scholarly journals 2D Voronoi Coverage Control with Gaussian Density Functions by Line Integration

2017 ◽  
Vol 10 (2) ◽  
pp. 110-116
Author(s):  
Naoki HAYASHI ◽  
Kohei SEGAWA ◽  
Shigemasa TAKAI
Keyword(s):  
Density Functions ◽  
Coverage Control ◽  
Gaussian Density

scholarly journals Modelling with star-shaped distributions

2020 ◽  
Vol 8 (1) ◽  
pp. 45-69
Author(s):  
Eckhard Liebscher ◽  
Wolf-Dieter Richter
Keyword(s):  
Probabilistic Models ◽  
Arbitrary Dimension ◽  
Probability Density Functions ◽  
Density Functions ◽  
Wide Range ◽  
Gaussian Density ◽  
Spherical Distributions ◽  
Data Points ◽  
Non Gaussian ◽  
General Method

AbstractWe prove and describe in great detail a general method for constructing a wide range of multivariate probability density functions. We introduce probabilistic models for a large variety of clouds of multivariate data points. In the present paper, the focus is on star-shaped distributions of an arbitrary dimension, where in case of spherical distributions dependence is modeled by a non-Gaussian density generating function.

scholarly journals Optimal Threshold Computing in Automatic Image Thresholding using Adaptive Particle Swarm Optimization

2012 ◽  
Vol 10 (5) ◽  
Author(s):  
P. Moallem ◽  
N. Razmjooy
Keyword(s):  
Particle Swarm Optimization ◽  
Detection Rate ◽  
Particle Swarm ◽  
Correct Detection ◽  
Optimal Threshold ◽  
Density Functions ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Wide Range ◽  
Gaussian Density

histogram which can be modeled as a mixture of two Gaussian density functions, estimating these densities inpractice is not simply feasible. The objective of this paper is to use adaptive particle swarm optimization (APSO) forthe suboptimal estimation of the means and variances of these two Gaussian density functions; then, the computationof the optimal threshold value is straightforward. The comparisons of experimental results in a wide range of complexbimodal images show that this proposed thresholding algorithm presents higher correct detection rate of object andbackground in comparison to the other methods including Otsu’s method and estimating the parameters of Gaussiandensity functions using genetic algorithm (GA). Meanwhile, the proposed thresholding method needs lower executiontime than the PSO-based method, while it shows a little higher correct detection rate of object and background, withlower false acceptance rate and false rejection rate.

Stock Identification of Sockeye Salmon (Oncorhynchus nerka) with Scale Pattern Recognition

1982 ◽  
Vol 39 (4) ◽  
pp. 611-617 ◽  
Author(s):  
Rodney C. Cook
Keyword(s):  
Sockeye Salmon ◽  
Classification Scheme ◽  
Oncorhynchus Nerka ◽  
Measurement Data ◽  
Gulf Of Alaska ◽  
Error Rates ◽  
Classification Error ◽  
Density Functions ◽  
Stock Identification ◽  
Gaussian Density

The non-Gaussian density functions underlying polynomial discrimant functions are employed in a classification scheme designed for sockeye salmon (Oncorhynchus nerka). A leaving-one-out approach is used to estimate the smoothing parameters in the density functions and to obtain nearly unbiased estimates of expected actual error rates in the classification scheme. The result is that all available observations of known origin may be used to determine the discriminant rule and estimate classification error rates. These are needed to obtain point estimates of the proportions of subpopulations present in areas of intermingling. Several additional improvements over the polynomial discriminant method are noted. The scheme is applied to scale measurement data of sockeye salmon from Bristol Bay, the Gulf of Alaska, and the Kamchatka Peninsula.Key words: stock identification, discriminant analysis, sockeye salmon

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