122 research outputs found
Classification and Verification of Online Handwritten Signatures with Time Causal Information Theory Quantifiers
We present a new approach for online handwritten signature classification and
verification based on descriptors stemming from Information Theory. The
proposal uses the Shannon Entropy, the Statistical Complexity, and the Fisher
Information evaluated over the Bandt and Pompe symbolization of the horizontal
and vertical coordinates of signatures. These six features are easy and fast to
compute, and they are the input to an One-Class Support Vector Machine
classifier. The results produced surpass state-of-the-art techniques that
employ higher-dimensional feature spaces which often require specialized
software and hardware. We assess the consistency of our proposal with respect
to the size of the training sample, and we also use it to classify the
signatures into meaningful groups.Comment: Submitted to PLOS On
Analytic Expressions for Stochastic Distances Between Relaxed Complex Wishart Distributions
The scaled complex Wishart distribution is a widely used model for multilook
full polarimetric SAR data whose adequacy has been attested in the literature.
Classification, segmentation, and image analysis techniques which depend on
this model have been devised, and many of them employ some type of
dissimilarity measure. In this paper we derive analytic expressions for four
stochastic distances between relaxed scaled complex Wishart distributions in
their most general form and in important particular cases. Using these
distances, inequalities are obtained which lead to new ways of deriving the
Bartlett and revised Wishart distances. The expressiveness of the four analytic
distances is assessed with respect to the variation of parameters. Such
distances are then used for deriving new tests statistics, which are proved to
have asymptotic chi-square distribution. Adopting the test size as a comparison
criterion, a sensitivity study is performed by means of Monte Carlo experiments
suggesting that the Bhattacharyya statistic outperforms all the others. The
power of the tests is also assessed. Applications to actual data illustrate the
discrimination and homogeneity identification capabilities of these distances.Comment: Accepted for publication in the IEEE Transactions on Geoscience and
Remote Sensing journa
How good are MatLab, Octave and Scilab for Computational Modelling?
In this article we test the accuracy of three platforms used in computational
modelling: MatLab, Octave and Scilab, running on i386 architecture and three
operating systems (Windows, Ubuntu and Mac OS). We submitted them to numerical
tests using standard data sets and using the functions provided by each
platform. A Monte Carlo study was conducted in some of the datasets in order to
verify the stability of the results with respect to small departures from the
original input. We propose a set of operations which include the computation of
matrix determinants and eigenvalues, whose results are known. We also used data
provided by NIST (National Institute of Standards and Technology), a protocol
which includes the computation of basic univariate statistics (mean, standard
deviation and first-lag correlation), linear regression and extremes of
probability distributions. The assessment was made comparing the results
computed by the platforms with certified values, that is, known results,
computing the number of correct significant digits.Comment: Accepted for publication in the Computational and Applied Mathematics
journa
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