81,586 research outputs found
Texture descriptor combining fractal dimension and artificial crawlers
Texture is an important visual attribute used to describe images. There are
many methods available for texture analysis. However, they do not capture the
details richness of the image surface. In this paper, we propose a new method
to describe textures using the artificial crawler model. This model assumes
that each agent can interact with the environment and each other. Since this
swarm system alone does not achieve a good discrimination, we developed a new
method to increase the discriminatory power of artificial crawlers, together
with the fractal dimension theory. Here, we estimated the fractal dimension by
the Bouligand-Minkowski method due to its precision in quantifying structural
properties of images. We validate our method on two texture datasets and the
experimental results reveal that our method leads to highly discriminative
textural features. The results indicate that our method can be used in
different texture applications.Comment: 12 pages 9 figures. Paper in press: Physica A: Statistical Mechanics
and its Application
Closed Contour Fractal Dimension Estimation by the Fourier Transform
This work proposes a novel technique for the numerical calculus of the
fractal dimension of fractal objects which can be represented as a closed
contour. The proposed method maps the fractal contour onto a complex signal and
calculates its fractal dimension using the Fourier transform. The Fourier power
spectrum is obtained and an exponential relation is verified between the power
and the frequency. From the parameter (exponent) of the relation, it is
obtained the fractal dimension. The method is compared to other classical
fractal dimension estimation methods in the literature, e. g.,
Bouligand-Minkowski, box-couting and classical Fourier. The comparison is
achieved by the calculus of the fractal dimension of fractal contours whose
dimensions are well-known analytically. The results showed the high precision
and robustness of the proposed technique
Constructing A Flexible Likelihood Function For Spectroscopic Inference
We present a modular, extensible likelihood framework for spectroscopic
inference based on synthetic model spectra. The subtraction of an imperfect
model from a continuously sampled spectrum introduces covariance between
adjacent datapoints (pixels) into the residual spectrum. For the high
signal-to-noise data with large spectral range that is commonly employed in
stellar astrophysics, that covariant structure can lead to dramatically
underestimated parameter uncertainties (and, in some cases, biases). We
construct a likelihood function that accounts for the structure of the
covariance matrix, utilizing the machinery of Gaussian process kernels. This
framework specifically address the common problem of mismatches in model
spectral line strengths (with respect to data) due to intrinsic model
imperfections (e.g., in the atomic/molecular databases or opacity
prescriptions) by developing a novel local covariance kernel formalism that
identifies and self-consistently downweights pathological spectral line
"outliers." By fitting many spectra in a hierarchical manner, these local
kernels provide a mechanism to learn about and build data-driven corrections to
synthetic spectral libraries. An open-source software implementation of this
approach is available at http://iancze.github.io/Starfish, including a
sophisticated probabilistic scheme for spectral interpolation when using model
libraries that are sparsely sampled in the stellar parameters. We demonstrate
some salient features of the framework by fitting the high resolution -band
spectrum of WASP-14, an F5 dwarf with a transiting exoplanet, and the moderate
resolution -band spectrum of Gliese 51, an M5 field dwarf.Comment: Accepted to ApJ. Incorporated referees' comments. New figures 1, 8,
10, 12, and 14. Supplemental website: http://iancze.github.io/Starfish
A Data Cube Extraction Pipeline for a Coronagraphic Integral Field Spectrograph
Project 1640 is a high contrast near-infrared instrument probing the
vicinities of nearby stars through the unique combination of an integral field
spectrograph with a Lyot coronagraph and a high-order adaptive optics system.
The extraordinary data reduction demands, similar those which several new
exoplanet imaging instruments will face in the near future, have been met by
the novel software algorithms described herein. The Project 1640 Data Cube
Extraction Pipeline (PCXP) automates the translation of 3.8*10^4 closely
packed, coarsely sampled spectra to a data cube. We implement a robust
empirical model of the spectrograph focal plane geometry to register the
detector image at sub-pixel precision, and map the cube extraction. We
demonstrate our ability to accurately retrieve source spectra based on an
observation of Saturn's moon Titan.Comment: 35 pages, 15 figures; accepted for publication in PAS
Canalization of the evolutionary trajectory of the human influenza virus
Since its emergence in 1968, influenza A (H3N2) has evolved extensively in
genotype and antigenic phenotype. Antigenic evolution occurs in the context of
a two-dimensional 'antigenic map', while genetic evolution shows a
characteristic ladder-like genealogical tree. Here, we use a large-scale
individual-based model to show that evolution in a Euclidean antigenic space
provides a remarkable correspondence between model behavior and the
epidemiological, antigenic, genealogical and geographic patterns observed in
influenza virus. We find that evolution away from existing human immunity
results in rapid population turnover in the influenza virus and that this
population turnover occurs primarily along a single antigenic axis. Thus,
selective dynamics induce a canalized evolutionary trajectory, in which the
evolutionary fate of the influenza population is surprisingly repeatable and
hence, in theory, predictable.Comment: 29 pages, 5 figures, 10 supporting figure
Application of Fractal Dimension for Quantifying Noise Texture in Computed Tomography Images
Purpose
Evaluation of noise texture information in CT images is important for assessing image quality. Noise texture is often quantified by the noise power spectrum (NPS), which requires numerous image realizations to estimate. This study evaluated fractal dimension for quantifying noise texture as a scalar metric that can potentially be estimated using one image realization. Methods
The American College of Radiology CT accreditation phantom (ACR) was scanned on a clinical scanner (Discovery CT750, GE Healthcare) at 120 kV and 25 and 90 mAs. Images were reconstructed using filtered back projection (FBP/ASIR 0%) with varying reconstruction kernels: Soft, Standard, Detail, Chest, Lung, Bone, and Edge. For each kernel, images were also reconstructed using ASIR 50% and ASIR 100% iterative reconstruction (IR) methods. Fractal dimension was estimated using the differential boxâcounting algorithm applied to images of the uniform section of ACR phantom. The twoâdimensional Noise Power Spectrum (NPS) and oneâdimensionalâradially averaged NPS were estimated using established techniques. By changing the radiation dose, the effect of noise magnitude on fractal dimension was evaluated. The Spearman correlation between the fractal dimension and the frequency of the NPS peak was calculated. The number of images required to reliably estimate fractal dimension was determined and compared to the number of images required to estimate the NPSâpeak frequency. The effect of Region of Interest (ROI) size on fractal dimension estimation was evaluated. Feasibility of estimating fractal dimension in an anthropomorphic phantom and clinical image was also investigated, with the resulting fractal dimension compared to that estimated within the uniform section of the ACR phantom. Results
Fractal dimension was strongly correlated with the frequency of the peak of the radially averaged NPS curve, having a Spearman rankâorder coefficient of 0.98 (Pâvalue \u3c 0.01) for ASIR 0%. The mean fractal dimension at ASIR 0% was 2.49 (Soft), 2.51 (Standard), 2.52 (Detail), 2.57 (Chest), 2.61 (Lung), 2.66 (Bone), and 2.7 (Edge). A reduction in fractal dimension was observed with increasing ASIR levels for all investigated reconstruction kernels. Fractal dimension was found to be independent of noise magnitude. Fractal dimension was successfully estimated from four ROIs of size 64 Ă 64 pixels or one ROI of 128 Ă 128 pixels. Fractal dimension was found to be sensitive to nonânoise structures in the image, such as ring artifacts and anatomical structure. Fractal dimension estimated within a uniform region of an anthropomorphic phantom and clinical head image matched that estimated within the ACR phantom for filtered back projection reconstruction. Conclusions
Fractal dimension correlated with the NPSâpeak frequency and was independent of noise magnitude, suggesting that the scalar metric of fractal dimension can be used to quantify the change in noise texture across reconstruction approaches. Results demonstrated that fractal dimension can be estimated from four, 64 Ă 64âpixel ROIs or one 128 Ă 128 ROI within a head CT image, which may make it amenable for quantifying noise texture within clinical images
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