Computer Vision
Computer Vision (CV) is the science of reproducing regions of the complexness of the human visual system to extract valuable information from digital images or videos. CV is an area of computer science that deals with technologies and tools to make computers notice what you see and interpret the world around them. Thanks to the technological advances in AI and Machine Learning, computer vision has taken a massive leap toward the future. The idea is to make digital systems by replicating the human visual system to process, analyze, and comprehend digital images and extract meaningful insights from the real world. Computer vision aims to automate tasks that the human optical system can do. Merely put, it ensures the machines comprehend an image just the way we do or, sometimes, more practical.
Machine Vision
Machine Vision uses existing technologies and instruments to help machines accurately communicate specific data or information in new ways and involve them in solving real-world issues. The application area of machine vision is more or less particular to the industrial environments, where the tasks are very specified, and the conditions are simplified and well known. Today, it contains all industrial and non-industrial applications. One of the most common machine vision system applications in practice is development assessment in a manufacturing operation. Before designing a machine vision system, the tasks and conditions need to be considered. It is also used across varied business application areas such as inspection, guidance, identification, tracking, and more. The processing of images provided to the computer by sensors is truly the core of machine vision.
What’s The Difference between Computer vision and Machine vision
The idea of enabling machines to see and perform activities is not a new design. This idea has been around in science fiction for decades and is now very close to reality.
Machine vision appeared before computer vision. This engineering system uses existing technologies to mechanically “observe” the current stages in the production line. This helps manufacturers find defects in products before they pack their goods, or, for example, food distribution companies ensure that their food is labeled correctly.
If we look at machine vision as the body of a system, computer vision comprises the retina, optic nerve, brain, and central nervous system.
Since the advent of computer vision, machine vision has been evolving. If we look at machine vision as the body of a system, computer vision comprises the retina, optic nerve, brain, and central nervous system. A machine vision system uses the camera to view an image. Computer vision algorithms process and interpret the image, then command other system components to act on that information or data.
Computer vision can be used alone without being part of a more extensive machine system. But a machine vision system would not work without a computer and special software at its core. This goes beyond image processing. In computer vision (CV) terms, an image does not even have to be a photo or video. Instead, it could be an “image” of a thermal or infrared sensor, motion meter, or another source.
Beyond that, computer vision can also process 3D and moving images.
Among the unpredictable observations that early versions of such technology could not make. Through complex operations, all features within an identified image are analyzed, and rich information is provided about those images.
With the advancement of computer vision, the potential applications of machine vision have multiplied. What used to be heavy industry maintenance for simple binary operations has now appeared in automated vehicle braking systems, comparing faces of people with passport photos at airport security gates and assisting robots in surgery.
How machine vision and computer vision work together
Computer vision allows a variety of computer-controlled machines to operate with greater intelligence and safety. From large factory and farm equipment to tiny drones that can detect and automatically track a person, computer vision helps machines perform better and in more diverse ways than ever before.
Machine vision competencies in heavy industry for inspection and inspection purposes have long been proven.
The competence of machine vision in heavy industry for inspection and inspection purposes has long been proven. Cameras and computers together can capture and process images much more accurately and faster than any human. The production line can not make any mistakes in sensitive situations such as making pacemakers. Using human inspectors and controllers to conduct such rigorous inspections is too risky. When you compare human limitations with the capabilities of a computer’s eye and brain, you can easily see why.
It takes about ten years for a human being to even look at photos uploaded in the last hours of Snapchat.
Many modern manufacturing businesses can not survive without inspections of computer-based devices as part of their processes in this competition. One of the most common applications in food production is packaging and distribution.
Machine vision is used daily to reduce waste during the food separation process and ensure proper transport packaging and complete label verification. If the food is not labeled correctly, the supermarket will immediately return the product and impose heavy fines on the factory. Excessive returns can seriously damage a supplier’s reputation in an industry that cannot pose a risk to public health.
With all the information that food labels should now have as a legal requirement. So Humans can’t inspect the thousands of brands that a typical packaging factory produces every day.
Components of a typical machine vision system
Structurally, the following are the standard components of a machine vision system:
- One or more cameras
- Lighting to ensure image clarity
- lens
- Picture frame receiver
- A computer and software to analyze and process images
- Possibility of using pattern matching and other algorithms, depending on the nature of the ideas being analyzed
- Output components include a screen to display data and information plus mechanical parts such as a robotic arm
Which is more suitable, computer vision or machine learning?
Computer vision is operated to instruct the computers to understand the visual information seized from digital images or videos. In addition, Machine Learning is the use of computer vision systems in real-world interfaces. Both the technologies complete each other.
Is computer vision more difficult than machine learning?
Making a computer vision system is a challenging task; part of the problem is the complexness of visual data. And it’s even harder to make machines perform complex visual tasks.
The future of vision systems
There are many possibilities for the future of machine vision right now, and these possibilities are increasing almost every day. With the advancement of technology in visual systems, the potential for new applications is expanding. It is anticipated that optical systems will be increasingly built to achieve the desired results instead of adapting existing systems for new purposes.
New technologies are constantly emerging and advancing. This means that machine vision will not only cover more jobs. But it also means that the systems that will be created will be more flexible and customized for specific needs.
From a computer perspective, deep learning, cloud computing, faster processors, and data integration software offer a variety of opportunities. Also, Factories will benefit from machine learning and share production data with a broader business ERP.
From a machine perspective, advances in components provide highly improved raw materials such as various types of cameras. That can capture particular images, new lenses, sophisticated robotics, and more.
Source:https://rasekhoon.net/article/show/1520941/%D8%AA%D9%81%D8%A7%D9%88%D8%AA-%D9%85%DB%8C%D8%A7%D9%86-%D8%AF%DB%8C%D8%AF-%DA%A9%D8%A7%D9%85%D9%BE%DB%8C%D9%88%D8%AA%D8%B1%DB%8C-%D9%88-%D8%AF%DB%8C%D8%AF-%D9%85%D8%A7%D8%B4%DB%8C%D9%86%DB%8C