The power of Machine Learning applied to Image Recognition with CNN

Example of Image Recognition

Image Recognition is the process of identifying a feature or a object in an image or a video (that basically is a sequence of image where time is the third dimension). This concept is used in many processes for automation and it can be applied in several fields as process monitoring, surveillance, medical diagnosis and so on.

Image Recognition is made possible with the CNN, Convolutional Neural Network that are becoming also more popular than the ANN, Artificial Neural Network as shown in the graph below (Google trends)

Trend of the last 5 years of ANN vs CNN

But how does CNN works? It takes an image as input, and, after the input processing, the output will be the classification of the image. In order to classify the image, we see the image as an array where every single element is a pixel.

If we consider a coloured image 4x4 pixels, we have to consider 3 array for RGB colours (Red, Green and Blue). Every single value of every array can be from 0 to 255.