The state-of-the art in visual object retrieval from large
databases allows to search millions of images on the object
level. Recently, complementary works have proposed systems
...
Stephan Gammeter, Lukas Bossard, Till Quack, Luc V...
Scenes with cast shadows can produce complex sets of
images. These images cannot be well approximated by lowdimensional
linear subspaces. However, in this paper we
show that the...
In this paper we propose a robust visual tracking method
by casting tracking as a sparse approximation problem in a
particle filter framework. In this framework, occlusion, corru...
Our objective is to obtain a state-of-the art object category
detector by employing a state-of-the-art image classifier
to search for the object in all possible image subwindows....
Andrea Vedaldi, Varun Gulshan, Manik Varma, Andrew...
We present a fast and robust system for estimating structure
and motion using a stereo pair, with straight lines as
features. Our first set of contributions are efficient algorit...
Markov Random Field is now ubiquitous in many formulations
of various vision problems. Recently, optimization
of higher-order potentials became practical using higherorder
graph...
We present a shape-based algorithm for detecting and
recognizing non-rigid objects from natural images. The existing
literature in this domain often cannot model the objects
ver...
Xiang Bai, Xinggang Wang, Longin Jan Latecki, Weny...
Supervised learning of a parts-based model can be for-
mulated as an optimization problem with a large (exponen-
tial in the number of parts) set of constraints. We show how
thi...
M. Pawan Kumar, Andrew Zisserman, Philip H.S. Torr
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 method to learn visual attributes (eg.“red”,
“metal”, “spotted”) and object classes (eg. “car”,
“dress”, “umbrella”) together. We assume imag...