In this paper we document a simulation of the cat visual system intended to convey four of the major differences between the human and cat visual systems. Learning about an animal's visual system is an important step in understanding how that type of animal perceives the world around them, and how they behave within it. The cat visual system, for example, has been studied extensively by neuroscientists [20, 14, 1, 6], but the results of their work are difficult to convey using traditional text displays that are common in zoos, museums, and classrooms. We achieve some of our effects using fragment shaders applied as post-processes, enabling real-time simulation in either a game engine or live video feed. We also present Catalyst, an educational game that uses our simulation to teach players about the differences between human and cat vision. The tasks in Catalyst are based on the principle of situated cognition, and require the player to switch repeatedly between the two visual systems, thus emphasizing the contrast between them. The results of a user study suggest that Catalyst is successful in stimulating the interest of the players with regard to the material being presented, making it a suitable edutainment application for classrooms, zoos, or other educational settings.

We present an automatic, real-time video and image abstraction framework that abstracts imagery by modifying the contrast of visually important features, namely luminance and color opponency. We reduce contrast in low-contrast regions using an approximation to anisotropic diffusion, and artificially increase contrast in higher contrast regions with difference-of-Gaussian edges. The abstraction step is extensible and allows for artistic or data-driven control. Abstracted images can optionally be stylized using soft color quantization to create cartoon-like effects with good temporal coherence. Our framework design is highly parallel, allowing for a GPU-based, real-time implementation. We evaluate the effectiveness of our abstraction framework with a user-study and find that participants are faster at naming abstracted faces of known persons compared to photographs. Participants are also better at remembering abstracted images of arbitrary scenes in a memory task.

Visually important image features often disappear when color images are converted to grayscale. The algorithm introduced here reduces such losses by attempting to preserve the salient features of the color image. The Color2Gray algorithm is a 3-step process: 1) convert RGB inputs to a perceptually uniform CIE L*a*b* color space, 2) use chrominance and luminance differences to create grayscale target differences between nearby image pixels, and 3) solve an optimization problem designed to selectively modulate the grayscale representation as a function of the chroma variation of the source image. The Color2Gray results offer viewers salient information missing from previous grayscale image creation methods.