Human vision is an infinitely beautiful and complex ability. The eyes capture light, the receptors access information and the visual cerebral cortex processes it. In recent years, many steps have been taken to extend this extraordinary ability to humans and machines. From the first model of camera, in which a small box containing a piece of paper covered with silver chloride darkened with solar exposure when the shutter is opened, up to the most modern digital cameras and machine cameras of our days. 

With machine vision cameras, man has been able to reproduce how the human eye captures light and colors. Today, thanks to the vision camera, we face a much more complex aspect: machines understand and interpret the content of an image in the same way the human brain interprets it.

What is a vision camera? 

Vision camera detects and interpret important information through a digital image, a video or other visual input. A machine vision camera can not only recognize objects, animals or people present in a digital image or a video sequence but it can also:

• Infer useful information.
• Interpret the data obtained.
• Process them and take action or make reports based on the data obtained. 

Through this process, the vision camera can understand the contents present in an image, reconstruct a context and attribute a real meaning to what it represents. If AI is the discipline that gives computers the ability to think, computer vision is the one that gives them the ability to see, understand and interpret. To function and be able to interpret the contents present within an image, computer vision systems must first be “trained” through a machine learning process and using a large number of cataloged images, which will be the basis of the dataset that will allow the algorithm to recognize the subsequent ones intelligently. 

The process is similar to the human learning system. Our eyes have trained for years and years in distinguishing objects, finally managing to understand and interpret them. A vision camera system does not have retinas, optic nerves and visual cortex and appropriately labeled cameras, algorithms, data and images are used to be trained.

How machine cameras work

Machine cameras are based on the most advanced Machine Learning techniques and a large amount of data that allow the machine to interpret and understand an image with performances comparable to human sight. But how do machines recognize the objects present in an image? 

The whole system is based on three fundamental phases:

• Image acquisition: images or video sequences are acquired by the computer, even in real-time, using photos or 3D technology and video for analysis purposes.

• Image processing: The machine can process the image through deep learning models through specific tasks. These models are trained in advance by uploading thousands upon thousands of tagged and pre-identified images.

• Interpretation of the image: finally, the machine identifies, understands and classifies the processed image and, if necessary, takes action or a signal. 

Machine cameras can perform more or less advanced operations on an image to interpret it, depending on the techniques used and the type of task performed, including:

• Image Classification: analysis of the image content and attribution of a recognition label (e.g. dog, man, lamp, etc.)
• Object Detection: identification of all the entities present within the image; 
• Face Recognition: a fundamental task for identifying people’s faces within an image ;

Machine Vision Cameraallows systems to be trained to understand the context of a photo based on a set of data. In this way, the system can understand what all the numbers it is analyzing represent. It is essential to know that a computer does not see the image as we see it but observes a set of numbers to be processed and interpreted depending on how they are organized.