Edge detection

Feature detection is an importatnt aspect of any image or video processing application. The given code can be used for detecting cornera and edges in a RGB or grayscale image.It is based upon the famous paper on the topic titled
 "A Combined Corner and Edge Detector" by Harris and stephens.

%%%% This function can be used for corner and edge detection
%%%% for any RGB or grayscale image. It is based upon the famous
%%%% paper on the topic titled "A Combined Corner and Edge Detector"
%%%% by Harris and stephens.
　
%%%%%%%%%%%%% input arguments
%%%%% input = The input image whose corners and edges are to be detected.
%%%%% sigma(optional) = Standard deviation of gaussian filter . Default is
%%%%% 1.
%%%%% kernelsize(optional) = The size of gaussian kernel. Default is 8.
%%%%% thresh(optional) = threshhlod value to be used as described in the
%%%%% algorithm. Default is 0.002.
%%%%% ratio(optional) = The algorithm requires that gradients be convolved
%%%%% with a larger gaussian the second time around. This is the ratio of the
%%%%% of sigma to be used second time to that used first time. Default is
%%%%% 1.5.
　
%%%%%%%%%%%% output arguments
%%%%%% O= output binary image containing detected corners and regions.
%%%%%%% Example:-
%%%%%%%%%%%% O = feature_detection('bus10.jpg',1(sigma),8 (kernelsize),
%%%%%%%%%%%% 0.002 (thresh),1.5(ratio));
　
　
function O=feature_detection(input,varargin)
%%%% validate the arguments
error(nargchk(1,5,nargin));
%%% assign values to the input paraameters as per number of arguments
%%% specified by the user.
sigma=1;
kernelsize=8;
thresh=0.002;
ratio=1.5;
if(nargin>1)
sigma=varargin{1}(:);
end
if(nargin>2)
kernelsize=varargin{2}(:);
end
if(nargin>3)
thresh=varargin{3}(:);
end
if(nargin>4)
ratio=varargin{4}(:);
end
%%%% Read the image.
I=imread(input);
%%% if the image is RGB, convert it into grayscale.
if(size(I,3)==3)
I=rgb2gray(I);
end
%%% convert the image to double and display it.
I=im2double(I);
imtool(I);
%%% smoothen the image using gaussian filter.
h=fspecial('gaussian',kernelsize,sigma);
I=imfilter(I,h,'conv');
O=zeros(size(I,1),size(I,2));
% Calculate the gradient using the 7-tap Coefficients given by Farid and
% Simoncelli given in their paper "Differentiation of Discrete
% Multi-Dimensional Signals".
p = [ 0.004711 0.069321 0.245410 0.361117 0.245410 0.069321 0.004711];
d1 = [ 0.018708 0.125376 0.193091 0.000000 -0.193091 -0.125376 -0.018708];
FX=conv2(p,d1,I,'same');
FY=conv2(d1,p,I,'same');
FX=FX.^2;
FY=FY.^2;
FXY=conv2(d1,p,I,'same');
%%%% convolve the gradients with a larger gaussian.
gauss=fspecial('gaussian',ceil(ratio*kernelsize),sigma);
FX=imfilter(FX,gauss,'conv');
FY=imfilter(FY,gauss,'conv');
FXY=imfilter(FXY,gauss,'conv');
%%% each point in the image compute the scalar interest measure and
%%% compare it with specified thresh. If greater then set the
%%% corresponding pixel in output to be 1.
for y=1:1:size(I,1)
for x=1:1:size(I,2)
mat=[FX(y,x),FXY(y,x);FXY(y,x),FY(y,x)];
V=eigs(mat);
lambda1=abs(V(1));
lambda2=abs(V(2));
calc=(lambda1*lambda2)-0.06*(lambda1+lambda2).^2;
if(calc>abs(thresh))
O(y,x)=1;
end
end
end
imshow(O);