Activity 12: Color image segmentation

For this activity we try to distinguish ROI's based on their color hues. By changing our color coordinates such that we separate value into an independent variable, we are left with a 2D color map that disregards brightness. With value aside we may now distinguish objects based on color alone.

We achieved the actual differentiation via 2 ways: parametric and non-parametric. In the parametric method, we assume that the color distribution of our object is a Gaussian curve. It is parametric in that we may adjust the standard deviation of the spread to be more or less tolerant. In the non-parametric method, we use the objects actual color distribution and we use the resulting curve for back projection.

RESULTS

Original Image



http://www.missouriplants.com/Yellowopp/Helianthus_divaricatus_flowers.jpg

Parametric approach



Non-parametric approach

It seems that the assumption of a Gaussian distribution of colors results in the manifestation of the white noise on the ROI. This is probably a reflection of the normal distribution of noise on the original image. When we remove this assumption, we get a much more solid and accurate ROI since we use prior knowledge of the objects color distribution. I also observed in my peer's (Sison) work that this method is much more effective in differentiating adjacent colors (e.g. orange and red).

I give myself a 10/10 because I was able to execute the procedure completely and efficiently.