6,683 research outputs found
Saliency-guided Adaptive Seeding for Supervoxel Segmentation
We propose a new saliency-guided method for generating supervoxels in 3D
space. Rather than using an evenly distributed spatial seeding procedure, our
method uses visual saliency to guide the process of supervoxel generation. This
results in densely distributed, small, and precise supervoxels in salient
regions which often contain objects, and larger supervoxels in less salient
regions that often correspond to background. Our approach largely improves the
quality of the resulting supervoxel segmentation in terms of boundary recall
and under-segmentation error on publicly available benchmarks.Comment: 6 pages, accepted to IROS201
Semantic distillation: a method for clustering objects by their contextual specificity
Techniques for data-mining, latent semantic analysis, contextual search of
databases, etc. have long ago been developed by computer scientists working on
information retrieval (IR). Experimental scientists, from all disciplines,
having to analyse large collections of raw experimental data (astronomical,
physical, biological, etc.) have developed powerful methods for their
statistical analysis and for clustering, categorising, and classifying objects.
Finally, physicists have developed a theory of quantum measurement, unifying
the logical, algebraic, and probabilistic aspects of queries into a single
formalism. The purpose of this paper is twofold: first to show that when
formulated at an abstract level, problems from IR, from statistical data
analysis, and from physical measurement theories are very similar and hence can
profitably be cross-fertilised, and, secondly, to propose a novel method of
fuzzy hierarchical clustering, termed \textit{semantic distillation} --
strongly inspired from the theory of quantum measurement --, we developed to
analyse raw data coming from various types of experiments on DNA arrays. We
illustrate the method by analysing DNA arrays experiments and clustering the
genes of the array according to their specificity.Comment: Accepted for publication in Studies in Computational Intelligence,
Springer-Verla
i2MapReduce: Incremental MapReduce for Mining Evolving Big Data
As new data and updates are constantly arriving, the results of data mining
applications become stale and obsolete over time. Incremental processing is a
promising approach to refreshing mining results. It utilizes previously saved
states to avoid the expense of re-computation from scratch.
In this paper, we propose i2MapReduce, a novel incremental processing
extension to MapReduce, the most widely used framework for mining big data.
Compared with the state-of-the-art work on Incoop, i2MapReduce (i) performs
key-value pair level incremental processing rather than task level
re-computation, (ii) supports not only one-step computation but also more
sophisticated iterative computation, which is widely used in data mining
applications, and (iii) incorporates a set of novel techniques to reduce I/O
overhead for accessing preserved fine-grain computation states. We evaluate
i2MapReduce using a one-step algorithm and three iterative algorithms with
diverse computation characteristics. Experimental results on Amazon EC2 show
significant performance improvements of i2MapReduce compared to both plain and
iterative MapReduce performing re-computation
A PDE approach to centroidal tessellations of domains
We introduce a class of systems of Hamilton-Jacobi equations that
characterize critical points of functionals associated to centroidal
tessellations of domains, i.e. tessellations where generators and centroids
coincide,
such as centroidal Voronoi tessellations and centroidal power diagrams. An
appropriate version of the Lloyd algorithm, combined with a Fast Marching
method on unstructured grids for the Hamilton-Jacobi equation, allows computing
the solution of the system. We propose various numerical examples to illustrate
the features of the technique
Time-Frequency Analysis of Terahertz Radar Signals for Rapit Heart and Breath Rate Detection
We develop new time-frequency analytic techniques which facilitate the rapid detection of a person\u27s heart and breath rates from the Doppler shift the movement of their body induces in a terahertz radar signal. In particular, the Doppler shift in the continuous radar return is proportional to the velocity of the person\u27s body. Thus, a time-frequency analysis of the radar return will yield a velocity signal. This signal, in turn, may undergo a second time-frequency analysis to yield any periodic components of the velocity signal, which are often related to the heart and breath rates of the individual. One straightforward means of doing such an analysis is to take the spectrogram of the ridgeline of the spectrogram of the radar signal. Instead of exactly following this approach, we consider an alternate method in which the ridgeline of the radar signal\u27s spectrogram is replaced with a signal computed from spectral centroids. By using spectral centroids, rather than the ridgeline, we produce a smooth signal that avoids some traditional problems with ridgelines, such as jump discontinuities and over-quantization. This new method for time-frequency analysis uses a Toeplitz matrix-based algorithm that has a fast Fourier transform-based implementation, and permits centroids of the vertical strips of the spectrogram of the radar signal to be computed without ever having to explicitly compute the spectrogram itself. This algorithm has a lower computational cost than the ridgeline method, and allows us to increase our frequency resolution. We conclude by testing these ideas on real-life data, successfully determining the heart and breath rates of a subject a distance of 10 meters from the radar aperture
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