CODEWITHSUNDEEP
algorithmmatlabopencvimage-processingcomputer-vision
WangYudong
WangYudong

Reputation: 4423

Detect black dots from color background

My short question

How to detect the black dots in the following images? (I paste only one test image to make the question look compact. More images can be found →here←).

enter image description here

My long question

As shown above, the background color is roughly blue, and the dots color is "black". If pick one black pixel and measure its color in RGB, the value can be enter image description here (0, 44, 65) or enter image description here (14, 69, 89).... Therefore, we cannot set a range to tell the pixel is part of the black dot or the background.

I test 10 images of different colors, but I hope I can find a method to detect the black dots from more complicated background which may be made up of three or more colors, as long as human eyes can identify the black dots easily. Some extremely small or blur dots can be omitted.

Previous work

Last month, I have asked a similar question at stackoverflow, but have not got a perfect solution, some excellent answers though. Find more details about my work if you are interested.

Here are the methods I have tried:

  1. Converting to grayscale or the brightness of image. The difficulty is that I can not find an adaptive threshold to do binarization. Obviously, turning a color image to grayscale or using the brightness (HSV) will lose much useful information. Otsu algorithm which calculates adaptive threshold can not work either.

  2. Calculating RGB histogram. In my last question, natan's method is to estimate the black color by histogram. It is time-saving, but the adaptive threshold is also a problem.

  3. Clustering. I have tried k-means clustering and found it quite effective for the background that only has one color. The shortage (see my own answer) is I need to set the number of clustering center in advance but I don't know how the background will be. What's more, it is too slow! My application is for real time capturing on iPhone and now it can process 7~8 frames per second using k-means (20 FPS is good I think).

Summary

I think not only similar colors but also adjacent pixels should be "clustered" or "merged" in order to extract the black dots. Please guide me a proper way to solve my problem. Any advice or algorithm will be appreciated. There is no free lunch but I hope a better trade-off between cost and accuracy.

Upvotes: 16

Views: 8338

Answers (3)

MZimmerman6
MZimmerman6

Reputation: 8603

Here is an extraodinarily simplified version, that can be extended to be full RGB, and it also does not use the image procesing library. Basically you can do 2-D convolution with a filter image (which is an example of the dot you are looking for), and from the points where the convolution returns the highest values, are the best matches for the dots. You can then of course threshold that. Here is a simple binary image example of just that.

%creating a dummy image with a bunch of small white crosses
im = zeros(100,100);
numPoints = 10;

% randomly chose the location to put those crosses
points = randperm(numel(im));
% keep only certain number of points
points = points(1:numPoints);
% get the row and columns (x,y)
[xVals,yVals] = ind2sub(size(im),points);

for ii = 1:numel(points)
   x = xVals(ii);
   y = yVals(ii);
   try
       % create the crosses, try statement is here to prevent index out of bounds
       % not necessarily the best practice but whatever, it is only for demonstration
       im(x,y) = 1;
       im(x+1,y) = 1;
       im(x-1,y) = 1;
       im(x,y+1) = 1;
       im(x,y-1) = 1;
   catch err
   end
end
% display the randomly generated image
imshow(im)

% create a simple cross filter
filter = [0,1,0;1,1,1;0,1,0];
figure; imshow(filter)

% perform convolution of the random image with the cross template
result = conv2(im,filter,'same');

% get the number of white pixels in filter
filSum = sum(filter(:));

% look for all points in the convolution results that matched identically to the filter
matches = find(result == filSum);

%validate all points found
sort(matches(:)) == sort(points(:))
% get x and y coordinate matches
[xMatch,yMatch] = ind2sub(size(im),matches);

I would highly suggest looking at the conv2 documentation on MATLAB's website.

Upvotes: 3

bla
bla

Reputation: 26069

I was curios to test with my old 2d peak finder code on the images without any threshold or any color considerations, really crude don't you think?

im0=imread('Snap10.jpg');
im=(abs(255-im0));
d=rgb2gray(im);
filter=fspecial('gaussian',16,3.5);
p=FastPeakFind(d,0,filter);
imagesc(im0); hold on
plot(p(1:2:end),p(2:2:end),'r.')

enter image description here

The code I'm using is a simple 2D local maxima finder, there are some false positives, but all in all this captures most of the points with no duplication. The filter I was using was a 2d gaussian of width and std similar to a typical blob (the best would have been to get a matched filter for your problem). A more sophisticated version that does treat the colors (rgb2hsv?) could improve this further...

Upvotes: 7

gnovice
gnovice

Reputation: 125864

I was able to get some pretty nice first pass results by converting to HSV color space with rgb2hsv, then using the Image Processing Toolbox functions imopen and imregionalmin on the value channel:

rgb = imread('6abIc.jpg');
hsv = rgb2hsv(rgb);
openimg = imopen(hsv(:, :, 3), strel('disk', 11));
mask = imregionalmin(openimg);
imshow(rgb);
hold on;
[r, c] = find(mask);
plot(c, r, 'r.');

And the resulting images (for the image in the question and one chosen from your link):

enter image description here

enter image description here

You can see a few false positives and missed dots, as well as some dots that are labeled with multiple points, but a few refinements (such as modifying the structure element used in the opening step) could clean these up some.

Upvotes: 14

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