ECCV
2010
Springer
13 years 8 months ago
2010
Springer
Manifolds are widely used to model non-linearity arising in a range of computer vision applications. This paper treats statistics on manifolds and the loss of accuracy occurring wh...
MM
2004
ACM
13 years 9 months ago
2004
ACM
We consider the problem of learning a mapping function from low-level feature space to high-level semantic space. Under the assumption that the data lie on a submanifold embedded ...
ICML
2004
IEEE
13 years 9 months ago
2004
IEEE
We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. Noting that the kernel matrix implicitly maps the data into ...
IWANN
2005
Springer
13 years 9 months ago
2005
Springer
In many practical applications, the data is organized along a manifold of lower dimension than the dimension of the embedding space. This additional information can be used when le...
IPMI
2005
Springer
13 years 9 months ago
2005
Springer
Abstract. This paper presents a new registration method called Transitive InverseConsistent Manifold Registration (TICMR). The TICMR method jointly estimates correspondence maps be...
CVBIA
2005
Springer
13 years 10 months ago
2005
Springer
Cardiopulmonary imaging is a key tool in modern diagnostic and interventional medicine. Automated analysis of MRI or ultrasound video is complicated by limitations on the image qua...
SMA
2005
ACM
13 years 10 months ago
2005
ACM
Constructing splines whose parametric domain is an arbitrary manifold and effectively computing such splines in realworld applications are of fundamental importance in solid and ...
ICCV
2005
IEEE
13 years 10 months ago
2005
IEEE
The space of images is known to be a non-linear subspace that is difficult to model. This paper derives an algorithm that walks within this space. We seek a manifold through the ...
ICCV
2005
IEEE
13 years 10 months ago
2005
IEEE
This paper investigates the appearance manifold of facial expression: embedding image sequences of facial expression from the high dimensional appearance feature space to a low dim...
ICCV
2007
IEEE
13 years 10 months ago
2007
IEEE
Dimensionality reduction plays a fundamental role in data processing, for which principal component analysis (PCA) is widely used. In this paper, we develop the Laplacian PCA (LPC...