26,011 research outputs found
Evaluating and combining digital video shot boundary detection algorithms
The development of standards for video encoding coupled with the increased power of computing mean that content-based manipulation of digital video information is now feasible. Shots are a basic structural building block of digital video and the boundaries between shots need to be determined automatically to allow for content-based manipulation. A shot can be thought of as continuous images from one camera at a time. In this paper we examine a variety of automatic techniques for shot boundary detection that we have implemented and evaluated on a baseline of 720,000 frames (8 hours) of broadcast television. This extends our previous work on evaluating a single technique based on comparing colour histograms. A description of each of our three methods currently working is given along with how they
are evaluated. It is found that although the different methods have about the same order of magnitude in terms of effectiveness, different shot boundaries are detected by the different methods. We then look at combining the three shot boundary detection methods to produce one output result and the benefits in accuracy and performance that this brought to our system. Each of the methods were changed from using
a static threshold value for three unconnected methods to one using three dynamic threshold values for one connected method. In a final summing up we look at the future directions for this work
A Meta-Theory of Boundary Detection Benchmarks
Human labeled datasets, along with their corresponding evaluation algorithms,
play an important role in boundary detection. We here present a psychophysical
experiment that addresses the reliability of such benchmarks. To find better
remedies to evaluate the performance of any boundary detection algorithm, we
propose a computational framework to remove inappropriate human labels and
estimate the intrinsic properties of boundaries.Comment: NIPS 2012 Workshop on Human Computation for Science and Computational
Sustainabilit
Classifying LISA gravitational wave burst signals using Bayesian evidence
We consider the problem of characterisation of burst sources detected with
the Laser Interferometer Space Antenna (LISA) using the multi-modal nested
sampling algorithm, MultiNest. We use MultiNest as a tool to search for
modelled bursts from cosmic string cusps, and compute the Bayesian evidence
associated with the cosmic string model. As an alternative burst model, we
consider sine-Gaussian burst signals, and show how the evidence ratio can be
used to choose between these two alternatives. We present results from an
application of MultiNest to the last round of the Mock LISA Data Challenge, in
which we were able to successfully detect and characterise all three of the
cosmic string burst sources present in the release data set. We also present
results of independent trials and show that MultiNest can detect cosmic string
signals with signal-to-noise ratio (SNR) as low as ~7 and sine-Gaussian signals
with SNR as low as ~8. In both cases, we show that the threshold at which the
sources become detectable coincides with the SNR at which the evidence ratio
begins to favour the correct model over the alternative.Comment: 21 pages, 11 figures, accepted by CQG; v2 has minor changes for
consistency with accepted versio
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