This paper presents a new method to extract a network of vessels centerlines from a medical
image. The network is composed of local geodesics over a four-dimensional space that in...
Depth maps captured with time-of-flight cameras have
very low data quality: the image resolution is rather limited
and the level of random noise contained in the depth maps
is v...
Sebastian Schuon (Stanford University), Christian ...
This paper presents an algorithm for automatically detecting and segmenting a moving object from a monocular video. Detecting and segmenting a moving object from a video with limit...
Feng Liu (University of Wisconsin-Madison), Michae...
We propose a novel approach for improving level set seg-
mentation methods by embedding the potential functions
from a discriminatively trained conditional random field
(CRF) in...
Dana Cobzas (University of Alberta), Mark Schmidt ...
Recently a large amount of research has been devoted to
automatic activity analysis. Typically, activities have been
defined by their motion characteristics and represented by
t...
Object localization and classification are important problems in computer vision.
However, in many applications, exhaustive search over all class labels and image
locations is co...
Pedestrian detection is a key problem in computer vision,
with several applications including robotics, surveillance
and automotive safety. Much of the progress of the past
few ...
Bernt Schiele, Christian Wojek, Pietro Perona, Pio...
In many social settings, images of groups of people are captured. The structure of this group provides meaningful context for reasoning about individuals in the group, and about th...
This work presents a discriminative training method for
particle filters in the context of multi-object tracking. We
are motivated by the difficulty of hand-tuning the many
mode...
Recently, we proposed marginal space learning (MSL) as
a generic approach for automatic detection of 3D anatom-
ical structures in many medical imaging modalities. To
accurately...