Learning Object-Independent Modes of Variation with Feature Flow Fields
We present a unifying framework in which "object-independent" modes of variation are learned from continuous-time data such as video sequences. These modes of variation can be used as "generators" to produce a manifold of images of a new object from a single example of that obje...
Main Authors: | , , |
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Language: | en_US |
Published: |
2004
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Subjects: | |
Online Access: | http://hdl.handle.net/1721.1/6659 |