Act 12: Color Image Segmentation

In this activity, we try to single out a particular region in a given image by taking a cropped image of the region's surface, and implement it using both parametric segmentation and non-parametric segmentation methods and compare their outcomes. We make use of the image below:

Our region of interest is from that of the silver mouse:

Applying parametric segmentation on the original image using the above region of interest, we get the image below:

As said in the the activity manual, histogram backprojection is similar to what was done in Activity 4, except that the lookup histogram is now two-dimenstional. Thus for the nonparametric part, the following histograms were obtained from the p(r) and p(g) values of the ROI:

Applying histogram backprojection yields the result below


This time, we make use of a portion of the wooden table as a region of interest:

Using parametric segmentation:

The histogram for the nonparametric segmentation is shown below

And the resulting image from histogram backprojection is:

Note that the histograms above correspond properly to the normalized color chromaticity space shown below. The silver mouse appears much more bluish looking and shinier, hence it being closer to both the white and blue regions of the color chromaticity space. On the other hand, the wood ROI, while not exactly white, appears in a region where the colors mix.

It can be seen from the images that the parametric segmentation makes the region of interest much more visible. However, the nonparametric segmentation somewhat recovers specular reflection, as can be seen in both images. Parametric segmentation is more useful when you need to clearly outline the parts of the image that correspond to your region of interest. Nonparametric segmentation may be more useful for manipulating your image, such as changing the regions of interest's colors, hue, brightness, etc.

I will give myself a grade of 8/10 for this activity, due to having finished the exercise. The reduced grade is for not completely understanding the exercise. Hopefully, it will all be explained in the next meeting. I would like to thank Gilbert for helping me with the code in this exercise.