Using neuroimaging, researchers are succesfully mapping neural connectivity and in the process creating vivid "brainbows."
By Amelio Vázquez-Reina, Won-Ki Jeong, Jeff Lichtman, Hanspeter Pfister, September 2011
Exploring Twitter and live events by structure and context can shed light on what people think.
By David A. Shamma, December 2010
Frameless Rendering (FR) is a rendering paradigm which performs stochastic temporal filtering by updating pixels in a random order, based on most recent available input data, and displaying them to the screen immediately [1]. This is a departure from frame-based approaches commonly experienced in interactive graphics. A typical interactive graphics session uses a single input state to compute an entire frame. This constrains the state to be known at the time the first pixel's value is computed. Frameless Rendering samples inputs many times during the interval which begins at the start of the first pixel's computation and ends with the last pixel's computation. Thus, Frameless Rendering performs temporal supersampling - it uses more samples over time. This results in an approximation to motion blur, both theoretically and perceptually.This paper explores this motion blur and its relationship to: camera open shutter time, current computer graphics motion-blur implementations, temporally anti-aliased images, and the Human Visual System's (HVS) motion smear quality (see 'quality' footnote) [2].Finally, we integrate existing research results to conjecture how Frameless Rendering can use knowledge of the Human Visual System's blurred retinal image to direct spatiotemporal sampling. In other words, we suggest importance sampling (see 'sampling' footnote) by prioritizing pixels for computation based on their importance to the visual system in discerning what is occurring in an interactive image sequence.
By Ellen J. Scher Zagier, May 1997
