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# Feature Extraction

[Laboratory]

A Pattern Recognition system is composed of

1. Pre-processing
2. Feature Extraction
3. Classification

Feature Extraction is a crucial step in Pattern Recognition. It is responsible for measuring features of objects in an image.

In this experiment we have a binary image with different objects. The feature used in this illustrative example is the `first invariant moment`. It measures the spread of pixels from the centroid of the object.

Original binary image

Labeling is an intermediate step in feature extraction. It allows individual measurements of the objects. The maximum pixel value of the labeled image shown below gives us the number of objects, 28 objects. Note that there are three very small objects that cannot be seen at first sight.

Labeled image

Based on the `first invariant moment` attribute of each object, it is possible to plot and visualize the graph below. We can count 13 objects with small values, which correspond to the ring screws. There are 9 large values corresponding to the nails and tee-pins. There are also 3 objects with measurements with this attribute closed to zero which correspond to the three small noise dots in the image.

Region number by first invariant moment

- Lab: Feature Extraction