A guide to computer vision platforms

The ability to see is truly magical, for we can perceive so much of the beauty in this world and claim to understand it.

Mankind was to not advance so much without the ability to see; it underlies every activity of ours. And so, in this fast-paced digital world, it only makes sense to move towards building smart visual systems to increase our output tenfold.

This is what computer vision platforms aim to do.

What do you mean by computer vision?

Computer vision refers to the sub-discipline of computer science that attempts to re-create vision through technological means, as understood in biological beings.

It means that platforms and systems that employ computer vision imitate the working of biological eyes to “see” using a camera. Computers that run on specially designed algorithms than “process” these visual inputs.

How does it work?

Think of a simple camera you own or the camera app on your phone that clicks images and records videos. Now, what happens when you look at that image or video shot by the camera?

You can make sense of the objects in it and understand what they are using the pre-existing knowledge of the world that exists in our brains.

Photo by Marcus Spiske on Unsplash

Computer vision does something of that sort - it makes sense of the shapes in the images or video frames, just like we would. Using image recognition algorithms, computer vision trains systems to recognize the objects in the images and process them.

Computer vision also employs deep learning algorithms sometimes that have multiple layers in their neural networks to process large amounts of data and generate more precise outputs.

The algorithms draw shapes of the objects and categories them into a class.

For example, if there are small dogs in the image, an image recognition algorithm (which is pre-trained to detect these dogs) would draw a shape outlining them to classify them as “small dogs”.

Programmers need to manually train the algorithms to identify the object. This is one critical part of computer vision and a good example of how the algorithm is strengthened and made more accurate in its detection.

What are computer vision platforms?

Computer vision (CV) platforms take care of the end-to-end process of labelling and annotating data that you need to train computer vision models, to develop custom models so you can deploy these models onto Cloud and other devices.

They also have pre-trained models that can do basic tasks, like detecting inappropriate content. You might notice that this is what happens on social media platforms like Instagram and Facebook, that flag content as inappropriate or harmful.

Computer vision algorithms are at play there, as they do the job of extracting information from visual input and labelling the data obtained against the data used to train them.

Photo by Austin Distel on Unsplash
Since computer vision quite literally is changing how we, and machines, see things, democratizing the technology is the one way forward to ensure everyone has access to it to automate their processes and increase efficiency.

Many computer vision based companies are facilitating businesses and enterprises to shift focus to enhancing the quality of output and increasing productivity substantially without worrying about training the models.

Entities like Tooliqa provide businesses with the opportunity to make the most of computer vision.

They have developed tools that drastically reduce the time spent in delivering usable products and services to businesses, professionals and consumers even with a basic level of technical proficiency.

Tooliqa’s approach to this is to use deep learning computer vision algorithms to reconstruct spaces in 3D in quick time and also to use them to create near-perfect 360-degree environment models.

The importance of computer vision in today’s world

A study by Verified Market Research shows that the market for computer vision is expected to grow exponentially to reach USD 27.02 billion in the next 7 years.

Computer vision is one of the most advanced aspects of AI that has enormous commercial value due to its direct impact on various sectors. Disruptive organizations in every sector, from marketing to manufacturing to agriculture and social media, require computer vision.

Why is this so?

Because as humans, we love seeing things, capturing them digitally and making sense of them. And we lean on technology to help make life easier for us.

Photo by Headway on Unsplash

We are highly dependent on technology. Therefore, we need to make the most of this technology to improve our understanding of the world. They also allow us to refine systems to make them more efficient.

These algorithms enable computers to draw information from digital images, videos and other visual inputs to help us predict certain outcomes. This, consequently, helps us make better decisions and/or improve productivity.

Technological advancements like facial recognition systems and self-driving cars couldn’t possibly exist and improve every day if it weren’t for computer vision.

It’s because of the power of these highly-developed systems that can "think" and make these decisions on their own based on previously fed data, that everything from the slightest detection to the largest machines depends on.

Applications

Computer vision platforms create vision-based solutions for everything, from safety to construction, from the automotive industry to design, and customer engagement and insights. The data that computers learn from these algorithms automate the creation and management of several products and services.

Self-driving cars work because of computer vision. The visual input allows them to estimate distance and move accordingly. While integrating video footage and imaging, the algorithms allow these cars to make crucial decisions as we would.

This process occurs in real-time when cameras capture video footage and feed it into the software that makes these decisions.

Photo by Gabe Pierce on Unsplash

While self-driving cars might seem like a very alien concept to many, facial recognition is not since we witness it every day. When you check into work every morning and stand in front of the biometric system, you are interacting with a computer vision system. Even law enforcement uses computer vision to detect criminals and track their movement.

All of this is made possible because computer vision algorithms are constantly working their magic behind the gadgets we interact with.

This journey from early-stage visual biometric systems to self-driving cars shows the promise the field of Computer Vision holds for the future which has constantly been seen as a highly automated one. To push this automation to the next levels, we need our machines to see, analyze and act more smartly.

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