This paper proposes a new approach for video stabilization.
Most existing video stabilization methods adopt
a framework of three steps, motion estimation, motion compensation
an...
Ken-Yi Lee, Yung-Yu, Chuang Bing-Yu, Chen Ming Ouh...
This paper presents a target tracking framework for unstructured
crowded scenes. Unstructured crowded scenes
are defined as those scenes where the motion of a crowd
appears to b...
In this paper we describe a method to learn parameters
which govern pedestrian motion by observing video
data. Our learning framework is based on variational
mode learning and a...
When a curved mirror-like surface moves relative to its
environment, it induces a motion field—or specular flow—
on the image plane that observes it. This specular flow is
r...
Guillermo D. Canas, Yuriy Vasilyev, Yair Adato, To...
In this paper we present a combined approach for ob-
ject localization and classification. Our contribution is two-
fold. (a) A contextual combination of localization and clas-
...
We present a system that can match and reconstruct 3D
scenes from extremely large collections of photographs such
as those found by searching for a given city (e.g., Rome) on
In...
Sameer Agarwal, Noah Snavely, Ian Simon, Steven M....
This paper addresses the “boundary ownership” problem,
also known as the figure/ground assignment problem.
Estimating boundary ownerships is a key step in perceptual
organiz...
A key ingredient in the design of visual object classification
systems is the identification of relevant class specific
aspects while being robust to intra-class variations. Whil...
Given a single outdoor image, we present a method for estimating the likely illumination conditions of the scene. In particular, we compute the probability distribution over the su...
Jean-François Lalonde, Alexei A. Efros, Srinivasa...
We present an activity recognition feature inspired by
human psychophysical performance. This feature is based
on the velocity history of tracked keypoints. We present a
generat...