作者：
Reyes-Aldasoro, C. C.Univ Sheffield
Acad Unit Surg Oncol Canc Res UK Tumour Microcirculat Grp Sch Med & Biomed Sci Sheffield S10 2RX S Yorkshire England
An additive retrospective non-parametric algorithm for the correction of the inhomogeneous intensity background of images, commonly known as shading, is presented. The algorithm assumed that an original unbiased image...
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An additive retrospective non-parametric algorithm for the correction of the inhomogeneous intensity background of images, commonly known as shading, is presented. The algorithm assumed that an original unbiased image was corrupted by slowly-varying shading that could be estimated from the signal envelope in a process analogous to amplitude modulation detection. Unlike other filtering algorithms, the algorithm did not require pre-processing, parameter setting, user interaction, computationally intensive optimisation algorithms nor a restriction in size of the objects of interest relative to the scale of background variations. The algorithm provided satisfactory results for artificial and microscopical images.
The paper considers the problems of selecting the subset of informative textural features. The influence of the sliding window size on the result of textural analysis is discussed. A new nonparametric algorithm is pro...
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ISBN:
(纸本)0819457175
The paper considers the problems of selecting the subset of informative textural features. The influence of the sliding window size on the result of textural analysis is discussed. A new nonparametric algorithm is proposed for textural analysis. This algorithm is tested as applied to the problem of separation of cloud fields and estimation of their parameters from the NOAA AVHRR data.
A novel TBD algorithm for tracking dim moving point target in I R image sequence with low SNR was demonstrated. Original images are preprocessed using temperature non-linear elimination and Top-hat operator, and then ...
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ISBN:
(纸本)0819460621
A novel TBD algorithm for tracking dim moving point target in I R image sequence with low SNR was demonstrated. Original images are preprocessed using temperature non-linear elimination and Top-hat operator, and then a composite frame is obtained by projecting operation along the time axis to reduce 3D spatio-temporal scanning for target to 2D spatial hunting. Finally the target trajectory is tracked under the condition of constant false-alarm probability(CFAR). From the experimental results, the algorithm can successfully detect dim moving point target and accurately estimate its trajectory. The algorithm, insensitive to the velocity mismatch and the changes of statistical distribution of background clutter, is adaptable to real-time target detection and tracking.
In condensed matter the formation of a muonium atom from a positive muon and an election is described usually with a first order kinetic equation which assumes that the process is random and that the charge distributi...
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In condensed matter the formation of a muonium atom from a positive muon and an election is described usually with a first order kinetic equation which assumes that the process is random and that the charge distribution is uniform. According to this model the muon polarization function as a function of time should reduce to an exponential law. Experiments in superfluid helium demonstrates that this is incorrect. Our proposed technique allows to reconstruct the muonium formation rate function from the mu SR histogram in low transverse magnetic field without presupposing a particular theoretical form, i.e. with no parametrization. The technique is based on solving the integral equation of the first kind for the muon polarization function using the maximum likelihood method. The obtained results are of fundamental importance for the analysis of the charge kinetics in superfluid helium. (C) 2000 Elsevier Science B.V. All rights reserved.
An algorithm for identification of a nonlinear characteristic in a static distributed-parameter system is proposed. The algorithm is nonparametric in the sense that only smooth requirements are imposed on the unknown ...
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An algorithm for identification of a nonlinear characteristic in a static distributed-parameter system is proposed. The algorithm is nonparametric in the sense that only smooth requirements are imposed on the unknown characteristic and that the number of terms in its orthogonal series expansion is not fixed in advance, depending on the number of measurements (the idea borrowed from statistics). Computational simplicity of the algorithm is obtained by restricting its applications to the identification of only weakly nonlinear, static systems, which allows applying perturbation theory. It is shown that if the unknown characteristic in the system equation is replaced by its estimator, then the resulting state converges to the true system state in the mean square sense.
The present paper discusses a nonsupervised multicategory problem in terms of nonparametric learning. An algorithm for seeking modes of an unknown multidimensional probability density function (pdf) is considered by e...
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The present paper discusses a nonsupervised multicategory problem in terms of nonparametric learning. An algorithm for seeking modes of an unknown multidimensional probability density function (pdf) is considered by employing a hypercubic window function. The convergence proof of the algorithm is also presented. The discriminant function for multicategory problems is constructed by using the estimates of the modes of the multimodal pdf. An application of the mode estimation algorithm to nonparametric signal detection is described. The analytical result shows that our machine nearly converges to the optimal machine without supervision.
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