SDGDEDGE - Edge detection based on gradient second derivative.

Contents

Description

Edge detection filter with 2D kernel based on the 2nd derivative in the gradient direction or its combination with the Laplacian. This function convolves the input image with set 2D Gaussian kernels and then combines the results in accordance to the formula in [MH86].

Syntax

   edgemap = SDGDEDGE(I);
   edgemap = SDGDEDGE(I, sigma, 'propertyname',propertyvalues ...);

Inputs

I : input image of size (X,Y,C), possibly multichannel with C>1.

sigma : optional standard deviation of the Gaussian filter used for smoothing of the image; default: sigma=1.

Property [propertyname propertyvalues]

'plus' : optional boolean flag for using the 2nd derivative in the gradient direction combined with the Laplacian filter (filter 'PLUS', see [VV94]) when set to true; the regular SDGD is used otherwise; default: plus=false.

'thres' : threshold (in [0,1]) used for estimating the binary map from the derivative output filter; default: thres=0.1.

'hsize' : size of the window of the filtering kernel; default: hsize is estimated depending on sigma.

Outputs

edgemap : the output edge map.

Acknowledgment

Sergei Koptenko, Resonant Medical, Montreal (Qc., Canada), www.resonantmedical.com.

References

[MH86] D. Marr and E.C. Hildreth: "Theory of edge Detection", Proceedings of the Royal Society, 207:187-217, 1980. http://rspb.royalsocietypublishing.org/content/207/1167/187

[VV94] P.W. Verbeek and L.J. Vliet: "On the location error of curved edges in low-pass filtered 2-D and 3-D images", IEEE Trans. on Pattern Analysis and Machine Intelligence, 16(7):726-733, 1994. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=297954

See also

Ressembles: EDGECORNER, CANNYEDGE, CANNYEDGEPROD, ROTHWELLEDGE, CONGRUENCYEDGE, COMPASSEDGE, ANISOEDGE, ELDERZUCKEREDGE, KOETHEDGE, PETROUEDGE. Requires: SDGDEDGE_BASE.

Function implementation

function edgemap = sdgdedge(I, varargin)

parsing parameters

error(nargchk(1, 18, nargin, 'struct'));
error(nargoutchk(1, 1, nargout, 'struct'));

if ~isnumeric(I)
    error('sdgdedge:inputparameter','a matrix is required in input');
end

p = createParser('SDGDEDGE');
p.addOptional('sigma', 1., @(x)isscalar(x) && x>=0.05);
p.addParamValue('plus', false, @(x)islogical(x));
%p.addParamValue('method', 'sdgd', @(x)ischar(x) && ...
%    any(strcmpi(x,{'sdgd','plus'})));
p.addParamValue('thres', 0.1, @(x)isscalar(x) && x<=1 && x>0);
p.addParamValue('hsize',[], @isscalar);
p.addParamValue('mu', 0, @isscalar); % not used

% parse and validate all input arguments
p.parse(varargin{:});
p = getvarParser(p);

main computation

edgemap = sdgdedge_base(I, p.plus, p.sigma, p.mu, p.thres, p.hsize);

display

if p.disp
    figure, imagesc(edgemap), axis image off, title('SDGD edge map');
    if size(edgemap,3) == 1, colormap gray;  end;
end

end