Computer Vision or how to give eyes to our machines
#TechWatchbySeb — Weekly series of Tech sectors decrypted — Issue #26 — June 2nd, 2021
Hello my friends 🖐,
and welcome back to the #TechWatchbySeb ☕️ - The weekly series of Tech sectors decrypted.
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For this week we will share insights on a new area of Artificial Intelligence which is Computer Vision while traveling toFrance🇫🇷, the Uk 🇬🇧, Switzerland 🇨🇭, and Germany 🇩🇪.
As presented last week, I started a series around the different clusters of Artificial Intelligence. Last time, we analysed of Natural Language Processing to build machine chat with humans.
This week we will have a deeper look at Computer Vision
Indeed, thanks to advancements in artificial intelligence and computational power, computer vision technology has taken a huge leap toward integration in our daily lives. The computer vision market is expected to reach $48.6 billion by 2022.
Definition of Computer Vision and examples of Applications
Computer vision is the field of computer science that focuses on creating digital systems that can process, analyze, and make sense of visual data (images or videos) in the same way that humans do.
The concept of computer vision is based on teaching computers to process an image at a pixel level and understand it. Technically, machines attempt to retrieve visual information, handle it, and interpret results through special software algorithms.
The main tasks that can be achieved by Computer Vision technologies include:
Object classification: The system sees an image and can classify it.
Object identification: The system sees visual content and identifies a particular object on a photo/video.
Object tracking: The system processes video finds the object that matches search criteria and tracks its movement.
Content-based image retrieval: The system browses, searches and retrieves images from large data stores, based on the content of the images rather than metadata tags associated with them.
Also, I found a very interesting explanation presenting how Computer Vision works in an article written by Golan Levin, Image Processing and Computer Vision which explains that machines interpret images as a series of pixels, each with its own set of color values as described. The image below is a great illustration of it:
Now let’s have a look at 3 examples of applications of Computer Vision:
Augmented reality: Thanks to computer vision, the application will detect physical objects and use this information to place virtual objects within an environment.
Self-driving cars: Computer vision enables cars to make sense of their surroundings thanks to cameras that capture videos from different angles and send videos as an input signal to the computer vision software. The system detects objects like road marking, objects near the car such as pedestrians or other cars, traffic lights, etc.
Facial recognition: Computer vision apps, match photos of people’s faces to their identities. It is used by social media to tag people on photos.
As analysed in an article written by the research team at Early Metrics, there are a lot of use cases to deploy NLP solutions. Indeed, it can be used to monitor drones or use facial recognition for surveillance or risk prevention.
Tech ecosystem of Computer Vision
As presented earlier the potential of this market is pretty impressive and expected to reach around $50Bn by 2022. Thanks to some examples presented above, we can easily see the importance this technology is taking in our day-to-day life.
In the graph below, we can clearly see the uptrend of computer vision with a strong acceleration of the investments. The total cumulative funding went from around $1Bn in 2017 to over $9Bn in 2021.
If we compare this with Natural Language Processing (last week’s article), the amount raised by Computer Vision companies was greater by $3Bn.
Also, interesting insight comes from the location of the Tech companies that have been referenced to offer Computer Vision solutions. We can see a strong dominance of Chinese and American companies in the market. In a nutshell, the Chinese companies represent around $5Bn of the total fundings and the US $3Bn.
What is really surprising is not to see Europe at all…
What are the most funded European companies?
Even if Europe is not mapped as part of the main places for Computer Vision, we can see that there are some very successful companies.
Meero 🇫🇷: AI company providing enhanced photography services. The company has raised around $293M.
Blippar 🇬🇧: AI company specializing in augmented reality, artificial intelligence, and computer vision. The company has raised around $136M and has been acquired in 2019 by a Family Office.
Scandit 🇨🇭: AI platform for mobile computer vision and augmented reality (AR) solutions for enterprises. The company has raised around $123M.
What are the most active funds in Computer Vision in Europe?
We can see that there is already a good level of maturity in the market. As presented in the graph below there have been many funding rounds at various stages, including Series B, C and D.
Venture Kick 🇨🇭is a private, philanthropic initiative that provides pre-seed funding to entrepreneurs from Swiss universities. The fund has backed 11 Computer vision deals.
EASME 🇪🇺is the European Union executive agency for SMEs in charge of Enterprise Europe Network, COSME, and other programs. The fund has backed 11 Computer vision deals.
Investitionsbank des Landes Brandenburg 🇩🇪 is a business promotion bank in the federal state of Brandenburg. The fund has backed 7 Computer vision deals.
Computer vision is likely to become a ubiquitous technology, enabling social media, autonomous cars, and even tomorrow’s medical diagnostics. This market is already well advanced especially in China and the US, which is probably linked to investments done by tech giants such as the GAFA (Google, Amazon, Facebook, Apple) or BATX (Baidu, Alibaba, Tencent, Xiaomi) for which Computer Vision plays a big role.
So, that’s it for this week, wishing you a great week ahead🖐
Stay safe ❤️