Welcome to the world of computer vision, where humans train computers to interpret and understand the visual world. Thanks to the recent technological advancements in artificial intelligence and innovations in deep learning, computer vision has become a powerful tool driving transformation in industries.
The computer vision market has been expanding across multiple industries in the past years, leading to an expected growth of $17.4 billion in revenue by 2023 and $41.11 billion by 2030 (according to Allied Market Research).
It may not come as a surprise that businesses like Google and IBM are among the leading advocates of AI-based computer vision solutions. This serves as one of the indicators that computer vision has a promising future in advancing numerous industries.
By the end of this article, you’ll have a better understanding of:
Computer vision is a field of artificial intelligence that allows computers to obtain structured and meaningful information from digital images, videos, and other visuals. Based on this information, actions or recommendations can be given.
Over the past few years, ideas have been flowing when it comes to computer vision applications that use (vision) AI. However, the majority of these ideas remain in their proof of concept stage (PoC), not yet reaching production with a positive business case.
So far, we’ve only scratched the surface of vision AI’s potential. Can you imagine how much further it can go if more ideas were executed?!
Check out this hype cycle below for Artificial Intelligence, last updated in 2022 byThe hype cycle is a graphic presentation that reflects the different stages of a specific technology, such as maturity, adoption, and social application.
Credits: Gartner, 2022
This graph shows that computer vision is currently past the highest peak on the so-called ‘slope of enlightenment’, preparing to reach a steady plateau (‘plateau of productivity’), where mainstream adoptions will start to take off. Taking this step first requires the knowledge and expertise missing to build accurate and scalable solutions.
We’ve already established that most ideas have not yet gone past their PoC stage in computer vision.
Computer vision systems are being developed to reduce the workload on humans and their flaws in operations. Some industries are already benefiting from existing CV applications, here are 4 examples:
When it comes to computer vision, the agricultural sector has seen many contributions and solutions, from reducing production costs with intelligent automation to boosting productivity. Here are some of the uses of computer vision in agriculture:
With benefits like increased efficiency, improved safety, and reduced costs, computer vision in manufacturing is usually applied for inspection, quality control, remote monitoring, and system automation. The most commonare:
In healthcare, computer vision can help doctors and medical professionals identify diseases quickly and accurately. Some interesting uses:
We’ve seen the challenges of this industry in 2022. The energy crisis put a high demand on ensuring safe and reliable operations. Here are some common uses:
Having a traditional, manual inspection of equipment or meter readings has become too difficult and costly to achieve in large-scale power enterprises.
Here’s where computer vision comes in: enabling machines to see using cameras to autonomously complete tasks. To avoid manual work, save time, and provide accurate consumption data, Blicker allows utility companies to do meter readings with the simple act of taking a photo.
Let’s start with what we’ve been all hearing about lately: the Metaverse. Currently, the Metaverse is known as a virtual world that allows people to interact realistically.
Although it rapidly gained immense popularity over the past few years, the recent applications of computer vision in the Metaverse are now more available and accessible than before. The most notable part about this is Facebook’s rebrand to Meta, and how its campaigns have caused curiosity and excitement.
Such Metaverse innovations are only possible thanks to machines that can accurately understand the real world and recreate it. Computer vision plays an important role in recreating the user’s environment in 3D, as it provides a better understanding of the surrounding environment.
Users in the Metaverse are represented by avatars and are able to perceive their environment, which is possible through processing, analysing, and understanding visuals as digital images or videos to extract meaningful decisions and take action.
There is still a long way to go until the Metaverse becomes fully immersive, but large companies such as Facebook and Microsoft are already using virtual reality (VR) in their applications.
Credits: Getty Images/iStockphoto
As an emerging computing paradigm, edge computing is all about processing data closer to its source rather than depending on the cloud network. This technology is mainly used to enable efficiency and speed during processing.
Computer vision applications process data rapidly and in many cases, in real time. This data requires strong privacy protection, which is why edge computing may be a suitable solution for some. Internet bandwidth limitations could be another strong argument for on-edge processing.
Where there are advantages, there are also risks to on-edge. There is complexity in its infrastructure because it consists of various components that use new technology and communicate through multiple interfaces. When distributed over many devices, updating the software quickly becomes challenging, making it hard to apply a self-learning mechanism.
AI consists of systems that are both data and model centric. Data-centric AI revolves around changing or improving datasets to improve the performance of the model. In model-centric AI, an algorithm is updated while holding the fixed amount and type of data. However, fixed models have been more commonly used in the past couple of years. This involves spending a lot of time and energy to find the best model architectures.
Although a model-centric approach has been the most popular over the past three decades, it has recently been criticised for being limited to consumer platforms that can freely rely on generalised solutions. Additionally, it requires spending a lot of time and energy to find the best model architecture. 2023 is expected to see a bigger interest regarding data-centric approaches.
The popularity of Artificial Intelligence (AI) image generators spiked in 2022, but shows no signs of stopping!
Generative AI is a type of artificial intelligence that creates new, original content rather than just analysing or acting on existing data.
One of the most popular technologies in generative AI is DALL-E, a system that generates realistic photos and art from text descriptions. Now, developers can integrate it directly and it is available to the public.
This was in July 2022. A month later, Stable Diffusion was released, leading to a pool full of AI image generators competing to be the best software in the market. Let’s dive into the generative AI technology that recently caused a buzz all over the world: Lensa AI!
If you’re active on social media, you’ve probably seen avatar portraits of your friends being posted on their stories or the feed.is an app that uses artificial intelligence to transform photos into customised portraits.
December 2022 was a month full of beautifully crafted portraits posted all over social media. By collecting various images from users and the Stable Diffusion neural network, Lensa AI creates high-quality digital portraits. The app is even trained to mimic specific artists and styles!
Stable diffusion is a deep learning, text-to-image model, mainly used to create detailed images from text descriptions. Lensa applies this model, benefiting from its ‘in-painting’ and ‘outpainting’ features. The model can be asked to inpaint an image with new variations in photo editing (e.g; filling in parts of an image). This means that the model has a clear visual understanding of a person’s facial features, age, ethnic background, etc.
Generating images with diffusion models is based on the fact that we have powerful computer vision models that, given a large dataset, can learn complex operations.
Augmented reality (AR) provides an interactive experience by combining the real world with computer-generated content. It can be accessed via a smartphone, and enhances both the virtual and real world.
Merged reality (MR) is similar to AR in the sense that it does not separate you from your surroundings. The only difference is that it can read your surroundings and add digital content to it. MR needs a headset to complete the experience.
When computer vision and AR are combined, powerful vision capabilities are born. For example, there is the simultaneous localisation and mapping (SLAM) that provides a geometric position for AR systems. With this mix, 3D maps of an environment can be made by tracking the location and position of the camera. 2023 is expected to see much more use of merged reality enhanced by AR.
With the potential to revolutionise a wide range of industries, computer vision is here to stay. The recent advancements in machine learning and deep learning techniques have opened the door for highly sophisticated computer vision systems that can analyse and interpret visual data with great accuracy.
This article mentions popular examples of how these developments grew to be conceptualised and sometimes used once they’ve gone past their PoC stage. Nevertheless, 2023 is expected to be a year full of flourishing computer vision concepts, such as Facebook’s Metaverse that hooked consumers with awareness and excitement. What an exciting time for computer vision!