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...
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...
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 ...
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 ...
In this paper, we present a Deformable Action Template
(DAT) model that is learnable from cluttered real-world
videos with weak supervisions. In our generative model,
an action ...
This paper presents a simple yet practical 3-D modeling
method for recovering surface shape and reflectance
from a set of images. We attach a point light source to a
hand-held c...