TL;DR

Computer vision is the process of automated data extraction from images. In contrast, machine vision refers to the application of computer vision as a part of a more extensive hardware system.

Simply put, a machine vision system combines computer vision software with camera hardware to perform real-time processing.

Long-form

In artificial intelligence, the terms “computer vision” and “machine vision” are often used as synonyms but, in fact, represent different meanings.

At their core, both technologies rely on digital images and software algorithms to understand their content. While digital interpretation of visual information is involved in both technologies, their applications, purposes, and implementations significantly diverge. This article aims to clarify the differences and roles of each technology.

What is computer vision?

Computer vision is a branch of artificial intelligence that uses machine learning and neural networks to process digital images, videos, and other visual data to derive relevant information.

Processed images are broken down into pixels, and each pixel is given a label value. The values are inputted to perform a mathematical operation called convolution (an operation on two functions to produce a third function) and make predictions about what is on the image. The neural network performs convolutions and verifies the accuracy of its predictions through a series of iterations until the predictions become accurate.

It finds applications across various domains, like facial recognition, medical imaging, or text recognition (OCR). Examples of specific use cases could be:

• Detect tumours from X-ray images.
• Detect the faces of particular individuals on photos taken at events.
• Extract text from pictures of documents.

What is machine vision?

Machine vision is the technology that allows computer-based hardware to perceive their environment using cameras and other optical sensors. It uses hardware and software to conduct an end-to-end process, starting with capturing an image or set of images, analysing it, and obtaining relevant data. The system then uses the information for decision-making.

It finds applications, especially in industrial processes, where executing a specific action based on the vision system’s image processing and image analysis is necessary. Actual applications of this technology include quality inspection and defect detection on industrial production lines. Examples of specific use cases could be:

• Detect immature fruits or vegetables on the sorting line in a food processing plant.
• Inspect whether the bottle is filled, and if the cap is correctly applied in the beverage industry.
• Check the solar panel assembly process for correct construction. Verify parts, location, and measurements for maximum efficiency.

Key distinctions between computer vision and machine vision

Hardware Requirements

• ‍Computer vision is focused on software algorithms and deep learning models used to interpret and analyse visual data. It can be used alone, without the camera or sensors, or as part of a more extensive vision system. It can interpret data from a saved image, so it does not need any camera to capture images.
• ‍Machine vision systems often require specialised hardware, including high-resolution cameras, specific lighting conditions, and frame grabbers, which must be integrated into a more extensive system.

Complexity

• ‍Computer vision is commonly used to extract and use as much data as possible about an object. It encompasses many complex tasks, from object detection and image classification to scene understanding and 3D reconstruction. These tasks require sophisticated models that can generalize across diverse scenarios
• ‍Machine vision is more focused on specific, repetitive tasks. This focus allows optimizing machine vision systems for high-speed and high-accuracy performance within narrow parameters. Machine vision is typically used for quick decisions in a controlled environment.

Real-Time Processing

• ‍Computer vision’s real-time processing needs are less stringent. Apart from specific applications such as machine vision, it can operate on already stored images.
• ‍Machine vision systems require high-speed image processing and low latency to keep up with the production lines. They are optimized for speed and reliability, processing images at high throughput rates to make immediate decisions.<

Purpose

• ‍Computer vision has a broader scope, with applications spanning various fields such as healthcare, automotive, security, and retail.
• ‍Machine vision is predominantly used in manufacturing and industrial environments for quality control processes, inspection, and automation.

Machine vision and computer vision relation

Machine vision utilizes computer vision and enables its application in scenarios requiring real-time operation and fast decisions, allowing it to fulfil specific needs and challenges. As a result, in the context of applications, machine vision could be interpreted as a subset of computer vision.

Future of vision systems

The future of computer and machine vision is promising, with numerous advancements and innovations shaping its development. Some interesting aspects of their applications include:

• ‍3D vision processing: computer vision algorithms are becoming more advanced in capturing and analysing 3D images. These algorithms are combined with various methods, such as multiple cameras to capture images from different angles or light sensors to measure the time it takes for light to reflect off the surface of an object. 3D imaging allows for gathering more accurate depth and distance data and creating more precise 3D models for simulations used, for example, in medicine, and applications like digital twins.
• ‍Satellite vision: Satellites are becoming more affordable to launch and operate, and their imaging capabilities are becoming more advanced and insightful. Using computer vision technology to analyse images captured from space makes it possible to monitor various Earth’s activities, including deforestation, spreading floods and wildfires, expanding urban areas, and multiple activities in marine ecosystems, such as pollution and migration. As satellite imagery becomes more precise and detailed and computer vision algorithms become more sophisticated, we will gain more profound insights that enable us to intervene promptly and better use resources. The technology will be also widely used in the armed forces to track enemy troops.
• ‍Augmented Reality: computer vision is a crucial component of augmented reality, which overlaps digital information with the real world. Soon, new augmented reality devices will surge, including the highly anticipated sets from companies like Meta and Apple. This will make computer vision augmented tools more widely available to the public.

Summary

The lines have blurred in recent years, and computer and machine vision technologies have overlapped. Both fields have a wide range of areas, from factory manufacturing and healthcare to events and public security, and they are expected to continue growing in importance in the years to come. Awareness of these technologies’ differences is essential for understanding the nuanced subject of vision processing.

As technology continues to advance rapidly, we can expect computer and machine vision’s presence and impact on our lives to grow exponentially. Seamless human-computer interactions, amplified by visual intelligence, may become the norm sooner than we think.

Of course, the increasing ubiquity of these technologies also raises essential ethical considerations around privacy, bias, and security that must be carefully navigated. But there’s no denying the incredible potential of this technology to help solve challenges across many sectors.