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CVPR
2009
IEEE
15 years 3 months ago
Learning Shape Prior Models for Object Matching
The aim of this work is to learn a shape prior model for an object class and to improve shape matching with the learned shape prior. Given images of example instances, we can le...
Cordelia Schmid, Frédéric Jurie, Tin...
CVPR
2009
IEEE
15 years 3 months ago
Linear Embeddings in Non-Rigid Structure From Motion
This paper proposes a method to recover the embedding of the possible shapes assumed by a deforming nonrigid object by comparing triplets of frames from an orthographic video se...
Vincent Rabaud (University of California, San Dieg...
CVPR
2009
IEEE
15 years 3 months ago
Shared Kernel Information Embedding for Discriminative Inference
Latent Variable Models (LVM), like the Shared-GPLVM and the Spectral Latent Variable Model, help mitigate over- fitting when learning discriminative methods from small or modera...
David J. Fleet, Leonid Sigal, Roland Memisevic
ICCV
2009
IEEE
15 years 26 days ago
Moving in Stereo: Efficient Structure and Motion Using Lines
We present a fast and robust system for estimating structure and motion using a stereo pair, with straight lines as features. Our first set of contributions are efficient algorit...
Manmohan Chandraker, Jongwoo Lim, David Kriegman
ICCV
2009
IEEE
2061views Computer Vision» more  ICCV 2009»
15 years 26 days ago
Background Subtraction for Freely Moving Cameras
Background subtraction algorithms define the background as parts of a scene that are at rest. Traditionally, these algorithms assume a stationary camera, and identify moving obj...
Yaser Sheikh, Omar Javed, Takeo Kanade
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