This paper presents a method to quantitatively evaluate
information contributions of individual bottom-up and topdown
computing processes in object recognition. Our objective
is...
Deformable model fitting has been actively pursued in the computer vision
community for over a decade. As a result, numerous approaches have
been proposed with varying degrees of...
We present a vision-based method that assists human
navigation within unfamiliar environments. Our main contribution
is a novel algorithm that learns the correlation between
use...
2D Active Appearance Models (AAM) and 3D Morphable
Models (3DMM) are widely used techniques. AAM
provide a fast fitting process, but may represent unwanted
3D transformations un...
A general framework simultaneously addressing pose
estimation, 2D segmentation, object recognition, and 3D
reconstruction from a single image is introduced in this
paper. The pr...
We present a new variational level-set-based segmentation
formulation that uses both shape and intensity prior information
learned from a training set. By applying Bayes’
rule...
We propose a novel approach for multi-person trackingby-
detection in a particle filtering framework. In addition
to final high-confidence detections, our algorithm uses the
con...
Michael D. Breitenstein, Fabian Reichlin, Bastian ...
It has recently been shown that deformable 3D surfaces
could be recovered from single video streams. However, ex-
isting techniques either require a reference view in which
the ...
Aydin Varol, Mathieu Salzmann, Engin Tola, Pascal ...
Many interactive image segmentation approaches use an objective function which includes appearance models as an unknown variable. Since the resulting optimization problem is NP-har...
This paper proposes a generic method for action recognition
in uncontrolled videos. The idea is to use images
collected from the Web to learn representations of actions
and use ...
Nazli Ikizler-Cinbis, R. Gokberk Cinbis, Stan Scla...