AI Neural Network Based Systems

In the traditional image processing approach, a machine vision algorithm provides the computer with instructions to acquire the image, sequentially analyze its characteristics, and extract the information needed to solve the vision application. Such algorithms are programmed by the user or extracted from an existing machine vision library.

Image analysis based on neural network-based systems approach the image interpretation problem from a different perspective - instead of providing all the instructions to the computer (i.e.: the algorithm), the user trains the computer by showing a dataset of images and corresponding labels that provide feedback on what should be considered a correct result. This approach is closer to how the human brain learns, hence why we say that these systems rely on Artificial Intelligence (i.e.: AI).

AI algorithms are trained on large datasets of labelled images, allowing them to learn the characteristics and features that define different objects, scenes, or structures within images. The algorithm can then identify patterns within the images of each sub-dataset that allow it to reliably determine the output results of a machine vision system.

AI software with learning capabilities is based on combinations of data vectors, whose algorithm architecture is commonly called a “neural network”.