Pattern recognition 1

Pattern recognition is an important aspect in the quality control of products in today's society. Therefore it is important to obtain accurate machine vision else subpar products may be produced. In the next few activities, we will discuss different techniques for pattern recognition.

In this activity, we discuss the use of minimum distance classification for pattern recognition.

If we define a representative of class ωj to be its mean feature vector then:

where xj is the set of ALL feature vectors in class ωj and Nj is the number of samples in class ωj. The 'closeness' of an object to the representative can then be defined by the euclidean distance:

In order to determine which class the object belongs to, we compute for the smallest distance:

And the object belongs to the class with the smallest distance.

We test this algorithm for 4 classes

1 peso coin

Leaf

Card

5 peso coin

The patterns were recognized to an accuracy of 75%. However what was weird was that all the leaves were considered as 1 peso coin and this was the source of my errors.

All in all this was an easy activity so I give myself a 8/10 since my algorithm only have 75% accuracy.