3,929 research outputs found
Towards Semantic Fast-Forward and Stabilized Egocentric Videos
The emergence of low-cost personal mobiles devices and wearable cameras and
the increasing storage capacity of video-sharing websites have pushed forward a
growing interest towards first-person videos. Since most of the recorded videos
compose long-running streams with unedited content, they are tedious and
unpleasant to watch. The fast-forward state-of-the-art methods are facing
challenges of balancing the smoothness of the video and the emphasis in the
relevant frames given a speed-up rate. In this work, we present a methodology
capable of summarizing and stabilizing egocentric videos by extracting the
semantic information from the frames. This paper also describes a dataset
collection with several semantically labeled videos and introduces a new
smoothness evaluation metric for egocentric videos that is used to test our
method.Comment: Accepted for publication and presented in the First International
Workshop on Egocentric Perception, Interaction and Computing at European
Conference on Computer Vision (EPIC@ECCV) 201
Aspects of noncommutative Lorentzian geometry for globally hyperbolic spacetimes
Connes' functional formula of the Riemannian distance is generalized to the
Lorentzian case using the so-called Lorentzian distance, the d'Alembert
operator and the causal functions of a globally hyperbolic spacetime. As a step
of the presented machinery, a proof of the almost-everywhere smoothness of the
Lorentzian distance considered as a function of one of the two arguments is
given. Afterwards, using a -algebra approach, the spacetime causal
structure and the Lorentzian distance are generalized into noncommutative
structures giving rise to a Lorentzian version of part of Connes'
noncommutative geometry. The generalized noncommutative spacetime consists of a
direct set of Hilbert spaces and a related class of -algebras of
operators. In each algebra a convex cone made of self-adjoint elements is
selected which generalizes the class of causal functions. The generalized
events, called {\em loci}, are realized as the elements of the inductive limit
of the spaces of the algebraic states on the -algebras. A partial-ordering
relation between pairs of loci generalizes the causal order relation in
spacetime. A generalized Lorentz distance of loci is defined by means of a
class of densely-defined operators which play the r\^ole of a Lorentzian
metric. Specializing back the formalism to the usual globally hyperbolic
spacetime, it is found that compactly-supported probability measures give rise
to a non-pointwise extension of the concept of events.Comment: 43 pages, structure of the paper changed and presentation strongly
improved, references added, minor typos corrected, title changed, accepted
for publication in Reviews in Mathematical Physic
Understanding Marine Microbes, the Driving Engines of the Ocean
When you hear the word microbes, what comes to your mind? Something much too small to see and that makes you fall ill? Just because some microbes cause diseases that does not mean they are all evil. For example, in the marine (ocean) environment, the vast majority of microbes are good ones. They are the “driving engines” of the ocean and are essential for the health of our whole planet. Unfortunately, most of the marine microbes and their interactions with the marine environment are poorly understood. So, it is important to get an idea of which microbes are helping us and how they are doing this. These data will provide scientists with the knowledge to fight against big global challenges, such as climate change and environmental pollution. Unfortunately, it is very hard to study marine microbes due to their microscopic size, huge diversity, and their big home – the ocean. Therefore, we would like to engage “citizen scientists” in this project to help us to sample marine microbes so that we can identify them
Semantically Guided Depth Upsampling
We present a novel method for accurate and efficient up- sampling of sparse
depth data, guided by high-resolution imagery. Our approach goes beyond the use
of intensity cues only and additionally exploits object boundary cues through
structured edge detection and semantic scene labeling for guidance. Both cues
are combined within a geodesic distance measure that allows for
boundary-preserving depth in- terpolation while utilizing local context. We
model the observed scene structure by locally planar elements and formulate the
upsampling task as a global energy minimization problem. Our method determines
glob- ally consistent solutions and preserves fine details and sharp depth
bound- aries. In our experiments on several public datasets at different levels
of application, we demonstrate superior performance of our approach over the
state-of-the-art, even for very sparse measurements.Comment: German Conference on Pattern Recognition 2016 (Oral
- …