From Time Machine
GigaPan Time Machine enables simultaneous exploration of space and time across massive datasets that could not previously be interactively explored at full spatial and temporal resolution. The architecture we have implemented on our servers scales easily to far greater resolution than the current five examples demonstrate.
We now openly invite partnerships so that we can, together with researchers around the world, create new time machines that enable interactive explorations by scientists and the public alike. Almost any content type with massive resolution is appropriate for new time machines, from simulation results such as the 9 billion year cosmology machine to natural phenomena such as the growth of a plant, cell division processes captured with gigapixel resolution microscopes and even high-speed hyper-resolved kinetics such as a ballet dancer’s pirouette. For real-world time lapse capture over hours, days and months, the GigaPan Pro hardware offers a solution that is readily available. The wiki page here describes how to program a DSLR and the Pro firmware to enable such time lapse capture. For high-speed capture, we envision the use of large-format digital cameras or videocameras – possibly multiple devices capturing in parallel.
Following your initial data capture process, we will work with your data to align the imagery across time, then post-process the entire registered time lapse into a Time Machine data format, suitable for real-time interactive viewing using HTML5 from any computer. Finally, your domain specialists can write both general annotations and spatio-temporal trajectories, called Time Warps, that lead viewers through guided tours in space-time. Please contact us firstname.lastname@example.org with questions and proposed collaborations.
Randy Sargent, Chris Bartley, Paul Dille, Jeff Keller, Rich LeGrand, Illah Nourbakhsh, “Timelapse GigaPan: Capturing, Sharing, and Exploring Timelapse Gigapixel Imagery”. Proceedings of the Fine International Conference on Gigapixel Imaging for Science, November 2010.