The idea of machines that are able to see and act instead of humans isn’t a fresh one. Since the times of Herbert Wells people haven’t stop dreaming of them. Being once the main theme of sci-fi stories, the idea turned into reality nowadays. The terms “computer vision” and “machine vision” have already become usual for both pros and amateurs in the AI field. However, there’s one problem. They are not always used correctly. Do you personally see the difference between these two terms? If not, machine vision vs computer vision topic may arouse your deep interest.
This article aims at throwing the light on machine vision vs computer vision issue. It will help you take a closer look at what these terms mean. What’s more, we’ll also focus on the practical use of both technologies and the benefits they give. Hope after reading this post, you’ll clearly see the difference between computer and machine vision. As a result, you’ll never confuse the terminology again.
Machine Vision vs Computer Vision: Know the Basics
Of course, talking about machine vision vs computer vision is just impossible without learning the basics. Therefore, first things first. Let’s find out what the two terms mean in general.
What Is Computer Vision?
Frankly speaking, there’s no one universal definition for computer vision. Different sources explain it in various ways. According to Wikipedia, computer vision is a field that embraces methods used to acquire, process, analyze, and understand images. It interprets high-dimensional data from the real world producing numerical or symbolic information. Ballard and Brown’s textbook defines computer vision as the “construction of explicit, meaningful descriptions of physical objects from images”. Whereas Trucco and Verri state that computer vision lies in “computing properties of the 3-D world from one or more digital images”.
Whatever definition you go for, the essence stays the same. Computer vision is all about extracting information about an object (scene) via computer analysis of its image or sequence of images. It employs optical character recognition, image recognition, video recognition, video tracking, and other algorithms to make the most of the digital visual data.
As usual, a computer vision system consists of the following components:
- An image capture device (mostly a camera with an image sensor and a lens);
- Lighting appropriate for the specific application;
- An image capture board (frame grabber or digitizer);
- Image processing software.
In fact, the computer vision system approximately resembles the human vision. An image capture device serves as human eyes while image processing software works like a human brain. As a result, we get the precious information simply irreplaceable for many business fields.
Actually, the applications of computer vision are more than numerous. They include agriculture, geoscience, biometrics, augmented reality, medical image analysis, robotics, industrial quality inspection, security and surveillance to name just a few.
What Is Machine Vision?
As a rule, the term “machine vision” refers to the industrial usage of computer vision for automatic inspection, process control, and robot guidance. In other words, it’s the application of computer vision to factory automation. We turn to machine vision when we need to execute a certain function or outcome on the basis of the image analysis performed by the vision system. The software the machine vision system uses helps identify the pre-programmed features. Such a system triggers a variety of set actions according to the findings.
The machine vision process begins with imaging. Next, the automated analysis of the image and extraction of the required information come. Finally, the solution is created. To put it simply, a machine vision system resembles a human inspector who visually controls the quality of the products on assembly lines. Its “eyes” (digital cameras) and its “brain” (image processing software) are able to perform similar inspections. As a result, it makes decisions on the basis of the digital images analysis.
A machine vision system includes:
- An image capture device (a camera with an optical sensor);
- Lighting appropriate for the specific application;
- Camera interface card for a computer (frame grabber);
- Computer software for processing images;
- Digital signal hardware for reporting the results.
Traditionally, machine vision systems are programmed for performing narrowly defined tasks. For instance, they can count objects on the conveyor, search for defects, or read serial numbers. Unlike humans, they don’t possess the intelligence or learning capability. However, they are really helpful in many ways due to their high speed, accuracy, and ability to operate 24/7. Primarily, machine vision systems are used for image-based automatic inspection, sorting, and robot guidance.
So, What Is the Key Difference Between Machine Vision vs Computer Vision?
Perhaps, you’ve already realized that computer vision and machine vision differ not only in their names. Though they are two overlapping technologies and the boundaries between them are often blurred, they aren’t the same thing.
To begin with, computer vision doesn’t depend on machine vision. It can be used separately for a wide range of fields. On the contrary, machine vision can’t exist without computer vision because it employs computer vision algorithms. It goes without saying that all of you have drawn a family tree at the primary school. If you decide to place computer and machine vision on such a tree, machine vision will be, probably, the child of computer vision.
Next, computer vision is more a technique, whereas machine vision is more about specific industrial applications. In other words, computer vision is a scientific domain while machine vision is an engineering one.
Finally, used in industrial settings, machine vision deals with light and motion that are controlled. Besides, the viewed objects are already known and the observed events are predictable. Computer vision often deals with the objects of the “outside world” and their activities which are uncontrolled and sometimes quite unpredictable.
Machine Vision vs Computer Vision: Two AI Technologies in Action
It’s a common fact that it’s much easier to understand the difference between two technologies while seeing their practical implementation. Therefore, talking about machine vision vs computer vision we decided to show them in action.
Machine Vision in Use
As we’ve already mentioned, machine vision has found its major implementation in the manufacturing process. Here are just a few examples of the industrial machine vision everyday usage.
It’s common knowledge that many manufacturing processes require high accuracy to within a millimeter. Thanks to machine vision, it’s not a problem today. Machine vision software is capable of identifying and recognizing a variety of characteristics of the item. That’s why it’s irreplaceable for robotic guidance. Machine vision systems are able to locate the position and orientation of a part and evaluate its accuracy concerning a specified tolerance, angle, etc. Such info is helpful for measuring and verifying assembly. Besides, they can report the location and orientation of a part in 2D or 3D space to the robot locating the part or machine controller aligning the part.
Though human vision is flexible, it’s not capable of making fast, precise, repetitive measurements. That’s when machine vision is of great help. A machine vision system can calculate the distances between two or more points or geometrical locations on an object with pixel accuracy. It can perform thousands of measurements per second. What’s more, it does a good job even with such tricky calculations as circularity. A machine vision system defines if the measurements meet expectations. In case they don’t, it sends a signal. As a result, the object is ejected from the line because it has some form of a production error.
It’s quite obvious that the reputation of the company fully depends on the quality of the products. That’s why it’s so important to detect defective items before they leave your factory. Machine vision systems can check products for defects by automatically detecting and classifying them. Defects can be either cosmetic or affect the product functionality. Whatever the defect is, the system can react with a sound alarm or shut down the production.
It doesn’t matter whether your products come out in paper boxes, cans, containers or glass bottles. In any case, you have to be sure that they are packed in an appropriate way. That’s when machine vision systems are highly helpful. While dealing with bottles they can verify the fill level as well as detect open, cocked, and improperly torqued caps. When it comes to the box package, they can detect damaged or incorrectly closed boxes. What’s more, they can check that all edges are straight and parallel and nothing protrudes from the box.
Computer Vision in Use
Compared to machine vision, computer vision has found implementation in a broader range of fields. Actually, it is successfully used in each and every sphere that employs various cameras, drones, remote sensing, etc. Here are a few of its numerous usages.
Nowadays thousands of laboratories worldwide employ computer vision systems as the core of their scientific experiments and studies. Chemical and physical labs get access to the processes not visible for a human naked eye. Scientists can effortlessly examine and track particles or droplets, for instance. This means that they have a unique opportunity of getting the exhaustive information they need. With computer vision technologies, biologists are capable of studying animal behavior. It is done either at the lab, or in the wild. Medical specialists can benefit from computer vision as well while conducting tests or working on new medicines.
Protection and Security
Perhaps, today it’s rather difficult to find a public place without cameras. It’s not surprising. The security issue is of vital importance for businesses. Moreover, it is useful for individuals who care
Natural Resources Management
Undoubtedly, natural resources are crucial for the economic development of almost any country. It doesn’t matter whether the economy is based on mining, agriculture or forestry. In any case, computer vision is a great solution for remote inspection, monitoring, and fast decision making. With accurate geospatial data available due to computer vision software, many businesses can move to the new level. Survey drone technology is a reliable way to indicate the likely presence of oil, gas, and mineral natural resources. Moreover, it’s helpful to determine how to better use land in forestry and agriculture. Furthermore, it’s useful not only for planning harvests and mining but also for preserving ecosystems.
It’s quite evident that taking right marketing decisions requires careful data collection and analysis. That’s why a lot of retail businesses rely on computer vision technologies in improving their marketing strategy. In order to increase sales, retail stores examine the flow of their visitors. Firstly, they determine the busiest hours of their shops. Secondly, they define the dwell time their customers spend in certain departments. Thirdly, they study the emotions that every product evokes and find out those that are in demand. Finally, they identify VIPs in a timely manner and, of course, authenticate known shoplifters to avoid thefts.
Machine Vision vs Computer Vision: The Bottom Line
As you see, machine vision vs computer vision are different AI technologies. However, the benefits they give are alike. They enable to reduce cost, save time and effort, and significantly increase the efficiency of any business.
Do you have any thoughts concerning machine vision vs computer vision question? Feel free to share them in the comments section below.