For this activity, we tried to simulate the human brain’s capacity to differentiate objects. We extracted features of different objects that can be compared (e.g. RGB components, area, eccentricity, etc). If a test object has features that is close to the features of a certain class, we assign it to that class.
First a training set is prepared to have a basis of comparison. The test objects are the following
Figure 1. Training set.
The feature of each class is plotted (i.e. RG components, eccentricity).
Figure 2. Plot of the features of the training set.
To determine which class a test object belongs, we used the Euclidean distance
Where dj represents the distance to class j, x is a matrix containing the features of the test object (the superscript T means transpose) and mj is the mean of the features of the class j. The test object is assigned to the class that gives the largest d. The results are as follow
Table 1. Summary of results.
The result shows 100% accuracy of the method for the chosen test objects. However it should be noted the closeness of values for the coin test objects indicating that the method and/or the features chosen is insufficient to create a sharp distinction between very similar objects.
For this activity, I'll give myself a 10 for completing it and for having satisfactory results.
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