Abstract-We introduce a local image statistic for identifying noise pixels in images corrupted with impulse noise of random values. The statistical values quantify how different in intensity the particular pixels are from their most similar neighbors. We continue to demonstrate how this statistic may be incorporated into a filter designed to remove additive Gaussian noise. The result is a new filter capable of reducing both Gaussian and impulse noises from noisy images effectively, which performs remarkably well, both in terms of quantitative measures of signal restoration and qualitative judgements of image quality. Our approach is extended to automatically remove any mix of Gaussian and impulse noise.
We introduce propagation kernels, a general graph-kernel framework for efficiently measuring the similarity of structured data. Propagation kernels are based on monitoring how information spreads through a set of given graphs. They leverage earlystage distributions from propagation schemes such as random walks to capture structural information encoded in node labels, attributes, and edge information. This has two benefits. First, off-the-shelf propagation schemes can be used to naturally construct kernels for many graph types, including labeled, partially labeled, unlabeled, directed, and attributed graphs. Second, by leveraging existing efficient and informative propagation schemes, propagation kernels can be considerably faster than state-of-the-art approaches without sacrificing predictive performance. We will also show that if the graphs at hand have a regular structure, for instance when modeling image or video data, one can exploit this regularity to scale the kernel computation to large databases of graphs with thousands of nodes. We support our contributions by exhaustive experiments on a number of real-world graphs from a variety of application domains.
We present new estimates for the statistical properties of damped Lyman-α absorbers (DLAs). We compute the column density distribution function at z > 2, the line density, dN/dX, and the neutral hydrogen density, Ω DLA . Our estimates are derived from the DLA catalogue of Garnett et al. (2016), which uses the SDSS-III DR12 quasar spectroscopic survey. This catalogue provides a probability that a given spectrum contains a DLA. It allows us to use even the noisiest data without biasing our results and thus substantially increases our sample size. We measure a non-zero column density distribution function at 95% confidence for all column densities N HI < 5 × 10 22 cm −2 . We make the first measurements from SDSS of dN/dX and Ω DLA at z > 4. We show that our results are insensitive to the signal-to-noise ratio of the spectra, but that there is a residual dependence on quasar redshift for z < 2.5, which may be due to remaining systematics in our analysis.
We develop an automated technique for detecting damped Lyman-α absorbers (dlas) along spectroscopic sightlines to quasi-stellar objects (qsos or quasars). The detection of dlas in largescale spectroscopic surveys such as sdss-iii sheds light on galaxy formation at high redshift, showing the nucleation of galaxies from diffuse gas. We use nearly 50 000 qso spectra to learn a novel tailored Gaussian process model for quasar emission spectra, which we apply to the dla detection problem via Bayesian model selection. We propose models for identifying an arbitrary number of dlas along a given line of sight. We demonstrate our method's effectiveness using a large-scale validation experiment, with excellent performance. We also provide a catalog of our results applied to 162 861 spectra from sdss-iii data release 12.
We consider the problem of selecting an optimal set of sensors, as determined, for example, by the predictive accuracy of the resulting sensor network. Given an underlying metric between pairs of set elements, we introduce a natural metric between sets of sensors for this task. Using this metric, we can construct covariance functions over sets, and thereby perform Gaussian process inference over a function whose domain is a power set. If the function has additional inputs, our covariances can be readily extended to incorporate them-allowing us to consider, for example, functions over both sets and time. These functions can then be optimized using Gaussian process global optimization (GPGO). We use the root mean squared error (RMSE) of the predictions made using a set of sensors at a particular time as an example of such a function to be optimized; the optimal point specifies the best choice of sensor locations. We demonstrate the resulting method by dynamically selecting the best subset of a given set of weather sensors for the prediction of the air temperature across the United Kingdom.
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