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...

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Bibliographic Details
Main Authors: Miller, Erik G., Tieu, Kinh, Stauffer, Chris P.
Language:en_US
Published: 2004
Subjects:
Online Access:http://hdl.handle.net/1721.1/6659