My nieces are wriggly creatures... especially when we go out. One is liable to take off without sparing me a second glance, and one likes to hide behind bushes and yell "Boo!". My sister tends to dress them up in bright colors to make them stand out even in a crowd. Now, we'll try to let the machine find them for us.
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| Adorable nieces Bea (left) an Annika (right) (filename test.png) |
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sample cloth (spot.png) |
Colors that are close together would appear as a connected area in the normalized chromaticity space(NCS). Since it's easier to interpret data that's in two dimensions, instead of explicitly stating the RGB values, the R is mapped on the x axis, the G on the y axis, and B is determined by the equation
B = 1 - R - G
To determine the areas that are connected in the NCS, we take a spread along the R axis, a separate spread along the G axis, and multiply these two spreads together to make a symmetrical blob. Then the test picture is evaluated if each pixel's color has a high enough value in the NCS. Here's the code that does that, using a Gaussian function as the probability spread.
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| SIP code |
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| Extracted picture using a gaussian distribution
It was able to successfully extract the jackets, but it also detected the lips and part of the chin(presumably because some color from the jacket reflected off their skin, giving it a similar color). Adding the following lines to the code:
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| A little cleaning |
The connected areas which were less than 300 pixels were ignored, giving us a cleaner image.
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| Cleaner picture |
Histogram backprojection
Histogram backprojection works by taking the colors of your sample, and checking how often they appear. The test image is then evaluated - if a pixel color appears often in the sample, then it should be extracted, otherwise it is ignored.
I used the code given by Ma'am Jing to create a 2 dimensional histogram, then added the following lines at the end.
Line 20 basically says that as long as it appeared once in the sample, then it should be extracted in the test image. x and y map the red and green channels of the test image to the range 1-255 and bin it into integers. The for loops get the color of the RG channels, look it up in the hist, and place the hist value in a blank image.
It was somewhat able to get the rough area of the both of the jackets, but the extraction using the parametric method was much better. It can be noticed that Bea's jacket(from which spot was cropped), was detected much more densely than her sister's jacket. The detection can be improved by cropping a larger area next time.
Comparison
In terms of speed, the histogram backprojection should run faster, but it was actually slower in my code because of the indexing. In terms of segmentation success, backprojection's success would be very dependent on the representative sample you get. If a nearby shade does not appear in the sample, it will not be detected. On the other hand, overdetection was an initial problem using the method of segmentation by computing the probability.
Histogram backprojection would work better if the selected representative cropped image is non-uniform or has two colors, since the parametric method just takes the average and basically restricts the colors to a symmetrical blob in the NCS.
Fun stuff to do
I'm looking forward to using a similar code to keep track of my nieces in a video!
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The code used for the 2D histogram was created by Ma'am Jing. I thank Rommel Bartolome for a discussion that reaffirmed my understanding of what to do.
I'm giving myself a grade of 9/10 for being able to do both methods, the -1 point would be because the histogram backprojection was not entirely successful.
**Figure 3 comes from the Activity 11 manual prepared by Dr. Maricor Soriano.
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