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Perceptual color scales for univariate and bivariate data display

Zhang, Hongqin; Montag, Ethan
Fonte: The Society for Imaging Science and Technology (IS&T) Publicador: The Society for Imaging Science and Technology (IS&T)
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
35.9%
Ten univariate and six bivariate color-encoding schemes were created within the perceptually uniform CIELAB color space. The effectiveness of these color scales was evaluated in three psychophysical experiments. Experiments I and II tested the ten univariate scales and Experiment III tested the six bivariate schemes. Experiments I and III were paired-comparison experiments in which observers judged the utility of the various renderings. Experiment II evaluated the scales by having observers judge the values of indicated points in the images. Experiments I and II demonstrated that the performance of Spectral L* and the three diverging color scales were significantly better than the other six. Experiment III showed that the constant hue plane scheme had a better rendering performance than the double cone and cylinder schemes. In both the double cone and cylinder schemes, the narrow hue range performed better than the one with wide range. There was no strong image dependency for univariate scales, but there was for the bivariate schemes.; This article may be accessed on the publisher's website (additional fees may apply) at: http://www.imaging.org/store/epub.cfm?abstrid=33706 It may also be accessed from the author's website (additional fees may apply) at: http://www.cis.rit.edu/people/faculty/montag/PDFs/Zhang&Montag2006%20ICIS.pdf

Color in scientific visualization: Perception and image-based data display

Zhang, Hongqin
Fonte: Rochester Instituto de Tecnologia Publicador: Rochester Instituto de Tecnologia
Tipo: Dissertação
Português
Relevância na Pesquisa
106.18%
Visualization is the transformation of information into a visual display that enhances users understanding and interpretation of the data. This thesis project has investigated the use of color and human vision modeling for visualization of image-based scientific data. Two preliminary psychophysical experiments were first conducted on uniform color patches to analyze the perception and understanding of different color attributes, which provided psychophysical evidence and guidance for the choice of color space/attributes for color encoding. Perceptual color scales were then designed for univariate and bivariate image data display and their effectiveness was evaluated through three psychophysical experiments. Some general guidelines were derived for effective color scales design. Extending to high-dimensional data, two visualization techniques were developed for hyperspectral imagery. The first approach takes advantage of the underlying relationships between PCA/ICA of hyperspectral images and the human opponent color model, and maps the first three PCs or ICs to several opponent color spaces including CIELAB, HSV, YCbCr, and YUV. The gray world assumption was adopted to automatically 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. The second approach combines a true color image and a PCA image based on a biologically inspired visual attention model that simulates the center-surround structure of visual receptive fields as the difference between fine and coarse scales. The model was extended to take into account human contrast sensitivity and include high-level information such as the second order statistical structure in the form of local variance map...