Computer Vision is the field of scientific discovery that looks to develop techniques to help computers ‘see’ and understand the content of digital images, photographs and videos. While the idea of Computer Vision may sound somewhat simple, in reality, it has been one of the most complex problems AI engineers have had to solve. It is due to the fact that we still have a relatively limited understanding of biological vision. We understand the functioning of the eye but it is the complexity of visual perception that makes seeing and understanding sometimes quite subjective.
As computing power increased significantly in the 1990s and the internet became mainstream, there were suddenly large sets of images available online for analysis. This was when Computer Vision and its most famous application, facial recognition programs really began to come into their own. As of today, Computer Vision as a product of AI is really undergoing somewhat of a renaissance and it’s down to the convergence of 4 specific factors:
- Built-in cameras in mobile technology have resulted in a digital world saturated with photographs and videos
- Computing power has become more accessible for substantially less money
- Hardware specifically designed for Computer Vision and analysis is more widely available
- Newer algorithms like convolutional neural networks are able to actually take advantage of the hardware and software capabilities we now have more than ever before
To date, it has become relatively easy to index and search for text, but in order to index and search images, algorithms actually need to understand what an image contains. For a long time now, the content of images and videos has been ‘unclear’ and best described using meta descriptions, which are typed in by the individual who uploaded them. To really get value out of image data you need computers to not only see an image but to understand the content.
Like human capabilities for sight and comprehension, we would like to have computers do the following:
- Describe the content of a photograph it has ‘seen’
- Provide a basic summary of a video it has ‘seen’
- Recognise and identify a face it has ‘seen’
This would then allow us to categorise all of these elements for search more accurately (among other things).
How does Computer Vision work?
Computer Vision is a multidisciplinary subfield of AI and ML, and more specifically Deep Learning. It works in a simple three-step process.
Firstly, image acquisition, which can happen in real-time through videos, photos and any other kind of 3D technology. Images are then categorised for analysis. The second step is processing the image. This generally involves automated Deep Learning models. More often than not these models have been trained by being fed thousands of labelled and pre-identified images. The third step is crucial and is what makes Computer Vision distinct from image processing. It consists of understanding the image, which means the computer interprets an image that is properly identified and classified.
Computer Vision in different industry
The global computer vision market has grown substantially, reaching $19.82 billion in 2024 and is projected to reach $58.29 billion by 2030, growing at a CAGR of 19.8%. This exponential growth reflects the technology’s transformative impact across diverse sectors. Many businesses have begun to add Computer Vision to their operations and customer experiences, creating innovative solutions that enhance efficiency and engagement.
Retail and Commerce Innovation
Amazon has streamlined the shopping process through the applications of Computer Vision and Machine Learning. Through Amazon Go stores, the company uses Computer Vision to keep track of stock, maintenance and every customer in-store to ensure effectiveness and security. Cameras and sensors in brick-and-mortar stores connect each customer to their Amazon account whilst also keeping an accurate stock count of each item in the customer’s basket. As soon as you’re done shopping, the bill is automatically charged to your Amazon account without having to deal with a cashier to confirm your purchase.
Pinterest Lens takes connecting you with your interests to the next level. In order to use the feature, all you need is to snap a picture of an object and Pinterest Lens finds you similar items from their directory of images and articles. Pinterest is little more than an enormous catalogue of images and articles, and through a comprehensive Deep Learning CV backlog, the algorithms working in the background help feed the data you are shown.
Healthcare Applications
Computer vision technology is being deployed extensively in healthcare, with applications ranging from medical imaging analysis to patient monitoring. The technology aids healthcare professionals in achieving more accurate diagnoses and enabling earlier disease detection through advanced pattern recognition capabilities that can identify subtle indicators invisible to the human eye.
Manufacturing and Quality Control
The manufacturing industry leads in Computer Vision adoption at 35%, utilising the technology for quality checks, warehouse supply tracking and counting deliveries in the shipping process. CV can help with everything from identifying defects on production lines to optimising inventory management, making it particularly valuable for businesses seeking operational excellence.
Security and Safety Solutions
The use of computer vision in surveillance and security systems is growing rapidly, with systems using vision technologies for facial recognition, behaviour analysis and threat detection. This has applications ranging from public safety to secure access control in sensitive facilities.
Recent Innovations
In September 2024, Air India introduced ‘AEYE Vision’ on its mobile app, which uses AI-based computer vision technology to enhance the travel experience for passengers. This innovative feature allows users to scan their boarding passes, helping to streamline the boarding process and reduce delays.
In early 2024, Google introduced an updated version of Cloud Vision AI, featuring improved image recognition capabilities, enhanced optical character recognition (OCR) and better integration with Google Cloud services for enterprise use.
January 2025 saw Blaize and alwaysAI collaborate to transform real-time insights with computer vision and AI edge computing applications. The partnership incorporates alwaysAI’s CV technology and remote deployment abilities with Blaize’s chipsets and edge devices, making unified edge deployments more accessible for enterprises.
In closing
Whilst not new, Computer Vision technology is advancing at meteoric rates, which makes it one of the most popular new frontiers of Artificial Intelligence. The Asia Pacific region is expected to show the fastest growth, accounting for 41.7% of the market share in 2024, driven by significantly increasing investments in Chinese companies and growing adoption across industries.
Over time, the expectation is that CV will make it much easier for consumers to engage with businesses and products through enhanced shopping experiences, improved healthcare diagnostics and more secure environments. The way we see it, Computer Vision is definitely a technology worth keeping an eye on!
