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October 22, 2021 by Blog

Computer vision: Booster Dose to Automation

What are computer vision applications in daily life? Where is computer vision used in real life?

Our read comprises a comprehensive listing of novel, relevant computer vision applications in pre-eminent industries in 2022. Other than this, you are going to learn about computer vision: a modern approach to information tech.

Computer Vision a Modern Approach

For us humans it’s easy to process visuals with the help of our eyes that capture light, the brain receptors receive this light, and our visual cortex processes it. Over the years we have been able to transfer these visuals to machines in the form of cameras that closely mimic how our eyes work. From polaroid cameras to DSLR and now mirrorless cameras, the digital work has surely been enhanced well. Computer vision applications made all of this possible.

But capturing a visual is a different thing and understanding it is another. For example, a picture of a flower is easy to recognize for us but for computers, it isn’t. This same image for computers is processed in numbers that define the high and low contrast of the picture. Fortunately, the advanced algorithms of machine learning coupled with computer vision help to identify what the picture is representing.

computer vision applications

Computer Vision: How does it work?

~ Here Come the Technicalities ~

Computer vision applications are paving the way in performing the most complex tasks with a 100% accuracy rate. It still doesn’t mean that the model cannot make mistakes. Computer vision simply means how machines interpret images but it solely depends on how those algorithms are trained to perform this task.

In traditional programming, computer vision is really easy to explain, let’s take this code for example:

IF the center AND corner pixels are full THEN it’s an ‘X’

IF the center AND corner pixels are empty THEN it’s an ‘O’

Now, these basic code lines just let the computer differentiate between a simple X and a simple O in a pixel-based picture. But let’s say if the pictures are somewhat changed and they don’t exactly match up with the code that has been configured. Then what? The computer will fail.

But computer vision applications rely on machine learning to get the job done. When an array of images are presented to the computer, upon a wrong guess, the correct answer is revealed which makes the AI stronger. You can very well call this ‘playing with flashcards.’ The whole idea behind this is the guessing game. A wide variety of training data is presented to the machine for it to tune itself according to the correct data.

So how do computer vision applications learn to recognize complex images? The neural network makes it possible for computer vision to recognize complex images. For instance, some shots or scenes from the real world are broken down into simple patterns, a car wheel is made from concentric circles or an eye is made with arcs, etc

These different pixels are caught up by the neural network and work in a layer form:

Layer 1: Gathers all pixel values in a numeric input form

Layer 2: This is where all the edges are joined to formulate a generic shape

Layer 3: All of the data from previous layers is combined to make a final image

The obvious part of computer vision applications like ML takes millions of images and clips for the system to recognize and give a correct result.

Computer Vision: How does it work?

The Road to Blissful Automation

Computer vision applications are where they successfully open the vaults to automation. To start:

Retail Automation

Every retailer out there has a number one priority that is to make the customer experience flawless. One of the examples of computer vision applications, vision retailers automate their process of checkout and payment methods. One of the most intriguing aspects of computer vision for retail companies is customized customer interaction.

Also not to forget the ongoing process in optimizing inventory.

Banking sector Automation

A decade ago nobody would have thought mobile banking would be possible but look where the banks stand now. As the consumer market increases, the demand for mobile banking is also affected. Computer vision applications play a vital role in the detection of fraud and make the banking experience safer for users nowadays. Bank deposits are now automated, we have said goodbye to tedious waitings for money deposits.

For the future of computer vision applications, its uses will increase the accuracy in banking for key business functions.

Surveillance Automation

Computer vision applications are doing wonders in the surveillance sector. Airport security hugely relies on computer vision a modern approach to detect intruders. With the level of automation that computer vision is providing, airport screening is getting a lot easier and faster. Forged passports and false identity cards can be tracked in seconds. This is not specific to airports only.

The overall security industry is truly benefiting from computer vision applications and boosting the automation levels. In areas where the environment is more dynamic and prone to change, detection of objects from a distance is faster than ever.

Computer vision applications and Surveillance Automation

Testing Automation

AI-powered computer vision has shown software testing to be automated and the results are astonishing. Programming the system to perform analysis on both the data and conformity of particular software has allowed developers to automate the tiring processes of quality control.

Test automation is also branched to inspection automation where all the manufacturing industries take full advantage of computer vision applications to perform faster and safer inspection checks which improve efficiency and product quality.

Testing Automation through

Conclusion

Is there any more confusion left? ML and computer vision a modern approach, these are correlated. Without computer vision applications, neither AI and ML-powered systems can work. One of the biggest purposes of computer vision is to bring an automation revolution in every sector as we see it. But in this article, we touched on the latest and trending sectors where computer vision is excelling greatly.

Stay tuned for part 2 of this article where we’ll go in-depth into computer vision applications and how they will impact the years to come.

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