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...
We present a novel variant of the RANSAC algorithm
that is much more efficient, in particular when dealing with
problems with low inlier ratios. Our algorithm assumes
that there...
Fusing partial estimates is a critical and common problem
in many computer vision tasks such as part-based detection
and tracking. It generally becomes complicated and
intractab...
In this paper, we introduce a novel iterative motion tracking
framework that combines 3D tracking techniques with
motion retrieval for stabilizing markerless human motion
captur...
Andreas Baak, Bodo Rosenhahn, Meinard Muller, Hans...
Face identification is the problem of determining
whether two face images depict the same person or not.
This is difficult due to variations in scale, pose, lighting,
background...
Matthieu Guillaumin, Jakob Verbeek, Cordelia Schmi...
Random Forests (RFs) have become commonplace
in many computer vision applications. Their
popularity is mainly driven by their high computational
efficiency during both training ...
Christian Leistner, Amir Saffari, Jakob Santner, H...
Object detection in cluttered, natural scenes has a high
complexity since many local observations compete for object
hypotheses. Voting methods provide an efficient solution
to ...
Atmospheric conditions induced by suspended particles,
such as fog and haze, severely degrade image quality.
Restoring the true scene colors (clear day image) from a
single imag...