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...
We propose a novel approach to reconstruct complete
3D deformable models over time by a single depth camera,
provided that most parts of the models are observed by the
camera at...
We present methods for training high quality object detectors
very quickly. The core contribution is a pair of fast
training algorithms for piece-wise linear classifiers, which
...
Object tracking typically relies on a dynamic model to
predict the object’s location from its past trajectory. In
crowded scenarios a strong dynamic model is particularly
impo...
We describe a method for producing a smooth, stabilized
video from the shaky input of a hand-held light field video camera—
specifically, a small camera array. Traditional stab...
Brandon M. Smith, Li Zhang, Hailin Jin, Aseem Agar...
User-provided object bounding box is a simple and
popular interaction paradigm considered by many existing
interactive image segmentation frameworks. However,
these frameworks t...
Victor Lempitsky, Pushmeet Kohli, Carsten Rother, ...
In this paper, we investigate the detection of semantic
human actions in complex scenes. Unlike conventional
action recognition in well-controlled environments,
action detection...
This purely theoretical work investigates the problem
of artificial singularities in camera self-calibration. Selfcalibration
allows one to upgrade a projective reconstruction
t...
The classical approach to depth from defocus uses two
images taken with circular apertures of different sizes. We
show in this paper that the use of a circular aperture
severely...
High-level, or holistic, scene understanding involves
reasoning about objects, regions, and the 3D relationships
between them. This requires a representation above the
level of ...