CS Talk
Voicu Popescu, Purdue University
Title: Image Generalization Through Camera Model Design
Zoom Link: https://yale.zoom.us/j/95740385111?pwd=aHQwOFlhb01GTkZKT0hKcEYwN2g1UT09
Host: Holly Rushmeier
Abstract:
Most computer graphics and visualization applications rely on images rendered with the planar pinhole camera, which approximates the human eye. Whereas for some applications it is important to generate images that closely resemble what users would actually see if they explored a physical replica of the dataset, for many other applications the constraints of the planar pinhole camera are unnecessarily restrictive. In this talk we give an overview of our work to remove the uniform sampling rate and the single viewpoint constraints of conventional images. The image generalization is implemented through an intervention at the camera model level. We present the camera model design paradigm that abandons the conventional rigidity of the camera model in favor of designing and optimizing the camera model for each application, for each dataset, and for each view. The resulting generalized image is more effective than a conventional image, yet it remains easy to compute, continuous, and non-redundant. We illustrate the benefits of image generalization in a wide range of applications such as focus+context, remote, and multiperspective visualization, visibility computation, rendering acceleration, virtual and augmented reality navigation, and diminished reality.
Bio:
Voicu Popescu is an Associate Professor of Computer Science at Purdue University. He has a Computer Science Ph.D. degree from the University of North Carolina at Chapel Hill, and a Computer Science B.Sc. degree from the Technical University of Cluj-Napoca, Romania. His research interests lie at the confluence of computer graphics, visualization, computer human interaction, and computer vision, with applications in defense, healthcare, and education.