This is my sample mouse tracking result produced by image segmentation methods.
The algorithm includes: preprocessing-> pixel labeling-> Gabor filtering-> region clustering-> postprocessing
Two contributive cited Matlab functions for this project:
imgaussian.m:
function I=imgaussian(I,sigma,siz)
% IMGAUSSIAN filters an 1D, 2D color/greyscale or 3D image with an
% Gaussian filter. This function uses for filtering IMFILTER or if
% compiled the fast mex code imgaussian.c . Instead of using a
% multidimensional gaussian kernel, it uses the fact that a Gaussian
% filter can be separated in 1D gaussian kernels.
%
% J=IMGAUSSIAN(I,SIGMA,SIZE)
%
% inputs,
% I: The 1D, 2D greyscale/color, or 3D input image with
% data type Single or Double
% SIGMA: The sigma used for the Gaussian kernel
% SIZE: Kernel size (single value) (default: sigma*6)
%
% outputs,
% J: The gaussian filtered image
%
% note, compile the code with: mex imgaussian.c -v
%
% example,
% I = im2double(imread('peppers.png'));
% figure, imshow(imgaussian(I,10));
%
% Function is written by D.Kroon University of Twente (September 2009)
if(~exist('siz','var')), siz=sigma*6; end
if(sigma>0)
% Make 1D Gaussian kernel
x=-ceil(siz/2):ceil(siz/2);
H = exp(-(x.^2/(2*sigma^2)));
H = H/sum(H(:));
% Filter each dimension with the 1D Gaussian kernels\
if(ndims(I)==1)
I=imfilter(I,H, 'same' ,'replicate');
elseif(ndims(I)==2)
Hx=reshape(H,[length(H) 1]);
Hy=reshape(H,[1 length(H)]);
I=imfilter(imfilter(I,Hx, 'same' ,'replicate'),Hy, 'same' ,'replicate');
elseif(ndims(I)==3)
if(size(I,3)<4) % Detect if 3D or color image
Hx=reshape(H,[length(H) 1]);
Hy=reshape(H,[1 length(H)]);
for k=1:size(I,3)
I(:,:,k)=imfilter(imfilter(I(:,:,k),Hx, 'same' ,'replicate'),Hy, 'same' ,'replicate');
end
else
Hx=reshape(H,[length(H) 1 1]);
Hy=reshape(H,[1 length(H) 1]);
Hz=reshape(H,[1 1 length(H)]);
I=imfilter(imfilter(imfilter(I,Hx, 'same' ,'replicate'),Hy, 'same' ,'replicate'),Hz, 'same' ,'replicate');
end
else
error('imgaussian:input','unsupported input dimension');
end
end
imageDerivatives2D.m:
function J=ImageDerivatives2D(I,sigma,type)
% Gaussian based image derivatives
%
% J=ImageDerivatives2D(I,sigma,type)
%
% inputs,
% I : The 2D image
% sigma : Gaussian Sigma
% type : 'x', 'y', 'xx', 'xy', 'yy'
%
% outputs,
% J : The image derivative
%
% Function is written by D.Kroon University of Twente (July 2010)
% Make derivatives kernels
[x,y]=ndgrid(floor(-3*sigma):ceil(3*sigma),floor(-3*sigma):ceil(3*sigma));
switch(type)
case 'x'
DGauss=-(x./(2*pi*sigma^4)).*exp(-(x.^2+y.^2)/(2*sigma^2));
case 'y'
DGauss=-(y./(2*pi*sigma^4)).*exp(-(x.^2+y.^2)/(2*sigma^2));
case 'xx'
DGauss = 1/(2*pi*sigma^4) * (x.^2/sigma^2 - 1) .* exp(-(x.^2 + y.^2)/(2*sigma^2));
case {'xy','yx'}
DGauss = 1/(2*pi*sigma^6) * (x .* y) .* exp(-(x.^2 + y.^2)/(2*sigma^2));
case 'yy'
DGauss = 1/(2*pi*sigma^4) * (y.^2/sigma^2 - 1) .* exp(-(x.^2 + y.^2)/(2*sigma^2));
end
J = imfilter(I,DGauss,'conv','symmetric');
