A16 – Color Image Segmentation

In this activity we do image segmentation using the normalized chromaticity coordinates(NCC) of each pixel of the region of interest(ROI) of an image using their RGB values. The image and the ROI is shown above.

Since b is just 1-r-g, it is sufficient to only know the r and g values. In this way we simplify our work since we would only deal with those two values.

The first segmentation technique we used is the probability distribution estimation or the parametric segmentation. We first calculated the mean and the standard deviation of the r values of the ROI. After this, we go to the image we want to segment and then calculate the probability of each pixel having a chromaticity r to belong to the ROI by using the gaussian probability distribution given by the equation below.

We also do this for the g values with a similar equation. The joint probability is then taken as the product of p(r) and p(g) which in turn becomes our segmented image. The segmented image is shown below.

As we can see the image was segmented. The bright region is the segmented region. The darker region is the unsegmented region.

The second technique we used was histogram back projection. We first get the 2 dimensional histogram of the ROI based on its r and g values. Since both the r and g values ranges from 0 to 1 I divided it to 256 divisions which means there are 256 values in the both the r and g axis of the 2d histogram ranging from 0 to 255. The histogram is shown below.

After we get the histogram of the ROI we normalize it. For each pixel in the image we want to segment we find its r and g values and then locate it's normalized histogram value. The histogram value would now be the value of the pixel of the segmented image. The segmented image is shown below.

We see that the segmentation seems to be similar to the parametric segmentation. It is obvious that only the segment with the closest chromaticity of the ROI can be seen.

The two techniques are both good at segmenting the image. The parametric segmentation was used assuming a normal distribution but it may not always be the case. The back projection method was much faster since no further calculations of the probability distributions were needed. In this activity I think the results of the parametric segmentation was better. Using the back projection method woult make pixels which are not of the patch. equal to zero. If there were some slight variations it would not belong to be segmented anymore even if it seems to be of the same chromaticity. The parametric segmetation was based on probability so even if there were slight variations it would still be segmented as long as it is close to the chromaticity of the patch. This may explain why the parametric segmentation appears brighter and some regions are not totally dark.

I give myself a grade of 10 for this activity since I have both implemented the techniques and also seem to give a reasonable explanation of the results. Comments would be highly encourage since I want to verify if my explanations were correct.
Thank you for Ma'am Jing, Jorge, Rap, Ed and Billy for helping in this activity.