We propose an unconventional but highly effective approach
to robust fitting of multiple structures by using statistical
learning concepts. We design a novel Mercer kernel
for t...
We study the scenario of a multiview setting, where several
calibrated views of a textured object with known surface
geometry are available. The objective is to estimate a
diffu...
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...
The concept of graph cuts is by now a standard method
for all sorts of low level vision problems. Its popularity is
largely due to the fact that globally or near globally optimal...
Carl Olsson, Martin Byr¨od, Niels Chr. Overgaard,...
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...
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...
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....