Hyperspectral image data can provide very fine spectral resolution with more than 200 bands, yet presents challenges for
visualization techniques for displaying such rich information on a tristimulus monitor. This study developed a
visualization technique by taking advantage of both the consistent natural appearance of a true color image and the
feature separation of a PCA image based on a biologically inspired visual attention model. The key part is to extract the
informative regions in the scene. The model takes into account human contrast sensitivity functions and generates a
topographic saliency map for both images. This is accomplished using a set of linear "center-surround" operations
simulating visual receptive fields as the difference between fine and coarse scales. A difference map between the
saliency map of the true color image and that of the PCA image is derived and used as a mask on the true color image to
select a small number of interesting locations where the PCA image has more salient features than available in the
visible bands. The resulting representations preserve hue for vegetation, water, road etc., while the selected attentional
locations may be analyzed by more advanced algorithms.
Many art objects have a size much larger than their softcopy reproductions. In order to develop a multiscale model that
accounts for the effect of image size on image appearance, a digital projector and LCD display were colorimetrically
characterized and used in a contrast matching experiment. At three different sizes and three levels of contrast and
luminance, a total of 63 images of noise patterns were rendered for both displays using three cosine log filters. Fourteen
observers adjusted mean luminance level and contrast of images on the projector screen to match the images displayed
on the LCD. The contrasts of the low frequency images on the screen were boosted while their mean luminance values
were decreased relative to the smaller LCD images. Conversely, the contrast of projected high frequency images were
reduced for the same images on LCD with a smaller size. The effect was more pronounced in the matching of projected
image to the smaller images on the LCD display. Compared to the mean luminance level of the LCD images, a reduction
of the mean luminance level of the adjusted images was observed for low frequency noise patterns. This decrease was
more pronounced for smaller images with lower contrast and high mean luminance level.
KEYWORDS: Independent component analysis, Principal component analysis, Visualization, Hyperspectral imaging, RGB color model, Associative arrays, Human vision and color perception, Color vision, Sensors, Colorimetry
This study investigated appropriate methodologies for displaying hyperspectral imagery based on knowledge of human color vision as applied to Hyperion and AVIRIS data. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) were used to reduce the data dimensionality in order to make the data more amenable to visualization in three-dimensional color space. In addition, these two methods were chosen because of their underlying relationships to the opponent color model of human color perception. PCA and ICA-based visualization strategies were then explored by mapping the first three PCs or ICs to several opponent color spaces including CIELAB, HSV, YCrCb, and YUV. The gray world assumption, which states that given an image with sufficient amount of color variations, the average color should be gray, was used to set the mapping origins. The rendered images are well color balanced and can offer a first look capability or initial classification for a wide variety of spectral scenes.
LCD televisions have LC response times and hold-type data cycles that contribute to the appearance of blur when objects are in motion on the screen. New algorithms based on studies of the human visual system's sensitivity to motion are being developed to compensate for these artifacts. This paper describes a series of experiments that incorporate eyetracking in the psychophysical determination of spatio-velocity contrast sensitivity in order to build on the 2D spatiovelocity contrast sensitivity function (CSF) model first described by Kelly and later refined by Daly. We explore whether the velocity of the eye has an additional effect on sensitivity and whether the model can be used to predict sensitivity to more complex stimuli. There were a total of five experiments performed in this research. The first four experiments utilized Gabor patterns with three different spatial and temporal frequencies and were used to investigate and/or populate the 2D spatio-velocity CSF. The fifth experiment utilized a disembodied edge and was used to validate the model. All experiments used a two interval forced choice (2IFC) method of constant stimuli guided by a QUEST routine to determine thresholds. The results showed that sensitivity to motion was determined by the retinal velocity produced by the Gabor patterns regardless of the type of motion of the eye. Based on the results of these experiments the parameters for the spatio-velocity CSF model were optimized to our experimental conditions.
The method of paired comparison based on Thurstone's case V of his law of comparative judgments is often used as a psychophysical method to derive interval scales of perceptual qualities in imaging applications. However, methods for determining confidence intervals and critical distances for significant differences have been elusive, leading some to abandon the simple analysis provided by Thurstone's formulation. Monte Carlo simulations of paired comparison experiments were performed in order to derive an empirical formula for determining error. The results show that the variation in the distribution of experimental results can be well predicted as a function of stimulus number and the number of observations. Using these results, confidence intervals and critical values for comparisons can be made using traditional statistical methods.
The method of paired comparison is often used in experiments where perceptual scale values for a collection of stimuli are desired, such as in experiments analyzing image quality. Thurstone's Case V of his Law of Comparative Judgments is often used as the basis for analyzing data produced in paired comparison experiments. However, methods for determining confidence intervals and critical distances for significant differences based on Thurstone's Law have been elusive leading some to abandon the simple analysis provided by Thurstone's formulation. In order to provide insight into this problem of determining error, Monte Carlo simulations of paired comparison experiments were performed based on the assumptions of uniformly normal, independent, and uncorrelated responses from stimulus pair presentations. The results from these multiple simulations show that the variation in the distribution of experimental results of paired comparison experiments can be well predicted as a function of stimulus number and the number of observations. Using these results, confidence intervals and critical values for comparisons can be made using traditional statistical methods. In addition the results from simulations can be used to analyze goodness-of-fit techniques.
Two psychophysical experiments were performed scaling overall image quality of black-and-white electrophotographic (EP) images. Six different printers were used to generate the images. There were six different scenes included in the experiment, representing photographs, business graphics, and test-targets. The two experiments were split into a paired-comparison experiment examining overall image quality, and a triad experiment judging overall similarity and dissimilarity of the printed images. The paired-comparison experiment was analyzed using Thurstone's Law, to generate an interval scale of quality, and with dual scaling, to determine the independent dimensions used for categorical scaling. The triad experiment was analyzed using multidimensional scaling to generate a psychological stimulus space. The psychophysical results indicated that the image quality was judged mainly along one dimension and that the relationships among the images can be described with a single dimension in most cases. Regression of various physical measurements of the images to the paired comparison results showed that a small number of physical attributes of the images could be correlated with the psychophysical scale of image quality. However, global image difference metrics did not correlate well with image quality.
This past fall the Center for Imaging Science initiated a distance learning option for its Masters Degree in Imaging Science. This program is identical to the local version of the degree except for th fact that students take the course at a distance. Initially, the program offered a specialization track in Color Imaging but now the program includes Remote Sensing and Digital Image Processing tracks. My course, Vision & Psychophysics, was one of the first courses to go online. The model we have used for this endeavor is an asynchronous one; students may take the courses anywhere and learn on their own schedule. Judging by the experience of the instructors and the feedback form our students, we feel that this endeavor has been a success. In this paper I will describe my experience in designing implementing, and teaching a distance-learning course. The goal is to facilitate others who may be considering teaching in this way by sharing my limited experience.
In order to systematically evaluate different gamut mapping algorithms, we have simulated gamut mapping on a CRT using simple rendered images of colored spheres floating in front of a gray background. Using CIELab as our device-independent color space, cut-off values for lightness and chroma, based on the statistics of the images, were chosen to reduce the gamuts for the test images. The gamut mapping algorithms consisted of combination of clipping and linearly mapping the original gamut in piecewise segments. Complete color space compression in RGB and CIELAB was also used. Each of the colored originals (R,G,B,C,M,Y, and Skin) were mapped separately in lightness and chroma. In addition, each algorithm was implemented with saturation (C*/L*) allowed to vary or remain constant. Using a paired-comparison paradigm, pairs of test images with reduced color gamuts were presented to twenty subjects along with the original image. For each pair the subjects chose the test images that better reproduced the original. Rank orders and interval scales of algorithm performance with confidence limits were then derived. Certain algorithms were found to perform best consistently over image color. For chroma mapping, clipping of all out-of-gamut colors while keeping lightness constant was the most preferred method. For lightness mapping at the top of the gamut, a particular piecewise mapping technique while keeping saturation constant was preferred. For lightness mapping at the bottom the results gave an indication of the type of algorithm that might be best while keeping chroma constant. The choice of device-independent color space may also influence the choice of gamut mapping algorithm.
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