ICIP
2009
IEEE
13 years 2 months ago
2009
IEEE
Leading compressed sensing (CS) methods require m = O (k log(n)) compressive samples to perfectly reconstruct a k-sparse signal x of size n using random projection matrices (e.g., ...
MVA
2000
13 years 4 months ago
2000
We present a linear rectification algorithm for general, unconstrained stereo rigs. The algorithm takes the two perspective projection matrices of the original cameras, and compute...
ICMCS
2010
IEEE
13 years 5 months ago
2010
IEEE
We propose three methods to derive longer fingerprints from features using projection based hashing methods. For this class of hashing methods, a feature matrix is projected onto ...
CEC
2010
IEEE
13 years 5 months ago
2010
IEEE
This paper proposes a method for reconstructing non-rigid 3D shapes from noisy 2D shapes. The proposed method estimates the 3D shape bases and projection matrices, exploiting low-r...
BMVC
1996
13 years 5 months ago
1996
This paperdescribes initial work on a family of projectivereconstructiontechniques that extract projection matrices directly and linearly from matching tensors estimated from imag...
ICMCS
1999
IEEE
13 years 8 months ago
1999
IEEE
We address the problem of recovering 3D models from uncalibrated images of architectural scenes. We propose a simple, geometrically intuitive method which exploits the strong rigi...
ECCV
2008
Springer
14 years 6 months ago
2008
Springer
We present a closed form solution to the nonrigid shape and motion (NRSM) problem from point correspondences in multiple perspective uncalibrated views. Under the assumption that t...
CVPR
2007
IEEE
14 years 6 months ago
2007
IEEE
Dimensionality reduction is an important issue when facing high-dimensional data. For supervised dimensionality reduction, Linear Discriminant Analysis (LDA) is one of the most po...