The spectral response of color systems isolates long-wave information about the world into its long-wave channel; as well the system isolates middle- and short-wave information into their channels. Crosstalk is the dilution of primary channel information with unwanted information from other channels (long-wave channel response to middle- and short-wave information, etc.). Even though Maxwell invented both 3-color photography and the color matching functions of human vision, these fields have standardized on entirely different classes of sensitivity functions. Image Makers use narrow band, minimaly overlapping, sensitivities to minimze crosstalk between the color information found in different spectral regions. Human vision, limited by the photochemistry of visual pigments, uses sensitivities with considerable spectral overlap at the early receptor level and employs post-receptor processing to achieve remarkable properties not usually found in image reproduction. The specific properties of spectral crosstalk provide an important signature of how humans achieve color constancy.

This paper proposes an accurate, compact, and generic method for representing spectral functions. The focus is on smooth functions, the case of most natural spectra. While pursuing the idea of using Fourier series expansion for its advantage in representation generality, we attempt to remove the problem of Gibbs phenomenon. The solution that we propose is a new method called symmetric extension. Given a smooth spectral function S1, we first generate a new function S2 which is a mirror reflection of S1 about the upper bound of the wavelength domain. Then we create another function U that merges S1 and S2, and apply Fourier expansion to U. Because the values of U at its boundaries are equal, Gibbs oscillation is largely reduced. Besides, since U is self symmetric, all sine terms in Fourier expansion vanish and therefore we only need to keep the cosine coefficients. These make our method not only accurate, but also compact. We have tested the method with a large number of real spectra of various types, and compared with the existing methods such as direct Fourier expansion and linear model. The numerical results have confirmed the advantages of the proposed method.

In real world no color exists. Only spectral light distributions interact to form the final color sensation. This paper presents preliminary experiments whose purpose is to test the robustness of a spatial color computation in relation to changes in the acquisition of spectral information. The basic idea is that human vision system has evolved into a robust system to acquire visual information, in this case the color, adapting to varying illumination conditions to guarantee color constancy. The presented experiments test changes in the output of a Retinex-derived tone mapping operator, varying illuminants and color matching function curves. Synthetic high dynamic range multispectral images have been computed by a photometric ray tracer using different illuminants. Then, using standard and modified color matching functions, a set of high dynamic range RGB images has been created. This set has been converted to standard RGB images using a linear tone mapping algorithm with no spatial color computation and one based on Retinex, performing a spatial color normalization. A discussion of the results is presented.

We estimated and analyzed the reflectance spectra of human skin at each pixel taken by a multi-band imaging system, and compared it with the RGB three bands imaging system. The multi-band camera can capture more than three bands, and estimate the spectral reflectance using more than three eigenvectors of principal component. In this paper, we implemented a six bands camera system, in which color of the light source is modulated for rapid measurement. The system consists of the RGB three bands digital camera and two different light sources which are strobe light and strobe light with optical filter. We captured the skin twice using each light source respectively. As a result, we could obtain six color bands images. We estimated the spectral reflectance image based on Wiener estimation method, by using the captured multi-band image and the RGB image respectively. Then, we analyzed and evaluated two spectral reflectance images by using principal component analysis. The result shows that that the reflectance spectra estimated from three-band image do not have enough accuracy to analyze the skin color quantitatively.

Calibration targets are widely used to characterize imaging devices and estimate optimal profiles to map the response of one device to the space of another. The question addressed in this paper is that of how many surfaces in a calibration target are needed to account for the whole target perfectly. To accurately answer this question we first note that the reflectance spectra space is closed and convex. Hence the extreme points of the convexhull of the data encloses the whole target. It is thus sufficient to use the extreme points to represent the whole set. Further, we introduce a volume projection algorithm to reduce the extremes to a user defined number of surfaces such that the remaining surfaces are more important, i.e. account for a larger number of surfaces, than the rest. When testing our algorithm using the Munsell book of colors of 1269 reflectances we found that as few as 110 surfaces were sufficient to account for the rest of the data and as few as 3 surfaces accounted for 86% of the volume of the whole set.

In many applications, it is requested to compute surface color appearance under different illuminants. If multispectral information about surface reflectance is available, the calculus of tristimulus values under illuminants of specified SPD is straightforward. When only colorimetric information is known, the illuminant change is performed adopting transforms based on the Von Kries scaling model (i.e. the Bradford transform). Unfortunately, the three dimensional colorimetric space may be limited for properly computing the change in appearance of colors due to a change of illuminant. In this paper, a solution to this problem is presented. Assumption of the proposed method is that the problem is specific for a domain of colors, and that this domain can be modeled in a three dimensional Gaussian space. Given the domain Gaussian space, colors may be represented through synthesized reflectance spectra. Exploiting synthesized reflectance is proved to be an effective strategy to implement an illuminant change transform.

In this paper, we propose a computer-assisted approach for spectral design and synthesis. This approach starts with some initial spectrum, modifies it interactively, evaluates the change, and decides the optimal spectrum. Given a requested change as function of wavelength, we model the change function using a Gaussian function. When there is the metameric constraint, from the Gaussian function of request change, we propose a method to generate the change function such that the result spectrum has the same color as the initial spectrum. We have tested the proposed method with different initial spectra and change functions, and implemented an interactive graphics environment for spectral design and synthesis. The proposed approach and graphics implementation for spectral design and synthesis can be helpful for a number of applications such as lighting of building interiors, textile coloration, and pigment development of automobile paints, and spectral computer graphics.

If digital cameras and scanners are to be used for colour measurement it is necessary to correct their device responses to device-independent colour co-ordinates, such as CIE tristimulus values. In order to do this it is sufficient to recover the underlying spectral reflectance functions from a scene at each pixel. Traditionally, linear methods are used to transform device responses to reflectance values. Recently, however, several non-linear methods have been applied to this problem, including generic methods such as neural networks, more novel approaches such as sub-manifold approximation and approaches based upon quadratic programming. In this paper we apply polynomial models to the recovery of reflectance. We perform a number of simulations with both tri-chromatic and multispectral imaging systems to determine their accuracy and generalisation performance. We find that, although higher order polynomials seem to be superior to linear methods in terms of accuracy, the generalisation performance for the two methods is approximately equivalent. This suggests that the advantage of polynomial models may only be seen when the training and test data are statistically similar. Furthermore, the experiments with multispectral systems suggest that the improvement using high order polynomials on training data is reduced when the number of sensors is increased.

This paper describes application of the amplitude-modulation (AM)-coded spectral matching imager (SMI) to real-time detection of natural objects such as human beings, animals, vegetables, or geological objects or phenomena, which are much more liable to change with time than artificial products while often exhibiting characteristic spectral functions associated with some specific activity states. The AM-SMI produces correlation between spectral functions of the object and a reference at each pixel of the correlation image sensor (CIS) in every frame, based on orthogonal amplitude modulation (AM) of each spectral channel and simultaneous demodulation of all channels on the CIS. This principle makes the SMI suitable to monitoring dynamic behavior of natural objects in real-time by looking at a particular spectral reflectance or transmittance function. A twelve-channel multispectral light source was developed with improved spatial uniformity of spectral irradiance compared to a previous one. Experimental results of spectral matching imaging of human skin and vegetable leaves are demonstrated, as well as a preliminary feasibility test of imaging a reflective object using a test color chart.

The acquisition of large or high-resolution multispectral images may require that different parts of the scene be acquired separately and then be mosaicked to obtain the whole image. While the problem of stitching together parts of an image to form a consistent whole has been studied rather extensively for traditional images, in this case view angles are typically wide, geometrical distorsions are significant, and shots are often markedly misaligned with one another. On the other hand, many current applications for multispectral acquisition present a different scenario, as view angles are narrow, shots follow a much more precise alignment, and the quality of the resulting mosaic may be sensitive to even the tiniest registration errors, depending on the context. Moreover, given the nature of multispectral imaging, precision in color reproduction is usually much more important than it is when dealing with traditional images. All these issues raise the question of whether traditional strategies will work for typical multispectral images too. In this paper, we report an experience in multispectral mosaicking. This experience was a first attempt at developing a mosaicking framework for multispectral images, including a suitable outline of the flow of operations as well as the selection of methods and procedures for geometrical registration and color synchronization. The scenario was that of an overhung camera acquiring images from above, with lighting provided by lamps used in professional photography; this can be seen as a typical studio setup, half-way between laboratory tests and 'field usage'. The performances of our framework in terms of quality of the resulting mosaics, resource requirements, and processing time, were evaluated through tests based on real acquisitions of different images.

Imaging devices operate at the physical interfaces corresponding to image capture and reproduction. The combination of physical insight and mathematical signal processing tools, therefore, offers unique advantages in solving problems in imaging systems. In this paper, we illustrate and support this idea using examples from our research on imaging, where the combination of physical insight, mathematical tools, and engineering ingenuity leads to elegant and effective solutions.

A natural color mapping method has been previously proposed that matches the statistical properties (mean and standard deviation) of night-vision (NV) imagery to those of a daylight color image (manually selected as the "target" color distribution). Thus the rendered NV image appears to resemble the target image in terms of colors. However, in this prior method the colored NV image may appear unnatural if the target image's "global" color statistics are too different from that of the night vision scene (e.g., it would appear to have too much green if much more foliage was contained in the target image). Consequently, a new "local coloring" method is presented in the current paper, and functions to render the NV image segment-by-segment by using a histogram matching technique. Specifically, a false-color image (source image) is formed by assigning multi-band NV images to three RGB (red, green and blue) channels. A nonlinear diffusion filter is then applied to the false-colored image to reduce the number of colors. The final grayscale image segments are obtained by using clustering and merging techniques. The statistical matching procedure is merged with the histogram matching procedure to assure that the source image more closely resembles the target image with respect to color. Instead of using a single target color image, the mean, standard deviation and histogram distribution of a set of natural scene images are used as the target color properties for each color scheme. Corresponding to the source region segments, the target color schemes are grouped by their scene contents (or colors) such as green plants, roads, ground/earth. In our experiments, five pairs of night-vision images were initially analyzed, and the images that were colored (segment-by-segment) by the proposed "local coloring" method are shown to be much more natural, realistic, and colorful when compared with those produced by the "global-coloring" method.

Multi-channel imaging gains more and more applications because of its advantage in better color reproduction and better spectral representation to avoid metamerism problem. Illuminant estimation for multi-channel images is not widely studied because most illuminant estimation methods are applied to trichromatic images. In this paper, some common illuminant estimation methods such as gray world and maximum RGB are extended to multi-channel images. Five methods are evaluated for multi-channel images including gray world, maximum RGB, Maloney-Wandell method, modified illuminant detection in linear space and reflectance constraint illuminant detection. The methods are evaluated in terms of illuminant detection efficiency through estimating the illuminant correlated color temperature. Among them, the method of reflectance constraint illuminant detection has the best efficiency. In addition, the former three methods, which were only used in illuminant estimation for three-channel images before, are attempted in illuminant spectral recovery. The recovery efficiencies are evaluated through comparing the difference between the recovered spectral distributions and the original ones. Maloney-Wandell method has large efficiency improvement when the number of channels increases from three to four. It also has the best spectral recovery among the three tested methods when the channel number is more than three.

A method is proposed for estimating fluorescent scene illumination with spiky spectrum by using a spectral imaging system. The system is composed of a LCT filter, a monochrome CCD camera, and a personal computer. The gray world assumption is used for estimating the illuminant spectrum from the camera outputs. It is possible to infer the material type of fluorescent light source by knowing the wavelengths of spikes on the estimated illuminant spectrum. A procedure is proposed for classifying scene illuminants into two steps; the gross classification of classifying the illuminant into three groups and the detailed classification of determining a unique fluorescent type. Algorithms are developed for peak detection using the second derivative spectrum for the gross classification and sensor correlation using the background continuum for the detailed classification. Finally, the feasibility of the proposed method is demonstrated in an experiment using six real fluorescent lamps.

To preserve museum collections of works of art, these collections are often photographed for display in digital museums. However, conventional photography cannot capture spectral characteristics of objects. In this paper, we describe a 16-band camera system designed to produce spectral images of ancient paintings. Results of color reproduction of captured images and results of spectral analysis of images of ancient paintings are also presented. The camera consists of a 2000×2000-pixel CCD, a rotational filter turret with 16 interference filters, and a PC-based image capturing and displaying unit. The camera's lens is interchangeable, and it enables two or more different view sizes. Each band image of the camera can be focused independently, and it reduces longitudinal chromatic aberration. A stroboscope is used for lighting, and the rotational filter turret and electrical shutter of the CCD have been synchronized with it. An electric motor-driven photographic platform is used to enable photographing large objects in several shots. We evaluated the results of color estimation for an image taken by this camera using the GretagMacbeth ColorChecker 24-color chart. The average ΔE_ab was 2.09 (maximum ΔE_ab was 4.03). Spectral reflectance were used to analyze a degraded area on an ancient painting.

An image capturing system for the reproduction of high-fidelity color color was developed and a set of three optical filters were designed for this purpose. Simulation was performed on the SOCS database containing the spectral reflectance data of various objects in the range of wavelength of 400nm ~ 700nm in order to calculate the CIELAB color difference ΔE_ab. The average color difference was found to be 1.049. The camera was mounted with the filters and color photographs of all the 24 color patches of the Macbeth chart were taken. The measured tristimulus values of the patches were compared with those of the digital images captured by the camera. The average ΔE_ab was found to be 5.916.

Images recorded by digital cameras are invariably distorted by errors of CCD sensors and a series of camera operations in the imaging process. The distortion sources include noise, geometrical distortion, gamma correction, intensity and chromatic bias, blurring, etc. However, the true signal of the incident light needs to be known in many situations. In addition to traditional visual computing tasks such as shape from shading, color constancy, and photometric stereo, acquiring a large natural image database in which each image has a high-dynamic range and is carefully calibrated to reflect the true signal of the incident light is essential to human vision research. We recently acquired such a database containing 1600 images using an Olympus C2040 digital camera and explained a series of color perceptual phenomena based on the statistics of these images. We introduced in this paper the techniques we used for calibrating and acquiring this database, which include the methods to correct the spatial falloff, non-linearity, spectral bias, blurring, and noises and to obtain high-dynamic range for each image. The techniques presented here can be used in acquiring similar databases for a wide range of human vision and computer vision research fields.

This paper presents a short historical perspective of color reproduction technology, where we are at present, and what challenges lie ahead. Today prevalent color management systems are based on pixel-wise processing of color data represented by 3 channels. We believe this does not adequately reproduce the color experience for the user, and that additional dimensions need to be considered. Three such dimensions are discussed: spectral, spatial, and goniometric. We believe the spectral representation plays an important role in color measurement and modeling; however a spectral image representation is likely not to be a mainstream technology due to the high cost incurred, and modest benefits that are appreciated only in certain niche applications. The incorporation of spatial context is likely to be of significant immediate benefit in commercial color imaging applications. Finally, the use of goniometric information in color reproduction is in its infancy, and bears promise. A brief overview of research efforts in each area is presented.

We propose a new technique to reproduce faithfully both the color and the gloss of an object on a computer, using multispectral images. An imaging spectrograph equipped with a monochrome charge-coupled device (CCD) camera is fixed in front of the target object. Multispectral images of a linear portion of the object's surface are captured at suitable intervals by a measuring system which comprises a light source orbiting the target object. To obtain spectral images for the whole surface, the target object is also rotated. The reflection is separated into diffuse and specular components, according to the dichromatic reflection model, and the diffuse parameters are estimated at 5-nm intervals between 380nm and 780nm for each pixel. Since the CCD camera used to capture images has a limited dynamic range, we suppose that the specular reflection is independent of wavelength for the dielectrics, and that the specular reflections are saturated, although some of them can be non-saturated. We adopt the Torrance-Sparrow reflectance model for the specular reflection, and estimate the specular parameters using the least squares method for each pixel. Our experimental results reveal that the diffuse parameters for the color and the specular parameters for the gloss of the target object are satisfactorily estimated.

The majority of papers illustrating the advantages of multispectral approaches in color imaging consider metamerism reduction as the main reason of turning, in the next years, from colorimetric imaging and reproduction to multispectral. Doubtless, if spectral match is the reproduction criterion, and if this is accomplished with sufficient accuracy, originals and reproductions match, visually, even if viewing conditions change. It follows that, in practical applications, the spectral-based approach is advantageous whenever the viewing conditions are unknown. From a theoretical viewpoint, metamerism-free reproductions have a considerable advantage vis-à-vis traditional prints. In practice, the measurability of such advantage rests on the possibility of a comparison between the original image and the corresponding reproduction, which, in turn, should quantify the ability of the observer to discover the mismatch. In this work, we compare results of colorimetric and spectral-based reproductions of single color patches in print. We consider a four ink printer, which we characterize colorimetrically and spectrally. Then, we perform a set of experiments. Test colors are reproduced using colorimetric and multispectral strategies. We compare the results obtained through measurements. The aim of our work is to discuss possible advantages or disadvantages of the spectral-based reproduction with respect to colorimetric reproduction for single colors.

Color management allows the deterministic handling of color data from input to output. One off the fundamental problems of such system is the definition of the appropriate color profile on creation. This is especially difficult in situations, where the input is scanned on a color scanner. In ssuch cases, the user has to correctly classify the input source - be it offset, xerography, inkjet, AgX - so that the correct scanner calibration can be used. This paper explores ways to eliminate the step and shows how an estimate of heh input can be generated from the originally scanned input data, without the need for additional sensors or more than 3 color bands.

A method is proposed for performing spectral gamut mapping, whereby spectral images can be altered to fit within an approximation of the spectral gamut of an output device. Principal component analysis (PCA) is performed on the spectral data, in order to reduce the dimensionality of the space in which the method is applied. The convex hull of the spectral device measurements in this space is computed, and the intersection between the gamut surface and a line from the center of the gamut towards the position of a given spectral reflectance curve is found. By moving the spectra that are outside the spectral gamut towards the center until the gamut is encountered, a spectral gamut mapping algorithm is defined. The spectral gamut is visualized by approximating the intersection of the gamut and a 2-dimensional plane. The resulting outline is shown along with the center of the gamut and the position of a spectral reflectance curve. The spectral gamut mapping algorithm is applied to spectral data from the Macbeth Color Checker and test images, and initial results show that the amount of clipping increases with the number of dimensions used.

We present a new method for the computation of both, image and device gamut boundaries. The method has been designed to bypass the quality vs. time trade off that one usually faces when computing gamut boundaries. This trade off is between the geometric accuracy of the boundary and the time it takes to compute it. Our method is geometrically accurate in the sense that the computed gamut boundary tightly encloses the color points that make up the gamut. At the same time it is fast compared to other methods. Thus it can be used in an image-dependent gamut mapping approach. The underlying concept of the presented method is a data structure that we call discrete flow complex which is derived from the discrete distance function to the color points. We have implemented the method and tested it with a suite of test images. Our experimental results show that the method is in fact fast and geometrically accurate. In the future we plan to use the gamut boundaries computed by our method for fast, high-quality, image-dependent gamut mapping in three dimensions.

Color gamut mapping has been recognized as one of the key elements for accurate image reproduction. A variety of gamut mapping algorithms (GMAs) has been proposed to minimize the image differences between originals and their reproductions across different media. However, the use of a single-GMA is normally unable to fulfill all types of image reproductions. Therefore, we doubt if it’s a right way to further improve the performance of a single algorithm while increasing its complexity. In this study, we propose an optimal multi-GMAs approach based on image analysis to select an appropriate GMA from a range of LUT-type GMAs to give the image the best treatment at low-computational cost. Because there are too many image statistics can be extracted from an image, the present study introduces methods to select the principal statistics for the GMA selection. To save the cost for real-time image analysis, the importance of scanning patterns and image compression was investigated as well. The results suggested that the performance of the above applications would be enhanced to a certain level if we choose appropriate image statistics for calculation.

The design of a gamut mapping algorithm (GMA) is always a compromise between preserving different competing aspects such as color, contrast and lightness. A natural requirement to a GMA is that the algorithmic treatment of this competition has to avoid any additional artefacts such as discontinuities or loss of contrast. In this paper several common gamut mapping algorithms are studied from this aspect, resulting in the observation that problems with geometric discontinuities are widespread. For the assessment of the phenomena induced by local mapping properties, an algorithmic test was developed and applied. This new test supports both, the quality check of existing as well as the development of new GMAs. Finally we present a first new algorithm designed to have a good behavior concerning continuity and contrast conservation which also performs well in classical psychophysical tests.

Though an incredible range of colors are perceptible to the human eye, there has been a notable tendency across human cultures to make similar classifications and identifications of colors, with some interesting exceptions. Among societies that only have three basic color terms (such as in New Guinea): “light, dark, and red” appear to be the universal distinctions employed by these societies with simple color systems (if a fourth distinction is made, the new addition will universally be “yellow” or “green/blue”; a fifth distinction will provide the alternate of the fourth; a sixth distinction will separate “blue” and “green,” etc.). Such patterns in human color definition can be understood at biological, evolutionary, and cultural levels. This presentation will review some current understandings about the relationship between human biology, evolutionary history, and the nature of color in both modern and prehistoric human culture – to highlight the biological reasons why some colors and color distinctions are more significant than others, at a universal (pan-human) level.

We sometimes can recognize the color which should not be expressed by the ink set of only two colors. Remarkable example is seen in prints made with ink set of cyan (C) and magenta (M); they look like rather regular full-color prints and missing yellow (Y) color is often recognized in the prints. We have reconfirmed quantitatively that humans can perceive "impossible" colors in prints that were created with just two colors: most subjects perceived "yellow" in those prints. We suppose that our natural color constancy works when we recognize two-tone color prints. We attempted to explain the reason by considering the von Kreis model, which describes the chromatic adaptation to changes of lighting conditions. Using the von Kreis model for C+M-prints showed good estimation of the original color of the reference C+M+Y-print. We have confirmed that this effect is likely to be due to a compensation mechanism that is similar to the one that allows color recognition under a colored light. We conclude from this result that C+M-prints tend to look like full color prints because we can easily compensate for the missing color in C+M-prints by only using the linear gain-control mechanism at our natural vision system.

Mixed Raster Contents (MRC) is a powerful image representation concept in achieving high compression ratios while maintaining high reconstructed image quality. The Multiple Extracted Constant Color Areas (MECCA) model, which is composed of one contone background layer and multiple constant color foreground layers has the advantages of its ease of decomposition and inherent text enhancement and noise reduction features. This paper presents a segmentation algorithm that extracts uniform text and other uniform color objects that carry detail information. The algorithm consists of four steps. First the test and objects are extracted from the image. Next, they are tested for color constancy and other features to decide if they should be represented by the MRC foreground layers. The objects that are chosen are then clustered in color space. The image is finally segmented such that each foreground layer codes the objects from the same color cluster.

In this paper we present and compare two linear techniques for image sequence enhancement speed up that transform image contrast and color considering mainly the spatial relationship between the image areas. The first technique called LLL for Linear Local LUT is a local technique based on a Look Up Table transformation. The second technique (called PC2D) is a global technique based on a color mapping between some key zones of the original and corrected image. The need for speed up technique is especially important when processing high definition images and live videos. To test and compare the performance of the two proposed methods we have chosen the ACE (Automatic Color Equalization) technique, an unsupervised color equalization algorithm. We applied the techniques to the fields of digital cinema and digital film restoration (images with high definition) and underwater aquarium videos (live videos).

In this paper, a new segmentation method is proposed. This method is based on the residuals of morphological opening and closing transforms with a geodesic metric. It may be considered analogous to a region growing technique. However, in contrast to using statistical local properties like in region growing approaches, the proposed method uses a pixel similarity rule based on the morphological characteristic of the connected components in the image. The proposed method is particularly well suited for the segmentation of complex image scenes such as aerial or fine-resolution images where very thin, enveloped and/or nested regions may have to be retained, and where the gradient calculation has a major drawback.

Remotely sensed satellite imagery of an earthquake-affected area can significantly assist in estimating the severity of infrastructure damage. Modern high-resolution satellite systems have been launched to provide users optical or Synthetic Aperture radar (SAR) data with sub-meter accuracy, which enable the possibility of sensing damage for individual infrastructure by means of pre- and post-event imagery. Herein, we focus our study on the region of Bam, Iran, which was devastated by a moment magnitude Mw = 6.6 earthquake on December 26, 2003, causing approximately 43,200 lives lost. To recognize houses within the Bam region before the earthquake, the boundary of houses are located using a combination of morphological gray-level open and intensity threshold operators. The unique aspect of this paper, as demonstrated with satellite imagery data from this event, is the use of an probabilistic framework for determining the optimal combination of morphological and intensity threshold parameters, which results in an estimated ground truth (EGT). By overlaying the EGT obtained from images before the earthquake with images of the same region after the earthquake, two statistical damage indices, including a new boundary-compactness based index proposed in this study, are compared. This comparison is presented using easily interpretable damage maps, where individual houses are rendered with colors representing the severity of damage.

This paper describes a new automatic color thresholding based on wavelet denoising and color clustering with K-means in order to segment text information in a camera-based image. A particular focus is given on stroke analysis to improve character segmentation, the step which follows color thresholding. Several parameters bring different information and this paper tries to explain how to use this complementarity. It is mainly based on the discrimination between two kinds of backgrounds: clean or complex. On one hand, this separation is useful to apply a particular algorithm on each of these cases and on the other hand to decrease the computation time for clean cases for which a faster method could be considered. Finally, several experiments were done to discuss results and to conclude that the use of a discrimination between kinds of backgrounds gives better results in terms of Precision and Recall. This separation of backgrounds is done with supervised classification. After tests with several classifiers (linear and quadratic discriminant analysis, K-nearest neighbors, neural networks and support vector machines), best results are given with a set of features based on properties of the gray-level histogram and by using a support vector machine.

A new ISO standard for visual displays: "Ergonomic requirements and measurement techniques for electronic visual displays" is soon to be released as a Draft International Standard. The core of the new standard is the part with generic ergonomic requirements for visual displays. Three parts of the standard describe three types of measurements: electro-optical ones, to be used in general; user performance test methods, for innovative displays for which no electro-optical methods exist; and field assessment methods, to be used outside of the laboratory, under the conditions of use at the workplace. The last part of the standard describes five compliance routes and procedures for five different display technologies and contexts of use. A number of choices and problems that standard writers have to face are mentioned. Should a visual display standard be written primarily for young users, with mostly a high visual acuity and in possession of their full accommodative power, wanting to use tiny hand-held displays featuring very small characters ? Or should the standard be focused on the elderly, with their reduction in visual faculties, barring the use of small characters that may irritate or disable such older users ? The question how to put human factors principles in standards sometimes seems a battle between idealists and realists. It therefore is important to strike a balance between different attitudes, backgrounds and interests in a standards writing committee - as indeed happens in ISO/TC 159/SC 4/WG 2, "Visual Display Requirements". The author is convener of this Working Group.

The theoretical approach is introduced to design the optimal chromaticities for primaries of a display with a given size of triangular color gamut in xy-plane. Optimal primaries are defined as a set of chromaticities of red, green and blue primaries with fixed white point that most optimally satisfying four criteria, i.e. gamut size, gamut shape, coverage of object colors and hue of the primaries, in the visually uniform color space, CIECAM02. It is assumed that the optimal gamut should cover that of sRGB and have similar maximum chroma for each hue. The number of SOCS data located outside the gamut is used as a criterion to judge the coverage of object colors. Also it is set the hues of primaries to be close to those of sRGB. The simulation results showed that the optimal primaries for 85% of NTSC area have similar points with sRGB for red and blue, and green primary is located in between sRGB and NTSC. For 100% of NTSC area, the optimal chromaticities are located near those of NTSC for red and green and that of sRGB for blue.

Authors have developed a wide color gamut and high brightness WUXGA LCD monitor with color calibrator. This monitor uses three color LED, including red, green and blue, as the light source for the backlighting. This framework involves a problem that the chromaticity points of white color (white balance) shifts over time due to the changes in the wavelength of emitted beams and emission luminance (efficiency) of the LEDs, which work as the light source, depending on the temperature． It also has a tendency to deteriorate in color non-uniformity performance to some extent because of the variation of luminance of individual LED elements and other factors compared to the backlighting of light source of conventional Cold Cathode Fluorescent light (CCFL). Authors have develop a color calibrator that has: 1) a feedback control circuit via optical sensors in which backlighting is incorporated, 2) a color non-uniformity control through modulation of image signals, and 3) a gamma-correction function, in order to solve this problem and to enable introduction of this device into applications that require strict color control. This research was organized in part by the New Energy and Industrial Technology Development Organization, Japan (NEDO).

The drive for larger size, higher spatial resolution, and wider aperture LCD has shown to increase the electrical crosstalk between electrodes in the driver circuit. This crosstalk leads to additivity errors in color LCD. In this paper, the LCD color crosstalk was modeled using a capacitance coupling mode and the crosstalk effect was analyzed with micrographs captured from an imaging colorimeter. The experimental result reveals the subpixel nature of color crosstalk that whenever any two neighboring subpixels are “on” at the same time, there is crosstalk from one subpixel to another, but whenever there is one “off” subpixel between the two “on” subpixels, there is no crosstalk between the “on” subpixels. There is positive crosstalk from right to left across all three subpixels. Based on this crosstalk model, the crosstalk of a LCD was characterized and a spatial subpixel crosstalk correction algorithm was developed to improve the color performance of LCD. The correction algorithm reduced crosstalk by a factor of 16. Compared to a 3D lookup table approach, the new algorithm is easier to implement and more accurate in performance.

The recent color display market is being focused on displays with larger panel size and larger color gamut. More specifically, for widening the color gamut, multi-primary display (MPD), which is a display having more than the conventional three-channels, has been an important issue. However, developing MPD faces many difficulties such as sustaining display luminance against 3-color displays, embodying a simple H/W structure, and assigning new color signals based on input RGB signals. The purpose of this study is to propose a method to display color on a new pixel structure for flat-panel displays with six primary colors intended to expand the color gamut, and to discuss a color decomposition algorithm for this new structure to assign signals to 6-channel from an input RGB signal. Special interests of this study are made on minimizing the deterioration of color image quality compared with RGB-based display and maximizing the usage of the widened color gamut. The result of this study was implemented on a prototype 6-color LCD and verified generation of the color signals for 6-color without luminance degradation. The device is capable of reproducing colors like emerald cyan, which cannot be displayed on a conventional RGB display. In addition, hardware implementation on FPGA verified commercial viability of the algorithm.

Multiprimary displays are needed to reproduce the most of visible color. However, while using four or more primary colors, it is difficult to convert color uniquely, because multiprimary displays have variability of color conversion. Even though multiprimary colors can be converted, there are some false edges in the reproduced image by using the most of the current conversion methods. We introduce linear programming to the color conversion in order to get high accuracy and eliminate false edges. Although the linear programming can help multiprimary displays to reproduce images without false edges, it takes long time to convert high-definition images. Hence, a fast color conversion method that keeps the advantage of the linear programming is proposed. This method is constructed of decision tree and linear programming. Because the decision tree is a discrete classification method, color conversion by the decision tree must be implemented as a discrete classification problem. Therefore, we feed the results of the conversion by linear programming to the decision tree. As the result of the conversion by the method, fast color conversion was obtained in comparison with that obtained only by using linear programming. In addition, our method almost eliminated the false edges in the reproduced image.

This paper proposes a color decomposition method for a multi-primary display (MPD) using a 3-dimensional look-up-table (3D-LUT) in linearized LAB space. The proposed method decomposes the conventional three primary colors into multi-primary control values for a display device under the constraints of tristimulus matching. To reproduce images on an MPD, the color signals are estimated from a device-independent color space, such as CIEXYZ and CIELAB. In this paper, linearized LAB space is used due to its linearity and additivity in color conversion. First, the proposed method constructs a 3-D LUT containing gamut boundary information to calculate the color signals for the MPD in linearized LAB space. For the image reproduction, standard RGB or CIEXYZ is transformed to linearized LAB, then the hue and chroma are computed with reference to the 3D-LUT. In linearized LAB space, the color signals for a gamut boundary point are calculated to have the same lightness and hue as the input point. Also, the color signals for a point on the gray axis are calculated to have the same lightness as the input point. Based on the gamut boundary points and input point, the color signals for the input point are then obtained using the chroma ratio divided by the chroma of the gamut boundary point. In particular, for a change of hue, the neighboring boundary points are also employed. As a result, the proposed method guarantees color signal continuity and computational efficiency, and requires less memory.

Projection displays generally do not reproduce colors evenly at different locations of the display. Depending on the display technology, the non-uniformity may be in luminance only, typically due to optical effects in the lens, or in all color dimensions of luminance, chroma and hue. Even though this non-uniformity often remains unnoticed by the user, for certain applications such as tiling/stitching of projection displays, the non-uniformity is an important problem. In this study we investigate the feasibility of using an inexpensive webcam to correct the projection display non-uniformity. Two main approaches are proposed and evaluated, one using colorimetric characterization of camera and display, and another closed-loop approach. Both approaches are based on displaying images that should ideally have a uniform color distribution, capturing the displayed images with the webcam, and using these captured images to create a correction function, which is then applied to images in order to correct them. Our results show that the feasibility of the proposed methods depends heavily on the qualities of the equipment involved. For standard low-end webcams it is generally difficult to obtain reliable device-independent color measurements needed for the colorimetric characterization approach, but the direct approach still gives reasonable results.

As large-scale direct view TV screens such as LCD flat panels or plasma displays become more and more affordable, consumers not only expect to buy a ‘big screen’ but to also get ‘great picture quality’. To enjoy the big picture, its viewing distance is significantly reduced. Consequently, more artifacts related to digital compression techniques show above the threshold of visual detectability. The artifact that caught our attention can be noticed within uniform color patches. It presents itself as ‘color blobs’ or color pixel clustering. We analyze the artifact’s color characteristics in RGB and CIELAB color spaces and underline them by re-synthesizing an artificial color patch. To reduce the visibility of the artifact, we elaborate several linear methods, such as low pass filtering and additive white gaussian noise and verify, whether they could correct or mask the visible artifacts. From the huge list of nonlinear filter methods we analyze the effect of high frequency dithering and pixel shuffling, also based on the idea that spatial visual masking should dominate signal correction. By applying shuffling, we generate artificial high frequency components within the uniform color patch. As a result, the artifact characteristics change significantly and its visibility is strongly reduced.

We developed the method to adjust a display to a predetermined state and to make ICC display profiles with human eyes. In our method, in order to measure the tone reproduction curve of a display device, the brightness and hue of a continuous tone image is adjusted to the brightness and hue of a binary image. In addition, for measuring the complex curve, three pairs of images having different values of brightness are used. Furthermore, the color of the display white is adjusted to the color of a white object under the viewing illuminant to adjust the display white for any viewing illuminant. To bear out the color matching accuracy of our method, two kinds of experiments were carried out for three LCDs, and three subjects. First, we measured the chroma of gray. The result was that the chroma of the display adjusted by our method was smaller than the chroma of the un-adjusted display. Second, we simulated the color of DDCP on the display and measured the matching accuracy. Then, our method could reduce the color difference between both devices. These results show the display adjusted by our method has enough accuracy to use for soft proofing.

Due to the drop size variation of the print heads in inkjet printers, consistent color reproduction becomes challenge for high quality color printing. To improve the color consistency, we developed a method and system to characterize a pair of printers using a colorimeter or a color scanner. Different from prior known approaches that simply try to match colors of one printer to the other without considering the gamut differences, we first constructed an overlapped gamut in which colors can be produced by both printers, and then characterized both printers using a pair of 3-D or 4-D lookup tables (LUT) to produce same colors limited to the overlapped gamut. Each LUT converts nominal device color values into engine-dependent device color values limited to the overlapped gamut. Compared to traditional approaches, the color calibration accuracy is significantly improved. This method can be simply extended to calibrate more than two engines. In a color imaging system that includes a scanner and more than one print engine, this method improves the color consistency very effectively without increasing hardware costs. A few examples for applying this method are: 1) one-pass bi-directional inkjet printing; 2) a printer with two or more sets of pens for printing; and 3) a system embedded with a pair of printers (the number of printers could be easily incremented).

Closed loop printer color calibration is a process to ensure consistent color reproduction. For an ink jet printer, the color rendering result may vary due to the manufacturing variations in ink drop weight, ink chemistry, and environmental effects of temperature and humidity on printing process. In this paper, we present a fast printer color calibration method which is designed specifically for mid- to low-end printers. A new linearization method has been proposed. Using this method, we can reduce the number of color patches used for color calibration, whereas maintain the high calibration accuracy. The basic idea is that, for each primary ink (CMYK or CMYKcm), instead of printing and measuring the whole color ramp to generate the linearization table online, we measure only one color patch to estimate the ink drop weight. Then by using a set of pre-build linearization tables, the final linearization table is obtained by interpolating the pre-build tables. We show how to select the ink jet pens with different drop weights to pre-build the linearization tables. We show also how to estimate the ink drop weight and compute the final linearization table by interpolation.

The use of four process inks (CMYK) is common practice in the graphic arts, and provides the foundation for many output device technologies. In commercial applications the number of inks are sometimes extended beyond the process inks depending on the customers’ requirements, and cost constraints. In inkjet printing extra inks have been used to both extend the color gamut, and/or improve the image quality in the highlight regions by using "light" inks. The addition of "light" inks are sometimes treated as an extension of the existing Cyan or Magenta inks, with the Cyan tone scale smoothly transitioning from the light to the dark ink as the required density increases, or are sometimes treated independently. If one is to treat the light ink as an extension of the dark ink, a simple blend can work well where the light and dark inks fall at the same hue angle, but will exhibit problems if the light and dark inks hues deviate significantly. The method documented in this paper addresses the problem where the hues of the light and dark inks are significantly different. An ink interaction model is built for the light and dark inks, then a composite primary is constructed that smoothly transitions from the light ink to dark ink, preventing the blended ink from over inking, while ensuring a smooth transition in lightness, chroma, and hue. The method was developed and tested on an XES (Xerox Engineering Systems) ColorGraphx X2 printer, on multiple substrates, and found to provide superior results to the alternative linear blending techniques.

A new image restoration technique to enhance details in enlarged images is presented. The proposed algorithm obtains a new enlarged image from a single traditionally zoomed frame, which is post-processed to enhance details. Enhancement is obtained through a weighted blending, based on a local measure of contrast, between two zoomed versions of the original picture: one optimized for low contrast and one optimized for details . The weights for blending are chosen according to the local contrast value: the bi-cubic image weights more when the contrast decreases, on the contrary the second frame is more relevant when the contrast increases. The blending process is straightforward, for this reason one has to avoid any artefacts in each of the source images: they can affect irremediably the final result. It is hence crucial, especially in the frame optimized for details, to enhance edges and corners with a minimum error of localization. To this aim, we propose to use for the blending an image enhanced by an adaptive filtering like anisotropic diffusion. In particular, we have obtained good results, both in term of efficiency and of quality of the final image, using in blending a new offset-smoothing technique based on the USAN principle.

A 3-D lookup table (LUT) approach has been developed for color separation for color inkjet printers to better control the color separation and ink limit. This method starts from the color separation for critical points, followed by 1-D interpolation to determine the color separation for grid points in critical lines, followed by 2-D interpolations to determine the color separation for grid points in critical planes, followed by 3-D interpolation to determine the color separation for the remaining grid points in the 3-D LUT. To control the spreading of K and high-density ink to some regions, such as the highlight region and the flesh tone region, we start from controlling the ink propagation in line interpolation, which is a fairly easy step, then control the ink propagation in planes, and finally control the ink propagation in 3-D interpolation. With this process, a 3-D LUT for the conversion from a printer RGB space or a virtual CMY space to an n-colorant space is built to maximize the printer gamut and to minimize grain. This paper describes the details of special interpolations to fill the grids in a 3-D color separation LUT, which includes controlling ink propagation in grids on selected lines, followed by special interpolations to minimize grain in plane-interpolation, followed by special interpolations to fill the remaining grids in the 3-D LUT.

Dot gain is different when dots are printed alone, printed in superposition with one ink or printed in superposition with two inks. In addition, the dot gain may also differ depending on which solid ink the considered halftone layer is superposed. In a previous research project, we developed a model for computing the effective surface coverage of a dot according to its superposition conditions. In the present contribution, we improve the Yule-Nielsen modified Neugebauer model by integrating into it our effective dot surface coverage computation model. Calibration of the reproduction curves mapping nominal to effective surface coverages in every superposition condition is carried out by fitting effective dot surfaces which minimize the sum of square differences between the measured reflection density spectra and reflection density spectra predicted according to the Yule-Nielsen modified Neugebauer model. In order to predict the reflection spectrum of a patch, its known nominal surface coverage values are converted into effective coverage values by weighting the contributions from different reproduction curves according to the weights of the contributing superposition conditions. We analyze the colorimetric prediction improvement brought by our extended dot surface coverage model for clustered-dot offset prints, thermal transfer prints and ink-jet prints. The color differences induced by the differences between measured reflection spectra and reflection spectra predicted according to the new dot surface estimation model are quantified on 729 different cyan, magenta, yellow patches covering the full color gamut. As a reference, these differences are also computed for the classical Yule-Nielsen modified spectral Neugebauer model incorporating a single halftone reproduction curve for each ink. Taking into account dot surface coverages according to different superposition conditions considerably improves the predictions of the Yule-Nielsen modified Neugebauer model. In the case of offset prints, the mean difference between predictions and measurements expressed in CIE-LAB CIE-94 ΔE₉₄ values is reduced at 100 lpi from 1.54 to 0.90 (accuracy improvement factor: 1.7) and at 150 lpi it is reduced from 1.87 to 1.00 (accuracy improvement factor: 1.8). Similar improvements have been observed for a thermal transfer printer at 600 dpi, at lineatures of 50 and 75 lpi. In the case of an ink-jet printer at 600 dpi, the mean ΔE₉₄ value is reduced at 75 lpi from 3.03 to 0.90 (accuracy improvement factor: 3.4) and at 100 lpi from 3.08 to 0.91 (accuracy improvement factor: 3.4).

This paper describes a high-performance scalable SIMD digital signal processor (DSP) developed for multifunctional printer systems. The proposed DSP supports a variable number of datapaths to cover a wide range of performance and maintains a RISC-like pipeline structure. Many special instructions suitable for image processing algorithms are included in the DSP. Various quad/dual instructions are supported to pack four data of byte length or two data of half-word length into one word length form. To process image data that have various bit-lengths, the proposed DSP is able to perform bit-field extraction/insertion before and after ALU operations. The DSP supports up to 4 extraction/insertion patterns at the same time, and one of them can be specified in the related instruction. Conditional instructions are supported to deal with complex relative conditions efficiently. In addition, an intelligent DMA block is integrated to align data in the course of data reading, because the alignment function can be implemented with a little hardware. Experimental results on typical image processing algorithms show that the proposed SIMD DSP is two times effective in performance compared to a commercial high-end DSP.

During the definition of a printed product in an MIS system, a lot of attention is paid to the production process. Considerably less attention is paid to the content management of the products since this does not have an immediate impact on the production costs (assuming that the number of inks or plates is known in advance). Special care must be taken when considering versioned products. With versioned products we here mean different products that have a number of pages or page layers in common. Typical examples are comic books that have to be printed in different languages. In this case, the color plates can be shared over the different versions and the black plate will be different. Other examples are nation-wide magazines or newspapers that have an area with regional pages or advertising leaflets in different languages or currencies. In this paper, we will present a way to manage different versions of a printed product. First, we will introduce a data model for version management. Next, we will show how the content of the different versions can be supplied by the customer and how the different versions can be derived. In the second part of the paper, we will show how the data model can be integrated in the workflow. Then we indicate how the supplied information can be used to determine an optimal process plan that shows the number of plates to be generated and the press plans indicating plate changes and press runs.

The presentation will demonstrate how through alternative methods of digital print production the Centre for Fine Print Research (CFPR) is developing methodologies for digital printing that attempt to move beyond standard reproductive print methods. Profiling is used for input and output hardware, along with bespoke profiling for fine art printmaking papers. Examples of artist's work, and examples from the Perpetual Portfolio are included - an artist in residence scheme for selected artists wanting to work at the Centre and to make a large-format digital print. Colour is an important issue: colour fidelity, colour density on paper, colour that can be achieved through multiple-pass printing. Research is also underway to test colour shortfalls in the current inkjet ink range, and to extend colour through the use of traditional printing inks.

Digital photography is no longer simply a successor to film. The market for picture capturing devices is booming and therefore the household penetration is growing rapidly. The digital market is now driven by additional devices such as mobile phones with camera and video functions (camphones) as well as innovative products derived from digital files. A large number of consumers do not print their images and non-printing has become the major enemy of wholesale printers, home printing suppliers and retailers. There is a need to archive all of these images taken with digital still cameras (DSCs) and/or camphones in addition to store it on a storage device. Beside the classical prints which are still the best long time archive there new products offering additional convenience to the consumer. This paper addresses the challenge facing our industry, namely how to encourage the consumer to print images easily and conveniently from all types of digital media. Software solutions to order via the internet and mobile phones are presented in combination with products based on classical silver halide and electro photography.

Linear Pixel Shuffling (LPS) dithering produces blue-noise-like patterns, but the placement of thresholds in a dither matrix is a result of an exact algebra, rather than iterative procedure -- as is usually the case. In this paper, we investigate the potential use of LPS for construction of color (CMYK) dithering masks. In case of LPS dithering, the addition of the same value to each mask threshold, using modular arithmetic, is equivalent to the spatial mask shift. We propose a set of three shifted color masks for C, M, and Y that we construct from the original LPS mask using modular arithmetic. The main advantage of this approach is its simplicity. These shifts can be "tailored" to the statistical properties of the image and the set of new screens can be calculated on the fly. The proposed method enables creation of screens of arbitrary size, since the dithering masks are tiled automatically (actually, the masks are of unlimited size). The number of gray levels in each screen is limited by the choice of a modulus number used for mask thresholds calculation. This enables us to use a virtually unlimited number of thresholds that are not necessarily linearly related to the LPS calculated matrix values. Thus, it is relatively easy to construct a non-linear dither screen that will compensate for any printer non-linearity.

This paper proposes an adaptive error diffusion algorithm for text enhancement followed by an efficient text segmentation that uses the maximum gradient difference (MGD). The gradients are calculated along with scan lines, then the MGD values are filled within a local window to merge text segments. If the value is above a threshold, the pixel is considered as potential text. Isolated segments are then eliminated in a non-text region filtering process. After the text segmentation, a conventional error diffusion method is applied to the background, while edge enhancement error diffusion is used for the text. Since it is inevitable that visually objectionable artifacts are generated when using two different halftoning algorithms, gradual dilation is proposed to minimize the boundary artifacts in the segmented text blocks before halftoning. Sharpening based on the gradually dilated text region (GDTR) then prevents the printing of successive dots around the text region boundaries. The method is extended to halftone color images to sharpen the text regions. The proposed adaptive error diffusion algorithm involves color halftoning that controls the amount of edge enhancement using a general error filter. However, edge enhancement unfortunately produces color distortion, as edge enhancement and color difference are trade-offs. The multiplicative edge enhancement parameters are selected based on the amount of edge sharpening and color difference. Plus, an additional error factor is introduced to reduce the dot elimination artifact generated by the edge enhancement error diffusion. In experiments, the text of a scanned image was sharper when using the proposed algorithm than with conventional error diffusion without changing the background.

Color-to-color misregistration refers to misregistration between color separations in a printed or display image. Such misregistration in printed halftoned images can result in several image defects, a primary one being shifts in average color. The present paper examines the variation in average color for two-color halftoned images as a function of color-to-color misregistration distance. Dot-on-dot/dot-off-dot and rotated dot screen configurations are examined via simulation and supported by print measurements. The color and color shifts were calculated using a spectral Neugebauer model for the underlying simulations. As expected, dot-on-dot/dot-off-dot color shifts were very high, while rotated dots screens exhibited very little color shift under the present idealized conditions. The simulations also demonstrate that optical dot gain significantly reduces the color shifts seen in practice.

Quantization of the accumulated diffused error (ADE) is an effective means to reduce on-chip storage in a hardware implementation of error diffusion. A simple uniform quantizer can yield a factor of 2 savings with no apparent loss in image quality. Nonuniform quantizers with memory that depend on the quantizer index or various features13 can yield even greater savings -- up to a factor of 4, with essentially no loss in image quality. However, these quantizers depend on the trainability of the tone-dependent error diffusion (TDED) framework to achieve this level of quality. In addition, the design of the quantizers must be coupled to that of the TDED parameters in either a sequential or iterative fashion.

We describe and implement an algorithmic framework for memory efficient, 'on-the-fly' halftoning in a progressive transmission environment. Instead of a conventional approach which repeatedly recalls the continuous tone image from memory and subsequently halftones it for display, the proposed method achieves significant memory efficiency by storing only the halftoned image and updating it in response to additional information received through progressive transmission. Thus the method requires only a single frame-buffer of bits for storage of the displayed binary image and no additional storage is required for the contone data. The additional image data received through progressive transmission is accommodated through in-place updates of the buffer. The method is thus particularly advantageous for high resolution bi-level displays where it can result in significant savings in memory. The proposed framework is implemented using a suitable multi-resolution, multi-level modification of error diffusion that is motivated by the presence of a single binary frame-buffer. Aggregates of individual display bits constitute the multiple output levels at a given resolution. This creates a natural progression of increasing resolution with decreasing bit-depth.

The CRT has the nonlinear light intensity response to the input digital values. However, light intensity curve of the PDP is close to linear within the normal operating range. Thus, in order to generate images on the PDP being equivalent to those on the CRT, the modification on the input digital values should be made. This process is often called as inverse gamma correction. When the inverse gamma correction is applied, the number of displayable gray levels in dark areas is considerably reduced. It would result in false contours that reduce the quality of displayed images. Error diffusion can be applied to solve this problem. However, the luminance variation between two consecutive integer values is much greater than 'just noticeable difference' of human vision system. It makes minor pixels appear as isolated dots. In this paper, an error diffusion algorithm is proposed to reduce the luminance variation. Also, three algorithms called in this paper as 'Fraction doubling', 'Minor pixel separation' and 'Within-frame minor pixel allocation' are proposed.

Glossmark technology is a halftone-based digital imaging process to embed visible watermarks into xerographic color prints. The gloss of a xerographic print depends not only on surface roughness of paper and toner, but also on the microscopic structure created by the halftone process. The surface relief of a halftone image can be treated as a two-dimensional phase grating. The shape, or profile, of the surface relief determines the reflected pattern of the illumination. A strong angular differential gloss can be obtained by employing two anisotropic halftone screens in the halftone process. A careful design of these screens enables embedding Glossmark images while maintaining the high quality of the color reproduction. The printing process, that simultaneously creates high quality primary and Glossmark images in a single step, requires neither special equipment nor special paper or toner. Glossmark images, shown in a high contrast of gloss, provide a perfect simulation of the traditional paper watermarks, while their digital implementation makes it easy to embed variable data as digital watermarks into individual documents.

DNA microarray are an experimental technology which consists in arrays of thousands of discrete DNA sequences that are printed on glass microscope slides and allows the monitoring of expressions for tens of thousands of genes simultaneously. Image analysis is an important aspect for microarray experiments that can affect subsequent analysis such as identification of differentially expressed genes. The aim of this step is to extract the gene expression data included in the spots image. Imge processing for microarray images includes three tasks: spot gridding, segmentation and information extraction. In this study, we address the segmentation and information extraction problems, and propose a new segmentation method and a new background and foreground segmentation correction method for accurate information extraction. The initial segmentation is based on minimum error thresholding under the assumption that the probability density distribution of spot image and background image satisfies Gaussian and the final results is obtained through refining initial segmentation by Bayes decision theory. The advantage of our method is that it does not have any restrictions on the spot shape. We compare our experimental results with those obtained from the widely used software GenePix.

The importance of digital maps increases continuously. Unfortunately, it is costly and time consuming to create a digital map out of the void. If we can successfully vectorize scanned paper maps, digital map production will be much easier and previous map resources can be well utilized. The first step of map vectorization is color clustering. Currently there exist a number of fast and efficient algorithms for automatic color clustering. However, they may not work well for maps of enormous noises. Here we introduce two interactive color clustering algorithms: color clustering with pre-calculated index colors (PCIC) and color clustering with pre-calculated color ranges (PCCR). A number of experiments are conducted to investigate into the performance of the two approaches. Finally, we introduce a novel and efficient vectorization algorithm, which can perform the entire vectorization algorithm in one pass of linear time.

This paper discusses a method to neutralize the influence of printed colors using the additive color mixing model. The method is developed for Picture Illusion by Overlap, an image hiding method for printed materials which requires no computation in revealing the hidden image. In Picture Illusion by Overlap, a color image is hidden into two different color images. When the two images are printed onto slides and stacked together, the hidden image appears to the viewers' eyes. The color neutralization enables the image hiding operation by reducing the influence of the hidden pixels within the two images. Some experiments are made to verify the effect of the color neutralization method and the illusion scheme.

Artificial object identification and image classification are two basic issues in remote sensing (RS) information extraction. All kinds of methods, from the pixel-based to window-based, have been tried respectively in two domains for many years, but the accuracy is not well until now. Two obvious limitations explain the reasons. One is that the processing cell can’t correspond with the true target in the real world, and the other is the feature, which participates in the identification procedure, is far from enough to describe the intrinsical characteristics of the object of interest. During recent two years, an object-oriented classification method is put forward to supply these gaps of the conventional classification method. On the one hand, by using the segmentation technique, the pixel clusters are extracted based on their similarities to form so-called object having thematic meaning; on the other hand, vectorization of these objects is performed by integrating the GIS (geographical information system) idea into the RS which makes it possible to describe the various features of each object, such as shape information and its spatial relationship to neighboring object. In this study, the authors attempt to use this new method to artificial object identification by taking example for ship extraction with the help of one spectral feature and eight shape features. Results indicate the object-oriented classification is feasible in practice, and it opens a new way for artificial object extraction.

In human retina, rod cells contribute to scotopic or dim-light vision and cone cells to photopic or bright-light vision. Excitation of the cone cells leads to the perception of color while that of rod cells helps in perception of various shades of gray. At low levels of illumination, only the rod cells are excited and gray shades are perceived. As the illumination level increases, more and more cone cells are excited and actual colors are perceived. The HSV model separates out the luminance component of a pixel color from its chrominance components, which is similar to the human perception of color. Hue represents pure colors, which are perceived by the excitation of cone cells. Saturation gives a measure of the degree by which a pure color is diluted by white light. Using the results of these analyses, we determine relative importance of hue and intensity based on the saturation of an image pixel. We effectively apply this method to the generation of a color histogram where each pixel contributes to gray color and true color. Using a weight function, percentage of true and gray color contributions of a pixel are measured. This information is used for content-based image retrieval applications.

Histogram warping is a novel histogram specification technique for use in color image processing. As a general purpose tool for color correction, our technique constructs a global color mapping function in order to transform the colors of a source image to match a target color distribution to any desired degree of accuracy. To reduce the risk of color distortion, the transformation takes place in an image dependent color space, featuring perceptually uniform color axes with statistically independent chromatic components. Eliminating the coherence between the color axes enables the transformation to operate independently on each color axis. Deforming the source color distribution to reproduce the dominant color features of the target distribution, the histogram warping process is controlled by designating the color shifts and contrast adjustments for a set of key colors. Assisted by mode detection, matching quantiles establish the correspondence between the color distributions. Interpolation by monotonic splines serves to extend the mapping over the entire dynamic range without introducing artificial discontinuities into the resulting color density. We show how our method can be applied to color histogram equalization as well as color transfer from an example image or a color palette.