Institute for Data Analysis and Visualization

We present the general idea of using common tools from the field of scientific visualization to aid in the design, implementation and testing of computer vision algorithms, as a complementary and educational component to purely mathematics-based algorithms and results. The interaction between these two broad disciplines has been basically non-existent in the literature, and through initial work we have been able to show the benefits of merging visualization techniques into vision for analyzing patterns in computed parameters. Specific examples and initial results are discussed, such as scalar field-based renderings for scene reconstruction uncertainty and sensitivity analysis as well as feature tracking summaries, followed by a discussion on proposed future work.