correlation methods.