In this chapter, we explored the basics of object segmentation in a controlled situation, where a camera take pictures of different objects.
We learned how to remove the background and light in order to allow us to binarize our image by minimizing the noise and also three different algorithms used to divide and separate each object of an image, allowing us to isolate each object in order to manipulate or extract features. Finally, we extracted all the objects on an image, where we are going to extract characteristics of each of these objects to train a machine learning system.
In the next chapter, we are going to predict the class of any of objects in an image, and then call to a robot or any other system to pick any of them, or detect an object that is not in the correct carrier tape, and then notify to a person to pick it up.