Computer vision is a part of Artificial Intelligence that empowers computers to get significant data from computerized pictures, videos and make proposals dependent on that data.
Simply you can understand if AI allows computers to think then computer vision encourages them to see, notice and understand. This is done with the help of deep learning and machine learning.
Computer vision is used in industries going from energy and utilities to manufacturing and automotive and the market is proceeding to develop.
Computer vision helps computers to learn the content of digital images. It helps computers to see, determine and process images as our human vision does, and after that, it gives the proper output.
The major objective of this subset of AI is to teach machines to collect information from pixels.
Today, computer vision is being used effectively in surveillance, autonomous vehicles, facial recognition, movement recognition, and many more.
Several companies provide artificial intelligence growth services for the advancement of computer vision.
Thus, computer vision is the modern technology that enables the digital world to interact with the real world.
"Computers can see, hear, and learn. Welcome to the future."
Applications of Computer Vision
In the various parts of our healthcare systems, computer vision has found a great application. In the healthcare sector, most of the medical data is image-based.
While computers will not totally supplant medical services faculty, there is a good chance to supplement routine diagnostics that require a ton of time and skill of human doctors yet don't contribute essentially to the final diagnosis.
This way computers fill in as an aiding tool for the medical care personnel. In the coming future, computer vision can possibly acquire some more genuine worth in the medical sector.
The best example of a used case of computer vision in healthcare is during the COVID-19 pandemic situation where computer vision is being used to detect pneumonia in the X-Ray reports of patients.
Cancer Detection- Image detection permits researchers to select slight contrasts between cancerous and non-cancerous, and diagnose information from MRI scans and inputted photographs as malignant or benign.
Movement Analysis- Pose Estimation computer vision applications that examine patient movement help doctors in diagnosing a patient effortlessly with increased exactness.
Disease Progression Score- Computer vision can be used to specify sufferers that are critically sick to direct medical attention (critical patient screening).
Tumor Detection- Tumor detection software using deep learning is vital to the medical sector since it can detect tumors at a high exactness to help doctors make their diagnosis.
The agriculture sector is enhancing very fast as they started using advanced technology. Due to the rise in demand, it is quite challenging to do it manually and that’s why the agriculture sector is using computer vision.
Computer vision helps in doing farming activities like weeding and harvesting.
AI-driven computer vision can be used to improve agriculture by expanding yields as it advises farmers about productive development strategies, crop wellbeing and quality, bug invasion, and soil conditions.
This technique will help in improving the overall quality of the crop and also saves time.
Image classification techniques are now being used to automate quality control of crops by evaluating and arranging them based on their physical parameters and properties.
Similarly, multispectral and hyperspectral aerial imagery given by drones catches definite data about soil and crop conditions to help screen stress and disease in farming.
Machine learning algorithms help in detecting damaged products.
With the help of computer vision applications, farmers can now easily pinpoint weeds and pests
Therefore, these technologies help farmers adopt more efficient growth methods, and this results in more profit.
( Related blog: 5 Applications of IoT in Agriculture )
In the manufacturing industry, computer vision is used for the quality control of the final goods. This can be either furniture, shoes, clothing, automobiles, FMCG products, and so on.
This mainly helps in making the highest quality products as computer vision can easily pinpoint the defects which the human eye cannot.
Computer vision is used by barcode readers to track the finished goods.
Even employee movement and tracking can be done with computer vision.
Computer Vision algorithms are trained with data examples to monitor humans and count them as they are traced. In a situation like COVID-19, when fewer people are allowed in the store this technique is very useful.
Also, with the help of a computer vision algorithm theft can be detected by autonomously analyzing the scene.
Banks and other monetary establishments have effectively begun to execute computer vision.
The banking industry uses computer vision broadly these days with the rise in fraud and counterfeit currency cases. The banking system uses AI-based answers to recognize counterfeit currency being inducted into the system at the client touchpoints.
With these, banks, alongside the police, can follow the source of the counterfeits significantly earlier.
Using computer vision, washed cheques, and fake cheques can be spotted effectively which isn't exactly obvious to the unaided eye.
Banking security systems use AI-based software to identify suspicious behavior and also keep an eye on their workers.
A few foundations permit their customers to open accounts using facial recognition for the check.
Image processing can likewise be used for electronic deposits as the customer presents a picture of the front, and the back of a check, and then the transaction is examined and finished.
( Also read: What is the Federal Reserve System? )
Computer vision is helping the automotive industry to fly high.
With the help of computer vision techniques, the automotive industry is developing self-driving cars and the best example of self-driving cars is Tesla cars. The company says that its cars use eight cameras around the vehicle for a 360-degree view.
Thus, these cameras use computer vision to render the road and traffic around the car.
It is believed that autonomous vehicles will reduce accidents as the chances of human error are minimized.
Waymo is another real-time application that makes use of computer vision. They are working hard to tighten transportation for people, building on self-driving cars and sensor technology formulated in Google labs.
Computer vision will help cars in reading temporary road signs and give way to oncoming emergency vehicles.
So, this application of computer vision sees it working alongside deep neural networks, enabling the car to drive on busy roads safely.
In the insurance industry, computer vision can simplify their operations by reducing the time needed and minimize instances of fraud.
Insurance companies use computer vision to assess pictures from the incident and this helps speed up the procedure of claims processing.
Computer vision can flawlessly distinguish the source of the occurrence and qualify it as real or phony. It can likewise recognize doctored pictures so false cases are separated consequently.
Computer Vision in Insurance
Insurance agencies are using computer vision since it is advantageous to them. They are saving a considerable amount by not paying out counterfeit cases.
Additionally, the customers of authentic cases have likewise profited as they get a quick resolution and payout.
Simply, we can say that in insurance computer vision can analyze assets, determine premiums, reduce fraud, reduce settlement time, reduce paperwork, and analyze paperwork data.
Computer vision plays a very important part in the sports industry.
It helps companies in optimizing the data by tracking the engagement and reaction of the audience present in the stadium, and teams playing the game.
Entirely automated sports production has been built through deep learning, which includes zoom-ins and pan-outs similar to professional, human-led production.
Rather than using cameramen, computer vision is being used to discern positions of players and the ball to concentrate mainly on those factors relying on what is in the belief.
Ball tracking is one of the applications of machine learning and deep learning that makes the ball seem visible on the screen. This makes news reporting easier for sports newscasters.
Computer vision also helps in tracking the players which helps in analyzing the performance of the player and even reviewing their technique.
An illustration of the use of computer vision in tennis can be seen in one of the significant competitions in the game.
In the year 2017, Wimbledon collaborated with IBM to incorporate automated video highlights catching important minutes in the match by essentially assembling information from players and fans, for example, crowd noise, player movement, and match information.
Also, on the business side, a pocket-sized device was planned by Grégoire Gentil that was done in a tennis match by using computer vision to distinguish the speed and situation of a shot and decide if the ball was outside the boundaries.
( Related blog: AI and Data Analytics in Cricket )
Modern technologies are helping in the security of public places like parking lots, bus stations, railways, subways, roads, highways, etc.
The computer vision has a diverse application for security purposes like:
Human abnormal behavior detection
Illegal parking detection
Speeding vehicle detection
This technology is aiding a lot in preventing several types of accidents and strengthening the security system.
Computer vision in surveillance is a very necessary application where surveillance cameras are omnipresent in every public place.
For instance, now retailers can easily keep an eye on the suspicious behavior of the customers.
So, we can conclude the blog by saying that the usage of computer vision applications increased in several industries and is very beneficial and this collaboration of humans and machines is taking this world to the next level.
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