The calibration of color printers is highly influenced by optical scattering. Light scattered at microscopic level within printed papers induces a blurring phenomenon that affects the linearity of the tone reproduction curve. The induced nonlinearity is known as optical dotgain. Engeldrum and Pridham analyzed its impact on printing, using Oittinen's light scattering model. They determined the scattering and absorption coefficients based on spectral measurements of solid patches only. Their calibration achieves good independence of any printing irregularities. However, the microscopic knife-edge measurements of Arney et al showed that the model overestimates the influence of the absorption coefficient. Unlike Oittinen's model, we directly approach the laterally scattered light fluxes. This is achieved by an extended three-dimensional Kubelka-Munk model. We describe how to determine our coefficients using measurements of mere solid patches, which allows us to decouple the optical dot gain from other printing influences. Our improved model successfully corrects the observed overestimation and is able to predict Arney's microscopic measurements.

The random variation in gloss often observed in images produced in electrophotographic printers has been examined by an analytical technique that combines the capabilities of a microdensitometer with a goniophotometer. The technique is called microgoniophotometry and measures both the spatial and the angular distribution of the specular component of reflected light. The analysis provides information about the spatial variation of specularly reflected light at all angles through which the specular light is reflected, not just at the equal/opposite angle at which gloss is traditionally measured. The results of this analysis have lead to an optical model of the random spatial variation in gloss. The results indicate that dry toner is typically not completely fused and can be described as a surface composed of two distinct regions. These two regions differ in the extent of fusing that has occurred, as manifested by their differences in specular reflectance characteristics. The difference in reflectance is manifested primarily in their different angular distributions of specular light and also in their spatial frequency.

The work in this paper describes a method that can assist the process of print identification with respect to the printing machine that produced it. The method used high spatial resolution and low-noise digital image analysis to measure the sharpness, intensity and size characteristics of individual text characters. The relative variations of these variables were used to identify the machine that produced the print under examination. The results showed that three machines could be distinguished and one of these machines also showed differences in the print produced when the toner cartridge was changed.

This paper introduces objective macro and micro line registration quality metrics based on Moiré interference patterns generated by superposing a lenticular lens grating over a hardcopy test page consisting of high-frequency Ronchi rulings. Metrics for macro and micro line registration are defined and a measurement procedure is described to enhance the robustness of the metric computation over reasonable variations in the measurement process. The method analyzes low frequency interference patterns, which can be scanned at low resolutions. Experimental measurements on several printers are presented to demonstrate a comparative quality analysis. The metrics demonstrate robustness to small changes in the lenticular lens and grating superposition angle. For superposition angles varying between 2° and 5°, the coefficients of variance for the two metrics are less than 5%, which is small enough for delineating between test patterns of different print quality.

A basic task in the construction and use of a stereoscopic camera and display system is the alignment of the left and right images appropriately–a task generally referred to as camera convergence. Convergence of the real or virtual stereoscopic cameras can shift the range of portrayed depth to improve visual comfort, can adjust the disparity of targets to bring them nearer to the screen and reduce accommodation-vergence conflict, or can bring objects of interest into the binocular field of view. Although camera convergence is acknowledged as a useful function, there has been considerable debate over the transformation required. It is well known that rotational camera convergence or "toe-in" distorts the images in the two cameras producing patterns of horizontal and vertical disparities that can cause problems with fusion of the stereoscopic imagery. Behaviorally, similar retinal vertical disparity patterns are known to correlate with viewing distance and strongly affect perception of stereoscopic shape and depth. There has been little analysis of the implications of recent findings on vertical disparity processing for the design of stereoscopic camera and display systems. I ask how such distortions caused by camera convergence affect the ability to fuse and perceive stereoscopic images.

In the dental field, 3D tooth modeling, in which each tooth can be manipulated individually, is an essential component of the simulation of orthodontic surgery and treatment. However, in dental computerized tomography slices teeth are located closely together or inside alveolar bone having an intensity similar to that of teeth. This makes it difficult to individually segment a tooth before building its 3D model. Conventional methods such as the global threshold and snake algorithms fail to accurately extract the boundary of each tooth. In this paper, we present an improved contour extraction algorithm based on B-spline contour fitting using genetic algorithm. We propose a new fitting function incorporating the gradient direction information on the fitting contour to prevent it from invading the areas of other teeth or alveolar bone. Furthermore, to speed up the convergence to the best solution we use a novel adaptive probability for crossover and mutation in the evolutionary program of the genetic algorithm. Segmentation results for real dental images demonstrate that our method can accurately determine the boundary for individual teeth as well as its 3D model while other methods fail. Independent manipulation of each tooth model demonstrates the practical usage of our method.

This paper describes a new device characterization model applicable to plasma display panels (PDP). PDPs are inherently dissimilar to cathode ray tube and liquid crystal display devices, and so new techniques are needed to model their color characteristics. The intrinsic properties and distinct colorimetric characteristics are first introduced followed by model development. It was found that there was a large deviation in colorimetric additivity and a variation in color due to differences in the number of pixels in a color patch (pattern size). Three colorimetric characterization models, which define the relationship between the number of sustain pulses and CIE XYZ values, were successfully derived for full pattern size: A three-dimensional lookup table (3D-LUT) model, a single-step polynomial model and a two-step polynomial model including three 1D LUTs with a transformation matrix. The single-step and two-step polynomial models having more than 8 terms and the 3D LUT model produced the most accurate results. However, the single-step polynomial model was selected and extended to other pattern sizes because of its simplicity and good performance. Finally, a comprehensive model was proposed which can predict CIE XYZ at sizes different to that used for the training set. It was found that one combined training set formed using two different pattern sizes could give better results than a single-size training set.

Based on the concept of multimedia convergence, imaging devices, such as cameras, liquid crystal displays (LCDs), and beam projectors, are now built-in to mobile phones. As such, mobile cameras capture still images or moving pictures, then store them as digital files, making it possible for users to replay moving pictures and review captured still images. Increasingly, users want LCD in the mobile phone (we call it mobile LCD hereafter) to reproduce the same colors as the real scene. Accordingly, this paper proposes a method for color matching between mobile camera and mobile LCD that includes characterizing the mobile camera and mobile LCD, gamut mapping, camera noise reduction, and a 16-bit lookup table (LUT) design. First, to estimate the CIELAB values for the objects in the real scene, mobile camera characterization is achieved through polynomial regression of the optimal order determined by investigating the relation between captured RGB values and measured CIELAB values for a standard color chart. Thereafter, mobile LCD characterization is conducted based on 16-bit processing because of the reduced bit depth of the images displayed on a mobile LCD. In addition, a sigmoid model is used to find the luminance value corresponding to the RGB control signal, instead of using gain offset gamma and S-curve models due to the adjustment of luminance curve made by a system designer for preference color reproduction. After completing the two types of characterization, gamut mapping is performed to connect the source medium (mobile camera) with the target medium (mobile LCD), then a combination of sigmoid functions with different parameters to control the shape is applied to the luminance component of the gamut-mapped CIELAB values to reduce camera noise. Finally, a three-dimensional RGB LUT is constructed using 16-bit/pixel-based data to enable color matching for moving pictures and inserted into the mobile phone. Experimental results show that moving pictures transmitted by a mobile camera can be realistically reproduced on a mobile LCD without any additional computation or memory burden.

This paper reports on a simple novel concept of addressing the problem of underdetermination in linear spectral unmixing. Most conventional unmixing techniques fix the number of end-members on the dimensionality of the data, and none of them can derive multiple (2⁺) end-members from a single band. The concept overcomes the two limitations. Further, the concept creates a processing environment that allows any pixel to be unmixed without any sort of restrictions (e.g., minimum determinable fraction), impracticalities (e.g., negative fractions), or trade-offs (e.g., either positivity or unity sum) that may be associated with conventional unmixing techniques. The proposed mix-unmix concept is used to generate fraction images of four spectral classes from Landsat 7 ETM+data (aggregately resampled to 240 m) first principal component only. The correlation coefficients of the mix-unmix image fractions versus reference image fractions of the four end-members are 0.88, 0.80, 0.67, and 0.78.

This paper proposes a novel color shift model-based segmentation and fusion algorithm for digital autofocusing of color images. The source images are obtained using new multiple filter-aperture configurations. We shift color channels to change the focal point of the given image at different locations. For each respective location we then select the optimal focus information and, finally, use soft decision fusion and blending (SDFB) to obtain fully-focused images. The proposed autofocusing algorithm consists of: (i) color channel shifting and alignment for varying focal positions; (ii) optimal focus region selection and segmentation using sum modified Laplacian (SML); and (iii) SDFB, which enables smooth transition across region boundaries. By utilizing segmented images for different focal point locations, the SDFB algorithm can combine images with multiple, out-of-focus objects. Experimental results show performance and feasibility of the proposed algorithm for autofocusing images with one or more differently out-of-focus objects.