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Deep Structure, Singularities, and Computer Vision - First International Workshop, DSSCV 2005, Maastricht, The Netherlands, June 9-10, 2005, Revised Selected Papers (Paperback, 2005 ed.)
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Deep Structure, Singularities, and Computer Vision - First International Workshop, DSSCV 2005, Maastricht, The Netherlands, June 9-10, 2005, Revised Selected Papers (Paperback, 2005 ed.)
Series: Image Processing, Computer Vision, Pattern Recognition, and Graphics, 3753
Expected to ship within 10 - 15 working days
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Whatisactuallytheinformationdirectlyrepresentedinthescale-space?Istarted
to wonder about this shortly after Peter Johansen, 15 years ago,
showed me his intriguing paper on how uniquely to reconstruct a
band-limited 1D signal from its scale-space toppoints. Still, I
have not fully understood its implications. Merely recording where
structure vanishes under blurring is su?cient to fully reconstruct
the details. Of course, technicalities exist, for example, you must
also know negative scale toppoints. Nevertheless, I ?nd it
surprising that we may trade the metric properties of a signal with
the positions of its inherent structure. The result has been
generalizedto analytic signals, shown also for the zero crossings
of the Laplacean, but has not yet been generalized to 2D. This
remains an open problem. In 2003, Peter Giblin, Liverpool
University, Luc Florack, Eindhoven Univ- sity of Technology, Jon
Sporring, University of Copenhagen, my colleague Ole Fogh Olsen,
and several others started the project collaborationDeep Structure
and Singularities in Computer Vision under the European Union, IST,
Future and Emerging Technologies program, trying to obtain further
knowledge about what informationis actuallycarriedby the
singularitiesof shapesand gray-scale images. In this project, we
probed from several directions the question of how much of the
metric information is actually encoded in the structure of shapes
and images. We, and many others, have given hints in this
direction.
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