GV12/3072 Coursework 1

This is the first coursework for course GV12/3072 on Image Processing. The subject of the coursework is simple image segmentation. For a basic pass in the coursework, you need to make a good attempt at the core section. If that's all you do you will get a grade of 50%. To get full marks you need to have a good try at one or more of the additional sections.

You should use Matlab to complete this assignment.

Hand in a short report on your work, which should be two written sides of A4 at most, plus any pictures and plots showing your results and a print out of your code. The short report should start with a brief list of what you have attempted and to what extent you have acheived each item. Next, write a short description of the methods you used for each part together with any conclusions you have drawn from your experiments.

The hand-in deadline for this coursework is 12:00 on 20th October. Hand in your report to JJ or Tricia in the CS departmental office. Make sure you attach a completed coursework cover sheet to your work.

Core section

Here is a classic image processing test-image called girlface:

We would like to separate the skin region from the background of the image and surrounding parts of the head. This is a tough image, so don't expect the result to match what a human would accomplish.

Download the image above and plot a histogram of the grey levels it contains.

Write a short program to threshold the image and try to identify a good threshold by trial and error.

Create a ground truth segmentation. You can do this using Matlab (look up the function roipoly), or using other painting tools.

Use your ground truth segmentation to plot an ROC curve for your thresholding algorithm. What threshold does the ROC curve suggest? You may have to implement an algorithm to find the point closest to a desired Operating Point. How does it compare to your trial and error estimate?

Use another image in addition to this one, but clearly define how that segmentation task has different challenges to the girlface.

Additional section You do not need to do all of this, but to get more than a basic pass have do one part at least.

Examine the effects of noise on the performance of your segmentation algorithm. Add various levels of synthetic noise to the face image and see how the performance changes. You can choose from several types of noise, but be careful that the image grey levels remain valid after you add the synthetic noise.

Implement a region growing algorithm and compare its performance at segmenting the face in the image above with the performance of the thresholding algorithm. Show the seeds you chose.

Attempt to improve segmentation by first normalizing by a hand-made background. Where does this help or hurt?