How Computer Vision Works

Computer vision mimics how the visual cortex of the human brain works. Like our brains, it can recognize and classify what it sees and even infer information from new data. But first, it must be trained using a type of AI called deep learning.

Training a computer vision application requires inputting large amounts of data into a use case-specific deep learning model. The more data that the model analyzes, the better the application is with recognizing what it has been developed to identify. For example, for a computer vision application to recognize a bird, it must first learn what a bird looks like. It does this by analyzing thousands, maybe even millions, of images of birds of different sizes, colors, and characteristics.

Equipped with this information, the application has the knowledge it needs to interpret and infer information from digital images.

Convolutional Neural Networks

Deep learning models used in computer vision are from a family of algorithms known as convolutional neural networks (CNNs). These networks analyze the RGB values embedded in pixels to detect identifiable patterns. CNNs can be developed to evaluate pixels based on a wide range of features that include color distribution, shape, texture, and depth, and accurately recognize and classify objects.