Efficient Graph based image Segmentation: BuildGLTree.m - File Exchange

Code covered by the BSD License

Highlights from
Efficient Graph based image Segmentation

showcs3(varargin) SHOWCS3 M-file for showcs3.fig
CoherenceFilter(u,Options) This function COHERENCEFILTER will perform Anisotropic Diffusion of a
D=derivatives(I,option) Sobel like derivatives with Scharr rotation invariance stencil notations.
I=imgaussian(I,sigma,siz) IMGAUSSIAN filters an 1D, 2D color/greyscale or 3D image with an
[Dxx,Dxy,Dxz,Dyy,Dyz,Dzz]=Str... From Structure Tensor to Diffusion Tensor, a 3D implementation of the 2D
[Dxx,Dxy,Dyy]=ConstructDiffus... Construct the edge preserving diffusion tensor D = [a,b;b,c] such as
[Jxx, Jxy, Jxz, Jyy, Jyz, Jzz... This function calculates the 3D Structure Tensor
[Jxx, Jxy, Jyy]=StructureTens... This function calculates the 2D
[mu1,mu2,v1x,v1y,v2x,v2y]=Eig... This function computes the eigenvectors and eigen values of the 2D image
[mu3,mu2,mu1,v3x,v3y,v3z,v2x,... This function calculates the eigen values and vectors, of the 3D image hessian.
diffusion_scheme_3D_standard(... Standard Discretization of 3D image diffusion.
graphSeg(img, threshold, min_...
knng_search(img, numOnns) Inputs:
u=diffusion_scheme_2D_implici... Diffusion scheme as introduced by Weickert "Anisotropic Diffusion
u=diffusion_scheme_2D_non_neg... The Basic non_negativity diffusion equation. (Can be found in "A Scheme for
u=diffusion_scheme_2D_rotatio... Most diffusion discretizations are not rotation-invariant, probably
u=diffusion_scheme_2D_standar... The standard diffusion equation. (Can be found in "A Scheme for
u=diffusion_scheme_3D_implici... This 3D Diffusion scheme is based on introduced by Weickert
u=diffusion_scheme_3D_non_neg... This is a basic non_negativity discretization of the 3D diffusion
u=diffusion_scheme_3D_rotatio... Most diffusion discretizations are not rotation-invariant. This is an
BuildGLTree.m
CoherenceFilterStep2D.m
CoherenceFilterStep3D.m
DeleteGLTree.m
KNNSearch.m
TestMexFiles.m
compile_c_files.m
test_GraphSeg.m
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from Efficient Graph based image Segmentation by Su Dongcai
A simple and efficient graph based image segmentation algorithm

BuildGLTree.m

% BuildGLTree construct a GL-Tree from a 2D point cloud
%
% SYNTAX
% ptrtree=BuildGLTree(px,py);
%
% INPUT PARAMETERS
%   px,py: [Nx1] double vectors of the x and y coordinates of points.
%     
%
% OUTPUT PARAMETERS
%   ptrtree: a pointer to the created data structure
%
%
% GENERAL INFORMATIONS
%
%     - GLTree is an exact method no approximation is done. If you find a
%      different value from the expected this means you found a bug so please
%      send a report to the author.
%     - GLTree works on double precision so these must be double vectors.
%     - GLTree do not support 3D points.
%     - The Data structure will be computed in linear time with the number of points.
%     - GLTree is faster on uniformly random data. On sparse ones should work
%      properly but may be  slower
%
%
%For question, suggestion, bug reports
%giaccariluigi@msn.com
%
% Visit my website:
% http://giaccariluigi.altervista.org/blog/
%
%Author : Luigi Giaccari
%Last Update: 7/12/2008
%Created : 10/10/2008

Highlights from Efficient Graph based image Segmentation

Highlights from
Efficient Graph based image Segmentation