We present a manifold learning approach to dimensionality
reduction that explicitly models the manifold as a mapping
from low to high dimensional space. The manifold is
represen...
We present a tunable representation for tracking that simultaneously encodes appearance and geometry in a manner that enables the use of mean-shift iterations for tracking. The cl...
In visual 3-D reconstruction tasks with mobile cameras, one wishes to move the cameras so that they provide the views that lead to the best reconstruction result. When the camera ...
Stefan Wenhardt, Benjamin Deutsch, Elli Angelopoul...
We present a hierarchical generative model for object recognition that is constructed by weakly-supervised learning. A key component is a novel, adaptive patch feature whose width...
This paper presents a novel spatio-temporal Markov random field (MRF) for video denoising. Two main issues are addressed in this paper, namely, the estimation of noise model and t...