ICCV
2009
IEEE
15 years 3 months ago
2009
IEEE
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....
ICCV
2009
IEEE
15 years 3 months ago
2009
IEEE
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...
ICCV
2009
IEEE
15 years 3 months ago
2009
IEEE
Markov Random Field is now ubiquitous in many formulations
of various vision problems. Recently, optimization
of higher-order potentials became practical using higherorder
graph...
ICCV
2009
IEEE
15 years 3 months ago
2009
IEEE
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...
ICCV
2009
IEEE
15 years 3 months ago
2009
IEEE
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...
ICCV
2009
IEEE
15 years 3 months ago
2009
IEEE
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...
ICCV
2009
IEEE
15 years 3 months ago
2009
IEEE
We present a method to learn visual attributes (eg.“red”,
“metal”, “spotted”) and object classes (eg. “car”,
“dress”, “umbrella”) together. We assume imag...
ICCV
2009
IEEE
15 years 3 months ago
2009
IEEE
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...
ICCV
2009
IEEE
15 years 3 months ago
2009
IEEE
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...
ICCV
2009
IEEE
15 years 3 months ago
2009
IEEE
Random Forests (RFs) have become commonplace
in many computer vision applications. Their
popularity is mainly driven by their high computational
efficiency during both training ...