Image Processing for recognizing 2D features

Question

I\'ve created an iPhone app that can scan an image of a page of graph paper and can then tell me which squares have been blacked out and which squares are blank.

I do th

Answer 1

To start with, this problem reminded me a bit of these demo's that might be useful to learn from:

• The DNA microarray image processing
• The Matlab Sudoku solver
• The Iphone Sudoku solver blog post, explaining the image processing

Personally, I think the most simple approach would be to detect the squares in your image.

1) Remove the background and small cruft

f_makebw = @(I) im2bw(I.data, double(median(I.data(:)))/1.3);
bw = ~blockproc(im, [128 128], f_makebw);
bw = bwareaopen(bw, 30);

2) Remove everything but the squares and circles.

se = strel('disk', 5);
bw = imerode(bw, se);

% Detect the squares and cricles via morphology
[B, L] = bwboundaries(bw, 'noholes');

3) Detect the squares using 'extend' from regionprops. The 'Extent' metric measures what proportion of the bounding-box is filled. This makes it a nice measure to distinguish between circles and squares

stats = regionprops(L, 'Extent'); 
extent = [stats.Extent];
idx1 = find(extent > 0.8);
bw = ismember(L, idx1);

4) This leaves you with your features, to synchronize or rectify the image with. An easy, and robust way, to do this, is via the Autocorrelation Function.

This gives nice peaks, which are easily detected. These peaks can be matched against the ACF peaks from a template image via the Hungarian algorithm. Once matched, you can correct rotation and scaling as you now have a linear system which you can solve:

x = Ax'

Translation can then be corrected using run-of-the-mill cross correlation against the same pre defined template.

If all goes well, you know have an aligned or synchronized image, which should help considerably in determining the position of the dots.