Investing in Computer Vision Companies in 2020
Over the course of the past five years, Nanalyze has unearthed opportunities for retail investors to receive exposure to the exciting field of artificial intelligence (AI). While there are more than 3,000 startups around the globe focused on commercializing various aspects of AI, the opportunity has largely belonged to venture capital investors, with the exception of a few areas such as pick and shovel investments in AI chips or computer visison and machine vision companies that provide the “eyes” for the AI algorithms to see the world as we do.
In this report for Nanalyze Premium subscribers, we’ve identified five stocks that are pure play investments on computer vision so that readers can now build an investment strategy around this emerging technology.
Looking to invest in AI stocks with real pure-play exposure to this exciting theme? Subscribe to Nanalyze Premium and check out our 30-page investing report on computer vision companies by clicking on the banner below. Read on to check out a report excerpt on our findings related to the computer vision space.
Computer vision technology has already become an integral part of our life. It’s used by your iPhone for facial recognition, when you switch your Tesla to autopilot, or when your friends appear as augmented reality zombies in your weekly group chat on Messenger. The technology also appears in many industrial machine vision solutions like sorting output on production lines or managing warehouse logistics. With the rapid development of artificial intelligence algorithms, computer vision solutions are now able to make autonomous decisions based on many different kinds of visual data. These findings are translated into operational efficiencies, cost savings, and bottom-line impact. While there aren’t many pure-play investment opportunities in AI for retail investors yet, these computer vision companies are the exception.
While each company has its own specific business segments, applications, and strategy, we have also identified some key trends across all these companies that provide insights into how computer vision will be commercialized in the future. Historically, these computer vision companies enjoyed a period of strong revenue growth driven by a growing demand for factory automation solutions. Then growth stalled in 2019, as customer spending decreased in both the consumer electronics and automotive sectors that drove the growth initially. Today, computer vision providers are looking to expand their customer base across multiple sectors to diversify their revenue streams.
Applications that we see with strong growth potential going forward include:
- Packaging for consumer goods and foodstuffs: quality control and package safety inspection tasks
- Robotics: motion control for robots using the feedback of the robot’s position, as detected by a 3D camera
- Internet of Things: cameras complement other sensors in an IoT setup and provide more detailed data for analysis
- Autonomous vehicles: sensor input and image processing that powers autonomous operations
- Neural networks and deep learning: these are the back-end of computer vision systems that recognize and categorize image input
- Logistics: the automated process of acceptance, shipment, and control of goods at warehouses
In order to deliver products to new markets and improve on existing offerings, all of these companies are spending heavily on research and development, reinvesting between 13% and 15% of revenues into R&D. Given the amount of money being plowed back into R&D, it’s strange to see that four out of these five companies pay dividends, something that’s not typical for high-growth tech companies. Profits reinvested in a high-growth business can be put to better use investing in company growth as opposed to giving a bit of cash back to stockholders. Add to that the challenging growth environment, and a dividend policy makes even less sense from where we’re sitting.
Whether these companies remain successful and return to their previous growth trajectory will depend on how they execute their tactical plans, which include diversifying their client bases across multiple sectors and geographies.
Machine Vision vs. Computer Vision
In order to perform the same tasks as humans, AI platforms need to have eyes, a mouth, or a body, depending on the application. The eyes are provided by machine vision systems, the mouth by natural language processing, and the body by robotics. Computer vision – also referred to as machine vision – allows AI algorithms to process, understand, and utilize visual data just as humans do, with the help of high-performance cameras and image processing software. The companies listed in this report are developing either some or all of these components. The first thing you might be wondering is what the difference is between “computer vision” and “machine vision.”
Much of what makes the tech world difficult to grasp for the average layperson is the gratuitous use of terminology. Sometimes the same concepts are regurgitated using new names, so the management consultancies out there can be seen as “thought leaders” and charge exorbitant rates for dubious value. As crafty Southeast Asians on the banana pancake trail like to say, “same same, but different.”
Still, names matter, and this led us to question what the difference is between computer vision and machine vision. In either case, we’re talking about equipping computers with the ability to make sense of the real-world using vision in much the same way humans do.
Machine vision technology came first when a handful of smart PhDs in the 1950s started researching 2D imaging for statistical pattern recognition. Further research centered around MIT in the 1960s and 1970s, and resulted in the first 3D models built from 2D images, as well as the first real-world applications like automatic object detection. In the 1990s, machine vision became a separate industry used in manufacturing environments for inspection, quality control, and sorting tasks on production lines. By that time, cameras and image processing had become quicker and more precise than human inspection. Machine vision systems of old were created for a specific task and a specific set of machines – for example, to check if food products were correctly labeled. These systems had image processing algorithms running in the background that made sense of the visual data input in real time and turned this input into automated (and typically binary) decisions about the products such as good/faulty, large/small, right bin/left bin.
Computer vision grew out of these back-end image processing algorithms with the advent of hardware like GPUs and software like machine learning that lets us capture imagery in extreme detail and analyze it very thoroughly. Computer vision is not constrained to specific use cases or machine systems, and the machine learning algorithms can make sense of input other than photographic imagery or video, including infrared or thermal imagery, motion sensor data, and 3D input. Hence, the technology is more universal and less constrained than machine vision.
The two technologies have significant overlaps, and as one develops it affects the other, and vice versa. Consequently, the two terms are becoming synonymous over time. As the world’s largest global vision and imaging trade group, AIA describes:
“The lines between computer vision and machine vision have been blurring over the years and today, the term machine vision is [also] used in non- industrial environments such as high-end surveillance, biomedical or life science applications, and even in the effort to improve an internet search engine’s ability to provide image recognition in search.”
Regardless of which term you prefer to use, we’ve identified five stocks that are pure play investments on both machine vision and computer vision. Sounds exciting? Subscribe now to read the rest of the 30-page analytical report and get access to other yummy Premium content on Nanalyze.
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