What Is the Difference Between Machine Vision and Computer Vision?
The key difference in computer vision vs. machine vision is CV has a much greater processing capability, while MV facilitates simpler automated choices. Machine vision implies the use of computer vision in an industrial or practical application. With computer vision, there is a bigger emphasis on understanding and predicting.
There are common features: both involve image processing and learning from datasets, use a camera, a capture board (and/or frame grabber), lighting, and software to handle the data, and have the speed and sometimes accuracy that human eyes can’t match.
But we’re also going to focus on areas where both technologies don’t match. Follow along to find out their strengths, limitations, and best use cases.
Table of Contents
- Computer Vision Definition and Applications
- Robot Vision in Retail
- In Warehouse Management
- In Self-Driving Cars
- What Is Machine Vision: Definition and Applications
- Robot Vision and Machine Vision in Manufacturing
- Industrial Vision Systems
- In Robotics
- Robotic Vision vs. Computer Vision
- How Do Computer and Machine Vision Work?
- Input and Output Data
- What Is Meant by Machine Vision?
- How Do Machine Vision Systems Work?
- Why Is Machine Vision Used?
- What Are the Various Technologies of Machine Vision?
Computer Vision Definition and Applications
Computer vision is the field of study that aims to teach computers how to see and understand digital images and videos. It is also the theory underlying the ability of AI systems to observe their surrounding environment and act on it.
Computer vision technology merges the capabilities of biological vision systems, sensors, computers, and machine and deep learning algorithms. In other words, it aims to perform the same tasks as human vision but in a faster and more efficient way.
Robot Vision in Retail
AI-powered robot vision helps achieve operational efficiency and prompt customer service both in brick-and-mortar stores and ecommerce platforms. Here are a few examples:
- Automated checkout
- Stock visibility
- Inventory management
- Marketing and consumer behavior
- Store security management
In Warehouse Management
Computer vision systems also enable warehouse automation. To establish an intelligent warehouse management platform model, businesses can apply CV in:
- Stock management — scanning, counting, inspecting
- Autonomous mobile robots
- Dimensioning systems
- Vehicle and drone navigation
- Smart glasses
In Self-Driving Cars
The primary objective of computer vision in self-driving cars is to navigate safely, quickly, efficiently, and comfortably through an environment. Advancements in this subfield allow for innovative real-world implementations. For example, computer vision can accurately detect lanes, traffic cars, obstacles, and signals, handle autonomous trajectory planning, and even create 3D maps.
What Is Machine Vision: Definition and Applications
Machine vision captures visual data and translates it into a form of data to make sense. It combines hardware and software for operational guidance and is usually a part of a bigger system, such as product assembly or packaging machines.
The successful use of machine visioning technology involves a variety of tasks of low-to-medium complexity. But it is still essential for machines like robotic welders, conveyors, and sorters, which machine vision facilitates in seeing, analyzing, and acting without human interference.
Robot Vision and Machine Vision in Manufacturing
Machine vision has a significant impact at every stage of the manufacturing process. There are two main areas that this technology targets: enabling operators to be more efficient and accurate and seamless integration with manufacturing equipment.
Below are some of the most common uses:
- Defect detection
- Predictive maintenance
- Reading text and barcodes
- Package inspection
- Worker safety
Industrial Vision Systems
Industrial vision systems link a group of cameras to a computerized treatment. Applications are very diverse, so we will only be able to list a few of them:
- Guiding assembly
- Part classification
- Parts localization and segmentation
- Anomaly detection
- Integrity of products
Much like with other integrations, robots are equipped with machine vision to achieve greater accuracy, orientation, and understanding. “Blind” robots can only do so much. But robots that can see and understand their environment can complete many different pre-programmed tasks by recognizing which one needs to be completed. In industrial robotics, it may involve picking up a product or part of a product and locating and working on a part in any orientation.
Robotic Vision vs. Computer Vision
If you want to understand the relationship between robotic vision and computer vision, imagine a family tree, where CV is the parent. So, they’re closely related but not the same.
There is also an important distinction to make. Robot vision incorporates techniques of machine vision, but they don’t always refer to the same thing. Some machine visioning applications are rooted in robotics, which is when the terms can be used interchangeably. But some of them aren’t related to robotics or robot vision.
Robot vision has its own separate area of research. It incorporates techniques and algorithms specific to robotics, such as kinematics, reference frame calibration, and the ability to physically affect the environment. For comparison, computer vision studies a broader set of subjects like mechatronics, intelligent transport and logistics, biomedical engineering, etc.
How Do Computer and Machine Vision Work?
The three main stages in both processes are image acquisition, processing, and action. But this is a very simplistic way of looking at it; there are actually thousands of actions happening behind the scenes.
The steps performed by the vision processing system are:
- Capturing or acquiring the digital image = input. Data may be supplied by vision sensors, digital cameras, ultraviolet or infrared cameras, etc.
- Pre-processing the input and optimizing it for further steps.
- Analyzing the image based on the required task (observation/detection, measurement, identification). This is what takes the most amount of time and processing power.
- Collecting information about the extracted features and comparing those values against certain criteria.
- Generate a result = output, often on a pass-fail or go/no-go basis for machine vision and interpretation and augmentation for computer vision.
Input and Output Data
While input and output data may often overlap, we define the main differences for what is fed to the system and what will be received in the end.
|Computer vision||Real-life and synthetic image and videos, stills, and time-related input||Information on size, color intensity, and other characteristics, visual reasoning, augmented image/video|
|Machine vision||Visual data from hardware image capture systems, often in real-time||Task-specific knowledge, like pass/fail decisions, absence/presence of an object, patterns, defects, colors|
Computer vision and machine vision aren’t in competition against each other. There is a time and place for each, and their differences make it easy to decide which one is suited for a certain task. For example, while machine vision works well in object detection, product inspection, and measurements, computer vision is better equipped for medical diagnostics, face recognition, and behavior analysis.
- Both computer and machine vision handle the processing of visual data.
- Computer vision can handle more advanced tasks but requires more computational power.
- Since computer and machine vision are aimed at different tasks, they analyze different types of inputs and produce different outputs.
- Applications for both technologies are extremely varied, but computer vision goes way beyond industrial use.
What Is Meant by Machine Vision?
Machine vision usually refers to the use of image processing for automatic inspection, process control, and robotic guidance. A machine visioning system uses a camera to view an image and algorithms that process the input. By the end, the machine vision component instructs other components in the system on how to act.
How Do Machine Vision Systems Work?
Machine vision uses sensors (such as cameras), processing hardware, and software algorithms. First, the system acquires or captures visual data. Next is the most important step — processing and analyzing. Even though it is complex, modern systems can handle hundreds or even thousands of parts per minute. The output depends on the task — for example, mechanically “seeing” steps along a production line.
Why Is Machine Vision Used?
Machine vision is used to automate complex or mundane visual inspection tasks. But if we look closer, there are many different application groups. It can be used for one or several of the following tasks — object detection, measurement, flaw detection, print defect identification, identification, locating, counting, etc.
What Are the Various Technologies of Machine Vision?
The hot-topic technologies that use computer vision are multispectral/hyperspectral (quality inspection, color inspection, and process monitoring), polarization (X-ray imaging, logistics/warehousing/distribution, and web inspection), embedded vision (packaging, packaging, robotics), 3D imaging (medical imaging, motion capture, industrial design), and computational imaging (consumer electronics, microscopy, human-computer interaction).