20,395 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
Quicksilver: Fast Predictive Image Registration - a Deep Learning Approach
This paper introduces Quicksilver, a fast deformable image registration
method. Quicksilver registration for image-pairs works by patch-wise prediction
of a deformation model based directly on image appearance. A deep
encoder-decoder network is used as the prediction model. While the prediction
strategy is general, we focus on predictions for the Large Deformation
Diffeomorphic Metric Mapping (LDDMM) model. Specifically, we predict the
momentum-parameterization of LDDMM, which facilitates a patch-wise prediction
strategy while maintaining the theoretical properties of LDDMM, such as
guaranteed diffeomorphic mappings for sufficiently strong regularization. We
also provide a probabilistic version of our prediction network which can be
sampled during the testing time to calculate uncertainties in the predicted
deformations. Finally, we introduce a new correction network which greatly
increases the prediction accuracy of an already existing prediction network. We
show experimental results for uni-modal atlas-to-image as well as uni- / multi-
modal image-to-image registrations. These experiments demonstrate that our
method accurately predicts registrations obtained by numerical optimization, is
very fast, achieves state-of-the-art registration results on four standard
validation datasets, and can jointly learn an image similarity measure.
Quicksilver is freely available as an open-source software.Comment: Add new discussion
Fast-Forward Video Based on Semantic Extraction
Thanks to the low operational cost and large storage capacity of smartphones
and wearable devices, people are recording many hours of daily activities,
sport actions and home videos. These videos, also known as egocentric videos,
are generally long-running streams with unedited content, which make them
boring and visually unpalatable, bringing up the challenge to make egocentric
videos more appealing. In this work we propose a novel methodology to compose
the new fast-forward video by selecting frames based on semantic information
extracted from images. The experiments show that our approach outperforms the
state-of-the-art as far as semantic information is concerned and that it is
also able to produce videos that are more pleasant to be watched.Comment: Accepted for publication and presented in 2016 IEEE International
Conference on Image Processing (ICIP
FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation
One of the most popular approaches to multi-target tracking is
tracking-by-detection. Current min-cost flow algorithms which solve the data
association problem optimally have three main drawbacks: they are
computationally expensive, they assume that the whole video is given as a
batch, and they scale badly in memory and computation with the length of the
video sequence. In this paper, we address each of these issues, resulting in a
computationally and memory-bounded solution. First, we introduce a dynamic
version of the successive shortest-path algorithm which solves the data
association problem optimally while reusing computation, resulting in
significantly faster inference than standard solvers. Second, we address the
optimal solution to the data association problem when dealing with an incoming
stream of data (i.e., online setting). Finally, we present our main
contribution which is an approximate online solution with bounded memory and
computation which is capable of handling videos of arbitrarily length while
performing tracking in real time. We demonstrate the effectiveness of our
algorithms on the KITTI and PETS2009 benchmarks and show state-of-the-art
performance, while being significantly faster than existing solvers
A fast GPU Monte Carlo Radiative Heat Transfer Implementation for Coupling with Direct Numerical Simulation
We implemented a fast Reciprocal Monte Carlo algorithm, to accurately solve
radiative heat transfer in turbulent flows of non-grey participating media that
can be coupled to fully resolved turbulent flows, namely to Direct Numerical
Simulation (DNS). The spectrally varying absorption coefficient is treated in a
narrow-band fashion with a correlated-k distribution. The implementation is
verified with analytical solutions and validated with results from literature
and line-by-line Monte Carlo computations. The method is implemented on GPU
with a thorough attention to memory transfer and computational efficiency. The
bottlenecks that dominate the computational expenses are addressed and several
techniques are proposed to optimize the GPU execution. By implementing the
proposed algorithmic accelerations, a speed-up of up to 3 orders of magnitude
can be achieved, while maintaining the same accuracy
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