This project will investigate the use of perceptually-motivated image metrics across a range of important applications in computer graphics. A wide variety of tasks in computer graphics assume some measure of similarity in appearance between two images. Such tasks include level-of-detail generation, raster font design, cartographic element placement, texture synthesis and shape transformation. Too often the metrics used in such work are poor at reflecting what a human would visually perceive for the given task. This project will re-visit several areas within computer graphics and will apply visually-based metrics to a number of applications with the goal of producing better images. Replacing the techniques that are used in existing applications with perceptually-based measures of image similarity has the potential to improve the results in a wide range of important applications. Applications that can benefit from the use of image metrics include: - Simulators for pilot and driver training. - Computer aided design of mechanical parts. - Automated map-making. - Raster font creation. - Interactive walkthroughs of large building interiors for architectural review - Comparison between "model" organs and the MRI or CT data of a medical patient - Vector and tensor field visualization for applications such as weather prediction and stress/strain simulations of mechanical and architectural structures. This project will investigate using techniques from image processing and computer vision to help define image metrics for particular tasks. Some of the properties that these metrics may incorporate are: sensitivity to edge strength and orientation, frequency responses at a number of scales, relative rather than absolute measures of intensity and color within an image, and the masking effects that noise has on perception. Once a metric is defined, an optimization procedure will be used to minimize the differences between the generated image or geom etry and the target object. The choice of optimization technique will depend on the application and on the form of the image metric. Computationally expensive optimization methods may be acceptable for off-line tasks. Where speed is essential, heuristics may be suggested by observations of the optimization routine while it is executing. The educational goals of this project are to introduce both undergraduate and graduate students to computer science research, to expose the community outside of academia to important applications of computer graphics, and to disseminate the programs, libraries and datasets that are developed by this project to other researchers worldwide. Students will be involved in research both through the computer graphics classes that are being developed and by working within a research group. ***
