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

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, in addition to low-level features such as color, luminance, and orientation. It generates a topographic saliency map for both the true color image and the PCA image, a difference map is then derived and used as a mask to select interesting locations where the PCA image has more salient features than available in the visible bands. The resulting representations preserve consistent natural appearance of the scene, while the selected attentional locations may be analyzed by more advanced algorithms

Dynamic Color Mapping of Bivariate Qualitative Data

Color is widely and reliably used to display the value of a single scalar variable. It is more rarely, and far less reliably, used to display multivariate data. Dynamic control over the parameters of the color mapping results in a more effective environment for the exploration of multivariate spatial distributions. This paper describes an empirical study comparing the effectiveness of static versus dynamic representations for the exploration of qualitative aspects of bivariate distributions. In this experiment, subjects made judgments about the correspondence of the shape, location, and magnitude of two patterns under conditions with varying amounts of random noise. Subjects made significantly more correct judgements (p < 0.001) about feature shape and relative positions using the dynamic representation, on average forty-five percent more. The differences between static and dynamic representations were greater in the presence of noise. 1