No BSD License
Highlights from
GeoML
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GeoML: intro
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ATE_EdgeDistance(varargin)
ATE_EDGEDISTANCE Computes the energy using the distance from edges.
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ATE_GradientNorm(varargin)
ATE_GRADIENTNORM Computes the energy using the norm of gradient.
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ATOptimize(img,model,free,Eex...
ATOPTIMIZE Optimizes a shape model over an image.
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GeoMLConvertToTree(model,visi...
GEOMLCONVERTTOTREE Converts a tree containing a model.
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GeoMLGenerateTree(model,visib...
GEOMLGENERATETREE Generates a tree containing a model.
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GeoMLIterate(model,join,mode)
GEOMLITERATE Iterate on a model.
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GeoMLParseModel(modelname)
GEOMLPARSEMODEL Parsing of a GeoML XML model description.
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gauss(x, sigma, mu, norm)
GAUSS Return the monodimensional gaussian function
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gaussianFilter(varargin)
GAUSSIANFILTER Filters an image using the gaussian derivative filter
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impixels(img,P)
IMPIXELS Get the values of image pixels at defined points.
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imscale(img, range)
IMSCALE Scale an image to fit the range
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imtype(img, type)
IMTYPE Chenges the type of an image
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plotpoints(X,symbol)
PLOTPOINTS This function allow to plot a series of points
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plotshape(X, closed, symbol)
PLOTSHAPE This function allow to plot a single shape
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points2dnormalize(Pi,mustRemo...
POINTS2DNORMALIZE Normalize 2d points
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points2domogenize(Pi)
POINTS2DOMOGENIZE Enshure a 3-coords omogeneous set of points
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View all files
from
GeoML
by Gabriele Lombardi
A general morphable template tool for image segmentation.
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| gauss(x, sigma, mu, norm)
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function func = gauss(x, sigma, mu, norm)
% GAUSS Return the monodimensional gaussian function
%
% Return the evaluation of the gaussian function on an array of points
%
% Params:
%
% x: The array of x points (def=[-10:10])
% sigma: The standard deviation (def=2.5)
% mu: The mean of th e gaussian (def=0)
% norm: Must be normalized? (def=true)
% Check x
if nargin<1
x = -10:10;
end
% Check sigma
if nargin<2
sigma = 2.5;
end
% Check mean
if nargin<3
mu = 0;
end
% Check norm
if nargin<4
norm = true;
end
% Computing the gaussian funcion
if norm
func = exp(-(x-mu).^2/(2*sigma^2))/(sigma*sqrt(2*pi));
else
func = exp(-(x-mu).^2/(2*sigma^2));
end
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