Detect black dots from color background

Question

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←).

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 (0, 44, 65) or (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.

Answer 1

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):

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.

Answer 2

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.')

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...