The idea that machines can see has been there since the advent of science fiction for decades. But now it has turned into a reality. First machine vision happened. It uses existing tech to “see” steps in production lines. Recently computer vision has taken over. If you think of machine vision as a body, then computer vision is its eye. There are quite a few differences when it comes to the debate of “computer vision vs machine vision”. It depends on their functions, purposes, what they do and their efficiency.
Let’s dive a little deeper.
What is Computer Vision?
You can use computer vision on its own. It does not need to be a part of a big machine. Here the function goes beyond image processing. In computer vision, the image does not have to be a photo or video. It could be a “thermal” or “infrared sensor” image or from motion detectors or other sources.
Amidst computer vision vs machine vision, the former can process 3D and moving images as well. This includes unpredictable observations that the previous versions of this technology could not resolve.
The complex operations can handle all sorts of features within an image. Then it will analyse it and provide enriching information about those images.
Work of Computer Vision
With the advancement of the former in computer vision vs machine vision, there are multiple possible applications for it, and it keeps on multiplying. What once was something that only heavy industries could do, now everyone has access to it. It now appears on the braking systems of self-driving vehicles, computers and even our passports when we check them at airport security.
Computer Vision Examples
There are various applications of computer vision:
- Self-Driving Cars: Use of cameras and other sensors to detect objects in the vicinity of the car. This can allow cars to navigate without human input. This technology is useful for self-driving prototypes and may become widespread in the coming years.
- Pedestrian Detection: This is for public safety and autonomous driving applications. By detecting pedestrians, cars can avoid getting into accidents. They can also adapt their speed and driving patterns according to their environment.
- Barcodes/RFID: This technology instantly reads tags and barcodes on products. This allows easy inventory tracking helping to streamline their operations.
- Defective Product Inspection: When you inspect products looking for defects before they are shipped, it can save money on customer returns and product quality improvements.
You can see how the former fares in this debate of computer vision vs machine vision.
Knowing how computer vision works in the debate, shall we see the details of the latter?
What is Machine Vision?
The machine vision system uses cameras to view an image. Then computers assess it and process it to interpret the image. Machine vision encompasses the methods, technology, software and hardware that you need in visual input processing.
In simple words, it captures the images of a given environment using a special camera. Then it processes it for various applications like object detection and visual inspection devices.
Work of Machine Vision
In this debate of computer vision vs machine vision, the latter involves high-quality cameras that capture pictures of the environment. Then they take a few predefined aspects of these images for processing.
For instance, a machine vision algorithm can detect stop signs in photos. If the camera captures an image of a road with multiple houses on each side, it will look for a stop sign.
Here is how machine vision finds its use.
- Quality Control: This technology can inspect products for damages or inconsistencies. For instance, a machine vision vision may detect if a screw is missing from an assembled product.
- Guiding: In some industries, you will need to guide objects through a process or assembly line. Machine vision tracks an item’s movement and provides instructions on where it needs to go.
This technology can function alone. In this debate of computer vision vs machine vision, the first goes way beyond image processing. In computer vision, companies will not even need a photo or video.
it could be from a ‘thermal’ sensor or infrared images, motion detectors and other sources.
Also, computer vision can process 3D and moving images. This also includes assessing unpredictable observations that previous versions of this technology could not do. Complex operations detect all types of features within an image and analyse and provide information about them.
With the advancement of computer vision, potential applications for machine vision have increased.
Machine Vision Examples
Here are a few use cases of the latter for this discussion of “computer vision vs machine vision”:
- Quality Assurance: Since machine vision makes tasks quick and automated, it is helpful for visual inspection on production lines. With machine vision, they can capture images of products on the manufacturing belt and detect any anomalies.
- Object Sorting: Cameras can detect the colours and sort the object accordingly. For instance, it will group red-coloured objects. If there was an object of a different colour in that batch, the machine will remove it.
With all these let’s take a quick look at the comparison of computer vision vs machine vision.
Computer Vision vs Machine Vision: Comparative Analysis
Here’s how these two systems differ in certain aspects:
| Criteria | Machine Vision | Computer Vision |
| Image Processing Versus Analysis | The biggest difference is the use of input. Machine vision uses process information based on images and an algorithm that is trained to look for certain images. | This processes and analyses the entire image. |
| Automated Tasks | This is more applicable for automation. It has software with set, strict parameters that helps the cameras find what they are looking for. | Computer vision differs from machine vision by its ability to recognize and infer meanings from images. It learns patterns from extensive data, enabling complex tasks like image classification. |
| Visual Media | It can process images that are taken on cameras. | This can process multiple modes of visual modes includes, images, videos, thermal imaging and more. |
| Integrated versus Standalone | Machine vision systems require integration into larger setups due to software dependencies. They often accompany computer vision systems. | Conversely, computer vision operates independently, serving as a standalone solution. |
Computer vision systems extract insights from various visual inputs like images and videos. In contrast, machine vision systems depend solely on images captured by their cameras.
FAQs: Computer Vision Vs Machine Vision: Which is the Best for Supply Chains
Is machine vision an AI?
It uses AI to give industrial equipment the ability to see and analyse operations in a secure, manufacturing facility. It helps with quality control and worker safety.
What are the kinds of machine vision systems?
Machine vision cameras include line scan, area scan, and 3D scan variants. Machine vision systems execute tasks such as presence inspection, positioning, identification, flaw detection, and measurement.
What is an example of computer vision?
One of the best innovations of computer vision is the invention of automation in cars. It enables advanced safety features like collision avoidance and pedestrian detection by processing visual data from cameras to interpret and react to the surrounding environment in real time.
Conclusion
The distinction of computer vision vs machine vision marks a significant advancement in technology, transforming once-fantastical ideas into tangible reality. While machine vision focuses on image processing within strict parameters, computer vision extends beyond, recognizing and interpreting visual data with greater flexibility.
Computer vision’s capacity to handle diverse media types, including 3D and moving images, expands its applications across various industries, from self-driving cars to quality assurance in manufacturing.
Furthermore, computer vision’s standalone functionality contrasts with machine vision’s reliance on integration into larger systems. Step into the automated world of analysing items for supply chains with the latest solutions and technological advancements from Qodenext.
