We present a method to learn visual attributes (eg.“red”,
“metal”, “spotted”) and object classes (eg. “car”,
“dress”, “umbrella”) together. We assume imag...
Supervised learning of a parts-based model can be for-
mulated as an optimization problem with a large (exponen-
tial in the number of parts) set of constraints. We show how
thi...
M. Pawan Kumar, Andrew Zisserman, Philip H.S. Torr
This paper investigates the design of a system for recognizing
objects in 3D point clouds of urban environments.
The system is decomposed into four steps: locating, segmenting,
...
Aleksey Golovinskiy, Vladimir G. Kim, Thomas Funkh...
In this paper we propose an algorithm to estimate missing
values in tensors of visual data. The values can be missing
due to problems in the acquisition process, or because
the ...
Ji Liu, Przemyslaw Musialski, Peter Wonka, Jieping...
Image texture can arise not only from surface albedo variations (2D texture) but also from surface height variations (3D texture). Since the appearance of 3D texture depends on th...