Detecting corners in an image

Question

I am currently trying to detect the corners in an image using a tecnique called Harruis Corner detector, I am using some code in Matlab to help me do this but it is not working.
Below I have the code in java and MatLab
Below is my code in java
private int[] pixels;
private int[] grey_scale_image;
private double[][] greylevels;
private BufferedImage image;
private Image result_image;
private int[] convolvedImage;
Point[] corners = new Point[100];
Pixel[][] pixelObjects = new Pixel[256][256];
int[][][] data;// = new int[][][];
double[] result223;
* Constructor for objects of class HarrisCorner
public HarrisCorner(BufferedImage image) {
this.image = image;
int width = image.getWidth();
int height = image.getHeight();
pixels = new int[width * height];
grey_scale_image = new int[pixels.length];
handlepixels(image, 0, 0, width, height);
greylevels = new double[width][height];
for (int j = 0; j < height; j++) {
for (int i = 0; i < width; i++) {
greylevels[i][j] = grey_scale_image[j * width + i];
double[][] kernel = {{-1, 0, 1} , {-1, 0, 1}, {-1, 0, 1}}; // -1 0 1; -1 0 1; -1 0 1 / 3 (look into that)
// print(kernel);
System.out.println("\n\n");
double[][] kernel1 = {{-1, -1, -1} , { 0, 0, 0}, {1, 1, 1}};
double[][] smooth_kernel = {{1, 1, 1}, {1, 1, 1}, {1, 1, 1}};
Matrix Ix = new Matrix(convolution2D(greylevels, width, height, kernel,
kernel.length, kernel[0].length));
Matrix Iy = new Matrix(convolution2D(greylevels, width, height, kernel1,
kernel.length, kernel[0].length));
System.out.println(Ix.getArray().length + " " + Ix.getArray()[0].length);
System.out.println(Iy.getArray().length + " " + Iy.getArray()[0].length);
Matrix Ixy = Ix.times(Iy);
Matrix Ix2 = Ix.times(Ix);
Matrix Iy2 = Iy.times(Iy);
Matrix Ixy2 = Ixy.times(Ixy);
//compute kernel using guassain smoothing operator guassain_kernel()
Matrix GIx2 = new Matrix(convolution2D(Ix2.getArray(),
Ix2.getArray().length,
Ix2.getArray()[0].length,
guassain_kernel(), smooth_kernel.length,
smooth_kernel[0].length));
Matrix GIy2 = new Matrix(convolution2D(Iy2.getArray(),
Iy2.getArray().length,
Iy2.getArray()[0].length,
guassain_kernel(), smooth_kernel.length,
smooth_kernel[0].length));
Matrix GIxy2 = new Matrix(convolution2D(Ixy2.getArray(),
Ixy2.getArray().length,
Ixy2.getArray()[0].length,
smooth_kernel, smooth_kernel.length,
smooth_kernel[0].length));
//c = (GIx2 + GIy2) - 0.04 * (GIx2 .* GIy2 - GIxy2.^2);
Matrix M = (Ix2.plus(Iy2)).minus( ( (Ix2.times(Iy2)).minus(Ixy2.times(Ixy2))).times(0.04));
//Matrix detM = (Ix2.times(Iy2)).minus(Ixy2.times(Ixy2));
//Matrix traceM = Ix2.plus(Iy2);
//Matrix M = detM.minus((traceM.times(traceM)).times(0.04));
// double[] row2 = M.getRowPackedCopy();
// Arrays.sort(row2);
// double theCorner = row2[row2.length-100];
// System.out.println("theCorner" +theCorner);
// double[][] responses = M.getArray();
// for(int i = 0; i < responses.length; i++) {
// for(int j = 0; j < responses[0].length; j++) {
// if(responses[i][j] <= theCorner)
// responses[i][j] = 0;
//double[][] ww = M.getArray();
double[][] cr22 = wrapBorder(Iy.getArray());
//double[][] corner_response = wrapBorder(responses);
result223 = new double[pixels.length];
for (int j = 0; j < greylevels[0].length; ++j) {
for (int i = 0; i < greylevels.length; ++i) {
pixelObjects[i][j].setValues(i,j,cr22[i][j]);
if(corner_response[i][j] != 0) {
//cr22[i][j] =
result223[j * (greylevels.length) + i] = -1;//((Color.red).getRGB());//cr22[i][j];
else
result223[j * (greylevels.length) + i] = cr22[i][j];//greylevels[i][j];
//System.out.println((Color.red).getRGB());
//scaleAllGray(result223);
convolvedImage = doublesToValidPixels(result223);
for (int i = 0; i < corners.length; i++) {
int index = row2.length - corners.length + i;
double pixel = row2[index];
for(int h = 0; h < 256; h++) {
for(int w = 0; w < 256; w++) {
Pixel p = pixelObjects[w][h];
if(p.getPixel() == pixel) {
corners[i] = new Point(p.getX(),p.getY());
//if(i==99) break;
This is the code in Matlab :
% % HISTORY
% 2001 Philip Torr ([email protected], [email protected]) at Microsoft
% Created.
% Copyright � Microsoft Corp. 2002
% REF:     "A combined corner and edge detector", C.G. Harris and M.J. Stephens
%     Proc. Fourth Alvey Vision Conf., Manchester, pp 147-151, 1988.
%%to do: we might want to make this so it can either take a threshold or a fixed number of corners...
% c_coord is the n x 2 x,y position of the corners
% im is the image as a matrix
% width is the width of the smoothing function
% sigma is the smoothing sigma
% subpixel = 1 for subpixel results (not implemented yet)
%%%%%bugs fixed Jan 2003
function [c_coord] = torr_charris(im, ncorners, width, sigma, subpixel)
if (nargin < 2)
error('not enough input in charris');
elseif (nargin ==2)
width = 3; %default
sigma = 1;
end
if (nargin < 5)
subpixel = 0;
end
mask = [-1 0 1; -1 0 1; -1 0 1] / 3;
% compute horizontal and vertical gradients
%%note because of the way Matlab does this Ix and Iy will be 2 rows and columns smaller than im
Ix = conv2(im, mask, 'valid');
Iy = conv2(im, mask', 'valid');
% compute squares amd product
Ixy = Ix .* Iy;
Ix2 = Ix.^2;
Iy2 = Iy.^2;
Ixy2 = Ixy .^2;
% smooth them
gmask = torr_gauss_mask(width, sigma);
%gim = conv2(im, gmask, 'valid');
%%note because of the way Matlab does this Ix and Iy will be width*2 rows and columns smaller than Ix2,
% for a total of (1 + width)*2 smaller than im.
GIx2 = conv2(Ix2, gmask, 'valid');
GIy2 = conv2(Iy2, gmask, 'valid');
GIxy2 = conv2(Ixy2, gmask, 'valid');
% computer cornerness
% c = (GIx2 + GIy2) ./ (GIx2 .* GIy2 - GIxy2 + 1.0);
%%%one problem is that this could be negative for certain images.
c = (GIx2 + GIy2) - 0.04 * (GIx2 .* GIy2 - GIxy2.^2);
%figure
%imagesc(c);
%figure
%c is smaller than before got border of 2 taken off all round
%size(c)
%compute max value around each pixel
%cmin = imorph(c, ones(3,3), 'min');
%assuming that the values in c are all positive,
%this returns the max value at that pixel if it is a local maximum,
%otherwise we return an arbitrary negative value
cmax = torr_max3x3(double(c));
% if pixel equals max, it is a local max, find index,
ci3 = find(c == cmax);
cs3 = c(ci3);
[cs2,ci2] = sort(cs3); %ci2 2 is an index into ci3 which is an index into c
%put strongest ncorners corners in a list cs together with indices ci
l = length(cs2)
lb = max(1,l-ncorners+1);
cs = cs2(lb:l);
ci2s = ci2(lb:l);
ci = ci3(ci2s);
corn_thresh = cs(1);
disp(corn_thresh);
%row and column of each corner
[nrows, ncols] = size(c);
%plus four for border
% c_row = rem(ci,nrows) +4;
% c_col = ( ci - c_row )/nrows + 1 +4;
border = 1 + width;
c_row = rem(ci,nrows) + border;
c_col = ( ci - c_row +2)/nrows + 1 + border;
% %to convert to x,y we need to convert from rows to y
c_coord = [c_col c_row];
%see Nister's thesis page 19.
if subpixel
disp('subpixel not done yet')
end
%display corners....
%hold on
%          plot(c_col, c_row, '+');
%          plot(c_coord(:,1), c_coord(:,2), '+');
% hold off
%index runs
% 1 4
% 2 5\
% 3 6
%ci = ci + 4 * nrows + 4;
%ci = (nrows + 4) * 4 + 4 + ci;
%c_patches = [gim(ci - nrows) gim(ci - nrows-1) gim(ci - nrows+1) gim(ci-1) gim(ci) gim(ci+1) gim(ci+nrows) gim(ci+nrows+1) gim(ci+nrows-1)];
% hold on
%      imagesc(im);
%          plot(c_col, c_row, 'sw')
% hold off
%      size(im)
%           size(cp)
%          imr = im2col(im, [5,5])';
%          im_corr = imr(ci);
%      im(c_row(:)-1, c_col(:)-1)
%          each row is a 3x3 matrix
%      c_patches = [ im(c_row-1, c_col-1) im(c_row-1, c_col) im(c_row-1, c_col+1) ...
% im(c_row, c_col-1) im(c_row, c_col) im(c_row, c_col+1) ...
% im(c_row+1, c_col-1) im(c_row+1, c_col) im(c_row+1, c_col+1) ];
%c_patches = [im(ci-1) im(ci) im(ci+1)];
%c_patches = [im(ci)];
Any help would greatly appreciated

Answer 1

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