The Rise of Computer Vision

With the advent of AI in the 1960s we could not have fathomed that six decades later, the world would become a global economy and ecosystem that stands on the backbone of artificial intelligence and sophisticated technology infrastructures.

Today, we have AI blended into our lives to the point where we don’t even realise the constant presence a highly complex and intelligent technology working to make things easier for us.

Many factors have contributed to the massive success of Artificial Intelligence. The dynamic pillars or segments of AI such as machine learning, computer vision, natural language processing, and speech recognition have all stood behind the exponential growth of AI.

Computer Vision started with simply giving computers the ability to recognise and identify shapes. Today, it has morphed into a giant neural network of high-speed image processing unlike any other. It is used in diverse cases such as fraud detection, crime and intelligence, manufacturing, health and safety, and so much more.

What has given rise to Computer Vision?

The integration of sophisticated processes. In sequence, the process looks a bit like this:

  1. Acquisition of images and visual data
  2. Image processing and edge detection
  3. Matching and segmenting visual data
  4. Analysing and interpreting it
Can you unlock your phone screen with facial recognition? Thank AI and computer vision. Computer Vision today is not a far-fetched luxury that only a few experience. It is a phenomenon that dominates our lives regularly.

Being able to classify images based on nature, people, portraits, and more on your smartphone is due to Computer Vision. Today, even our government IDs operate on retina recognition – which is a part of computer vision.

How did computers start “seeing” the world so quickly?

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Many factors contributed to the rise of computer vision in modern society.

1. The availability of powerful, high-speed computers that can process large amounts of data, for starters, is one of the leading factors for computer vision growth.

2. Secondly, data availability has risen exponentially in recent times. So much so, that the data we have available today, did not even exist two decades ago. Big Data and the availability of processors that can comprehend and compute this big data has also added to the rise of computer vision.

3. Also, funds. The availability of financial resources to research and grow the field of computer vision has been huge recently. The last 10 years have seen the rise of Augmented Reality, a phenomenon that sounds straight out of a science fiction movie- but is a reality.

Tech giants such as Facebook, Google, and Apple have been investing heavily in understanding and perfecting computer vision, finding multiple uses of this technology, and integrating it into our daily lives through their devices, apps, and more.

4. Finally, innovation. Our rapidly innovating scientific environment has led to the rise of computers and technology that is powerful, efficient, and affordable for the masses.

Today, smartphones, which are powerful devices combining artificial intelligence and many other technologies exist in the palm of every hand. While these devices come in a range of technological sophistication depending on their price range, every smartphone, more or less, is a wonderful machine capable of performing smart tasks.

The disruption in the AI space was forged by tech giants. But today, startups such as Tooliqa, SenseTime, and NAUTO have been working on making these technologies and wonders of Computer Vision and AI more accessible to industries and better integrated into people’s lives. For instance, Tooliqa has been focusing on the core pillars of computer vision by creating structured, advanced computer vision accessibility to creative industries- giving them a real sense of visual environmental depth.

So, why computer vision? And what role does it play in our future?

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Humans are a visual species. We love seeing things and capturing them. We understand best when the stimulus input is vision. And we’re moving towards an increasingly visual economy, and the world at large, every day.

Platforms like Instagram and Facebook see over a billion million photos uploaded every day.

Our shopping, music, art preferences- are all visual in some way or the other. Every platform, especially social media, that exists is increasingly moving towards a visual- photo and video- approach.

Today, Instagram and Facebook can tag and blur sensitive images, they can put disclaimers under images related to coronavirus, and even flag factually incorrect content. All this is connected to computer vision and a computer’s ability to “see” photos.

What are the applications?

Computer vision has also radically transformed how businesses operate.

In the recent past, video analytics and computer vision were just buzzwords that floated in tech. Today, businesses integrate their CCTV cameras with computer vision to create effective automated practices that hardly witness any deviation.

Retail, for example, uses computer vision to minimize shoplifting. Retail stores also use computer vision for self-checkout, understanding store layouts, demographics, customer profiles, and more. All this information is collected and helps in better decision making, marketing practices, and reduced costs.

Not only this, but retail stores also use computer vision for stock-taking. Now, they know something is about to run out just before it runs out.

How does computer vision work, really?

Computer vision is built on the AI phenomenon of Deep Learning. Deep learning is the ability of a computer to “learn” things deeply to perform human-like tasks. Computer vision uses multiple images of a kind to learn what an object or thing looks like to understand it and then interpret it in different settings, environments, and contexts.

For example, if a computer, using deep learning, learns what a “book” is. It can identify books of different colors, sizes, at different angles and even with different backgrounds. Deep neural networks that exist can be trained through a set of image and video data. This training data enables the computer to learn and process the information.

For example, in an office building- arms and ammunition, aerosols, and liquid containers are prohibited.

A deep learning system will “train” a computer on what these things look like. Every time the computer “sees” them, it will flag them and create an alert based on the patterns it has been taught.

Today, computer vision has translated from a whimsical concept in theory to a reality we live and breathe. The rise of computer vision has not only impacted industries like retail but niche industries and micro aspects of life. Some examples of this are- finding minute defects in manufacturing, monitoring endangered species and wildlife from a distance, analyzing sports and sports players, detecting fraud, identifying fake bills, airport security, and health and medicine.

The rise of computer vision has already begun but what we witness right now is just the tip of the iceberg. What is in store is a world where visual search becomes common practice, where computers see just as good as us, where the human attention span to visual data is no longer a threat to security, and so much more.

The future is truly visual, and our computers have just started seeing.

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