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Vortrag

Computational Photography

MPG-Autoren
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Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Freeman, W., & Schölkopf, B. (2008). Computational Photography. Talk presented at NIPS 2008 Workshop: Computational Photography. Whistler, BC, Canada. 2008-12-12.


Zitierlink: https://hdl.handle.net/21.11116/0000-0003-A0C8-6
Zusammenfassung
Computation will change photography. The sensor no longer has to record the final image, but only data that
can lead to the final image. Computation can solve longstanding photographic problems (e.g., deblurring)
and well as open the door for radical new designs and capabilities for image capture, processing, and viewing
(e.g., lightfield cameras). Many of these possibilities offer great machine learning problems, and much
of the progress in computational photography will rely on solutions to these challenging machine learning
problems. We have gathered five leading researchers in this new field to describe their work at the intersection
of photography and machine learning.