We describe a probabilistic framework for recognizing human activities in monocular video based on simple silhouette observations in this paper. The methodology combines kernel pr...
We propose a new approach to compute non-linear, intrinsic shape statistics and to incorporate them into a shape prior for an image segmentation task. Given a sample set of contou...
Guillaume Charpiat, Olivier D. Faugeras, Renaud Ke...
We propose a family of kernels between images, defined as kernels between their respective segmentation graphs. The kernels are based on soft matching of subtree-patterns of the r...
A good distance metric is crucial for unsupervised learning from high-dimensional data. To learn a metric without any constraint or class label information, most unsupervised metr...
The capacity to robustly detect humans in video is a critical component of automated visual surveillance systems. This paper describes a bilattice based logical reasoning approach...
Vinay D. Shet, Jan Neumann, Visvanathan Ramesh, La...
We look at the problem of location recognition in a large image dataset using a vocabulary tree. This entails finding the location of a query image in a large dataset containing 3...
The ability to determine the identity of a skull found at a crime scene is of critical importance to the law enforcement community. Traditional clay-based methods attempt to recon...
Carl Adrian, Nils Krahnstoever, Peter H. Tu, Phil ...
We present an efficient probabilistic method for identity recognition in personal photo albums. Personal photos are usually taken under uncontrolled conditions ? the captured face...
Smoothly bent paper-like surfaces are developable. They are however difficult to minimally parameterize since the number of meaningful parameters is intrinsically dependent on the...