1. QUIZZ Select any one from the following six problems.
Use your computer to solve it. I do not recommend Arduino or Raspberry Pi. Use laptop or tablet.
You can use Matlab, Octave, Orange and/or OpenCV plus any general purpose language like Python or C++. You can use any software from internet, but you must to give the detailed reference to it.
Write a detailed report. Explain what you did and why.
Analyze the results using the methods that were shown and discussed in our class.

2. Problem 1. Separate matches from coins.
Use any known to you computer vision approach to do the following:
Count the number of discs.
Count the number of rectangles.
Count the total area of disks.
Count the x,y coordinates of centers of 4 selected disks.
Write a Machine Learning software to distinguish images from the left from images from the right.

3. Problem 2. Skeletonization.
Use any known to you computer vision approach to do the following:
Create or find two sets of images with an object in front: Set 1 and Set 2.
These images must clearly belong to two classes, like boxes and bottles.
Perform skeletonization (thinning) of all images.
Find some important distinguishing features that exist in the thinned images from both sets. You should be able to distinguish these images with your naked eyes looking at thinned images from set Set 1 and Set 2.
Write a Machine Learning software to distinguish images from both thinned sets.
Analyze data as it was done in the class.

4. Problem 3. Shape parameters.
Given are shape parameters from left.
Create arbitrary two sets of images A and B.
Use shape parameters from left as attributes.
Using your camera and some simple preprocessing software, calculate the vectors of attributes for images from set A and set B. Assume 8 images in each set.
Use any Machine Learning software to classify your images to classes A and B.
Analyze data as it was done in the class

5. Problem 4. Melanoma Cancer
Given are four criteria for distinguishing benign skin moles from malignant moles, as shown in the right.
You can use any images of benign and malignant moles from internet.
Using software from internet, especially those discussed in class, write a complete package to distinguish benign from malignant moles.
Of course, the quality of your software will be much below commercial software, but you will demonstrate the point of combining image processing with machine learning using various methods.
Analyze data as it was done in the class

6. Problem 5. Ovulation Prediction
Given are three phases of cervical mucus ferning under microscope: C1 – long before ovulation, C2- ovulation coming soon, C3 – Ovulation. See at the right.
You can use any additional images of these ferning images on internet.
Using software from internet, especially those discussed in class, write a complete package to distinguish between these classes C1, C2 and C3.
Of course, the quality of your software will be much below commercial and research software, but you will demonstrate the point of combining image processing with machine learning using various methods for an important problem.
Analyze data as it was done in the class
C1. Long before ovulation
C2. Ovulation coming soon
Typical Projects
C3. ovulation

7. Problem 6. Any complete system that includes both Image Processing and Machine Learning
Given are two sets of images that have some practical importance.
Examples of what we have done in past are:
Beautiful versus ugly people.
Poisonous spiders versus non-poisonous spiders.
Poisonous Oregon mushrooms versus non-poisonous.
Dogs versus cats.
Boxes and bottles.
Can beers and juice containers.
Any objects for robot theatre, like robots and small furniture.
You can use any sets of images from two classes from the internet.
Using software from internet, or software from OpenCV, Orange, etc discussed in class, and especially those discussed in class, write a complete package to distinguish between these two classes of images.
Analyze data as it was done in the class