Object class models trained on hundreds or thousands of
images have shown to enable robust detection. Transferring
knowledge from such models to new object classes trained
from ...
Mean shift clustering is a powerful unsupervised data
analysis technique which does not require prior knowledge
of the number of clusters, and does not constrain the shape
of th...
Prominent feature point descriptors such as SIFT and
SURF allow reliable real-time matching but at a compu-
tational cost that limits the number of points that can be
handled on...
Michael Calonder, Vincent Lepetit, Pascal Fua, Kur...
This paper is focused on the Co-segmentation problem
[1] – where the objective is to segment a similar object from
a pair of images. The background in the two images may be
ar...
Geometric rearrangement of images includes operations
such as image retargeting, inpainting, or object rearrangement.
Each such operation can be characterized by a shiftmap:
the...
Image auto-annotation is an important open problem in
computer vision. For this task we propose TagProp, a discriminatively
trained nearest neighbor model. Tags of test
images a...
Matthieu Guillaumin, Thomas Mensink, Jakob Verbeek...
We consider a class of region-based energies for image
segmentation and inpainting which combine region integrals
with curvature regularity of the region boundary. To
minimize s...
The filter flow problem is to compute a space-variant
linear filter that transforms one image into another. This
framework encompasses a broad range of transformations
including...
We study the problem of estimating the epipolar geometry
from apparent contours of smooth curved surfaces
with affine camera models. Since apparent contours are
viewpoint depend...
Recognizing object classes and their 3D viewpoints is an
important problem in computer vision. Based on a partbased
probabilistic representation [31], we propose a new
3D object...