I am implementing an algorithm to find an ellipse at a cloud of data points.
I am trying to implement this algorithm, that seem to work well but is not in MatLab format: http://www.nlreg.com/ellipse.htm
Does anyone would like to discuss or exchange ideas about that implementation?
My code is written below. I cannot continue because the command solve is to lazy...
name = 'cloud_of_points.png' ;
Im = imread( name ) ;
Im = rgb2gray( Im ) ;
Im = 255 - Im ;
[ Y , X ] = find( Im ) ;
DATA = [ X' ; Y' ] ;
CG = mean(DATA',1) ;
Xcenter = CG(1) ; %X coordinate of center of ellipse
Ycenter = CG(2) ; %Y coordinate of center of ellipse
TiltAngle = 0 ; %Rotation of ellipse in counter-clockwise direction (radians)
DataAngle = atan( Y - ones(length(Y),1) * Ycenter ./ X - ones(length(X),1) * Xcenter ) ;
DataDistance = sqrt( ( X - ones(length(X),1) * Xcenter ).^2 + ( Y - ones(length(Y),1) * Ycenter ).^2 ) ;
% Compute the angle for the point on the ellipse with the tilt angle.
Angle = DataAngle - ones(length(DataAngle),1) * TiltAngle ;
% Compute the radius of the ellipse (distance from center to perimeter) for
% this data angle. (Uses polar coordinate equation for an ellipse.)
syms Ydim Xdim
r = sqrt( ( (ones(length(Angle),1) * Xdim).^2 .* (ones(length(Angle),1) * Ydim).^2 ) ./ ( ( ones(length(Angle),1) * Xdim .* sin(Angle) ).^2 + ( ones(length(Angle),1) * Ydim .* cos(Angle) ).^2 ) ) ;
%Compute the difference between the distance for the data point and the ellipse.
Deviation = DataDistance - r ;
%Minimize the sum of squared deviations.
Deviation_quadrado = Deviation.^2 ;
i_final = length( Deviation_quadrado ) ;
S = 0 ;
for i = 1 : i_final
S = S + Deviation_quadrado(i,1) ;
Eq1 = diff( S , Xdim ) ;
Eq2 = diff( S , Ydim ) ;
[ Xdim , Ydim ] = solve( Eq1 , Eq2 )
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