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IVC
2007
184views more  IVC 2007»
13 years 8 months ago
Image distance functions for manifold learning
Many natural image sets are samples of a low-dimensional manifold in the space of all possible images. When the image data set is not a linear combination of a small number of bas...
Richard Souvenir, Robert Pless
IROS
2008
IEEE
125views Robotics» more  IROS 2008»
14 years 3 months ago
Neighborhood denoising for learning high-dimensional grasping manifolds
— Human control of high degree-of-freedom robotic systems, e.g. anthropomorphic robot hands, is often difficult due to the overwhelming number of variables that need to be speci...
Aggeliki Tsoli, Odest Chadwicke Jenkins
ICPR
2010
IEEE
14 years 1 months ago
Face Recognition Using a Multi-Manifold Discriminant Analysis Method
—In this paper, we propose a Multi-Manifold Discriminant Analysis (MMDA) method for face feature extraction and face recognition, which is based on graph embedded learning and un...
Wankou Yang, Changyin Sun, Lei Zhang
SIGGRAPH
1995
ACM
14 years 10 days ago
Modeling surfaces of arbitrary topology using manifolds
We describe an extension of B-splines to surfacesof arbitrary topology, including arbitrary boundaries. The technique inherits many of the properties of B-splines: local control, ...
Cindy Grimm, John F. Hughes
NIPS
1997
13 years 10 months ago
Mapping a Manifold of Perceptual Observations
Nonlinear dimensionality reduction is formulated here as the problem of trying to find a Euclidean feature-space embedding of a set of observations that preserves as closely as p...
Joshua B. Tenenbaum