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Machine vision: everything you need to know

Machine vision is one part of artificial intelligence (AI) that is on the rise. It focuses on developing and refining techniques that allow machines to see, identify, and process images in the same way that human vision does.

In this article we will tell you about its main characteristics and what are the most common applications in the industry.

What is a machine vision system

A machine vision system is a combination of hardware and software that has the ability to capture and process image data. Currently, artificial vision systems are capable of offering high precision, great consistency and high mechanical and thermal stability.

Artificial vision systems are usually made up of a set of digital sensors inserted in industrial cameras capable of offering images and data.

The software can process, analyze, and measure a variety of data that engineers use to monitor processes and make sound decisions.

These systems are one of the industrial technological resources in which a greater number of advances have been developed in recent years.

How machine vision works

One of the fundamental components to realizing the full capabilities of artificial intelligence is giving machines the power of vision.

To emulate human sight, machines need to acquire, process, analyze and understand images. The tremendous growth in achieving this milestone was made possible by the interactive learning process made possible with neural networks .

It starts with a set of data collected with information that helps the machine learn a specific topic. If the goal is to identify cat videos as it was for Google in 2012, the dataset used by the neural networks needs to have images and videos with cats as well as examples without cats.

Each image needs to be tagged with metadata indicating the correct answer. When a neural network walks through the data and signals, an image with a cat is found; what helps to improve is the answer that is received about whether it was correct or not.

Neural networks are using pattern recognition to distinguish many different pieces of an image. Instead of a programmer defining the attributes that make a cat have a tail and whiskers, machines learn from the millions of uploaded images.

Components of a machine vision system

An artificial vision system consists of two basic components that are the acquisition of images: illumination device and image capture (camera) and the analysis of images: an image capture plate (image capturer or digitizer) and analysis software. .

The hardware configuration of machine vision systems is pretty standard. Generally, a system consists of:

  • An illumination device , which illuminates the sample under test.
  • A CCD solid state video camera , to acquire an image.
  • A frame-grabber , to perform A/D (analog-to-digital) conversion of the image capture or digitized pixels into an N by M column image.
  • A personal computer or microprocessor system, to provide image disk storage and calculation capability with vendor-supplied software and specific application programs.
  • A high-resolution color monitor , which helps visualize the images and the effects of various image analysis routines

What is machine vision used for?

Computer vision is one of the areas in Machine Learning where fundamental concepts are already being integrated into the main products we use on a daily basis. In this article I will describe the most used today:

Machine vision in autonomous cars.

It enables self-driving cars to make sense of their surroundings. The cameras capture video from different angles around the car and feed it to computer vision software, which then processes the images in real time to find road ends, read road signs, detect other cars, objects and pedestrians.

The autonomous car can then orient itself on streets and highways, avoid bumping into obstacles and safely drive its passengers to their destination.

Face recognition

Computer vision algorithms detect facial features in images and compare them to databases of face profiles. Consumer devices use facial recognition to authenticate the identities of their owners.

Social media apps use facial recognition to detect and tag users. Law enforcement agencies are also turning to facial recognition technology to identify criminals in videos.

Augmented reality and mixed reality

This technology allows computing devices such as smartphones, tablets, and smart glasses to overlay and embed virtual objects on real-world images.

Using machine vision, Augmented Reality equipment detects objects in the real world to determine locations on a device screen and place a virtual object.

For example, detect planes of tables, walls and floors, to establish the depth and dimensions of objects and be able to transform them into virtual objects in the physical world.


Machine vision algorithms can help automate tasks such as detecting cancerous moles on skin images or looking for symptoms on X-rays and MRIs.

Machine vision applications in industry

The customized implementation of an artificial vision tool allows industrial companies to develop customized functionalities such as: morphological analysis and shape defects, position markers, color and appearance analysis, foreign object detection, identification of defects and labeling quality, barcode reading; 1D, 2D, character recognition, and OCR and OCV verification.

In the manufacturing industry, companies use machine vision to identify product defects in real time. As the products come off the production line, a computer processes images or videos, and marks the different types of defects, even in the smallest products.

These functionalities are typically associated with artificial vision systems in the food and beverage sector, a sector that has been one of the pioneers as it has had to adapt to increasingly strict regulations to guarantee the quality of the products and improve the customer safety.

In turn, produce retailers can use computer vision to improve the shopping experience, increase loss prevention, and spot out-of-stock shelves. This technology is helping customers pay more quickly by facilitating the use of self-checkout machines to improve the entire checkout process.

Advantages of machine vision

The use of artificial vision is growing rapidly thanks to the discovery of advantages for industries. There are five main advantages that you should know about:

  • Processes in a simpler and faster way: it allows customers and industries to check products. Plus, it gives them access to your products.
  • Reliability: computers and cameras do not have the human factor of fatigue. The efficiency is usually the same, it does not depend on external factors such as sick leave or human errors due to exhaustion.
  • Precision: this technology ensures better precision in the final product.
  • A wide range of uses: We can see the same computer system in several different fields and activities (factories with warehouse tracking and supply shipment, and in the medical industry through scanned images, among many other options).
  • Cost reduction: time and error rate are reduced in the process.

Disadvantages of machine vision

Despite all the advantages of artificial vision thanks to the capacity of Machine Learning, we have to consider some disadvantages:

  • Need for specialists: there is a great need for specialists related to the field of Machine Learning and Artificial Intelligence. A professional who knows how these devices work and who takes full advantage of these technologies.
  • Machine vision failures: When the machine or device fails, it does not announce or anticipate that problem.
  • Image processing failure: When the device fails due to a virus or other software problems, it is very likely that the image processing will fail. So if we don’t solve the problem, the functions of the device may disappear. And it can paralyze all production.

Having knowledge in the operation of artificial vision systems will increase your skills as a professional in the industrial sector. Currently, the industry is demanding profiles that are prepared in this technology that is widely used by production process companies.

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