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University of Cambridge > Talks.cam > Machine Learning Journal Club > Speed dependent automatic zooming for browsing large documents
Speed dependent automatic zooming for browsing large documentsAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact Emli-Mari Nel. The abstract below is from the paper by Igarashi and Hinckley available at http://www-ui.is.s.u-tokyo.ac.jp/~takeo/research/autozoom/autozoom.htm ABSTRACT We propose a navigation technique for browsing large documents that integrates rate-based scrolling with automatic zooming. The view automatically zooms out when the user scrolls rapidly so that the perceptual scrolling speed in screen space remains constant. As a result, the user can efficiently and smoothly navigate through a large document without becoming disoriented by extremely fast visual flow. By incorporating semantic zooming techniques, the user can smoothly access a global overview of the document during rate-based scrolling. We implemented several prototype systems, including a web browser, map viewer, image browser, and dictionary viewer. An informal usability study suggests that for a document browsing task, most subjects prefer automatic zooming and the technique exhibits approximately equal performance time to scroll bars , suggesting that automatic zooming is a helpful alternative to traditional scrolling when the zoomed out view provides appropriate visual cues. I am writing a GUI using pygtk to test the way they propose to implement automatic zooming and scrolling. My (minimal) code as can be cloned from the git repository at git://github.com/admg26/scroom.git This talk is part of the Machine Learning Journal Club series. This talk is included in these lists:
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