How Computer Vision Applications Reshape Businesses and Solve Real-World Problems
If you’re intimidated by Artificial Intelligence, don’t be. This remarkable technology has already proven to be tremendously helpful to people and businesses. Take one of its subfields – computer vision. It allows machines, semi-controlled vehicles, drones, factories and farm equipment to work effectively and safely. You’ve probably experienced computer vision applications yourself without even knowing it. But how valuable can they be for your business? Let’s find out what computer vision is, how it works and where you can apply it.
To See Through the Eyes of a Machine
Every journey of learning new things starts with framing. According to the McKinsey Global Institute, computer vision is one of the five categories of AI. The other four are natural language, virtual assistants, robotic process automation and advanced machine learning.
Computer vision lets machines see, analyze and make decisions about the visual data. Here’s how it works. A machine goes through three stages or processes:
- It emulates the human eye.
- It emulates the visual cortex.
- It copies the way the human brain responds to visual data.
In simple English, computer vision teaches machines to extract and interpret image content similarly to the human eye.
Various industries are already making use of this technology in self-driving cars, face recognition, digital signage, smart cameras, AR, robots and many more. And you probably won’t be surprised to find out that computer vision is already in your mobile devices.
Computer Vision Algorithms in Action: Deep Learning, CNNs and RNNs
Sure, machines have learned to mimic the human eye, but they often get stuck when it comes to recognizing and extracting the meaning from images. People use their past experiences as context; we apply it to define and categorize what we see. Machines don’t have the luxury to do that.
On the flip side, computer vision has pattern recognition at its core. Here’s what the training process of a classification model for visual data looks like:
- Feeding thousands or even millions of labeled images to the model.
- Collecting patterns, using different learning algorithms in the model.
- Refining patterns and promoting the ones that catch the details in the data best.
Let’s say, we show a computer a million pictures of elephants. It subjects them to different algorithms that analyze the colors, the shapes, the distances between shapes, the borders of the objects, how objects border on each other and so on. This is how the computer defines what an “elephant” means. In theory, this experience will help the computer determine unlabelled images of elephants.
Machines can figure out patterns on their own. This process is called Deep Learning. Within computer vision, Convolutional Neural Networks (CNNs) are usually applied.
How do CNNs work? These networks break images into small matrixes of pixels (aka filters). Then, they calculate these filters and compare them to specific patterns. At first, CNNs define items on the image roughly: just the curves and edges. Several iterations later, they combine surfaces, layers, depths, discontinuities of the spaces and distinguish faces, cars, animals, clothes and the like.
On the whole, CNNs are good at understanding image content, but they fail at processing video. They can’t define and categorize objects that might change over time. Luckily, the computer vision experts have found a way to process video and introduced another algorithm built on CNNs – Recurrent Neural Networks (RNNs). RNNs represent multiple copies of the same network. Each copy passes a message to the aftercomer. RNNs have a chain-like nature and are closely linked to sequences and lists.
What is the difference between the two neural networks? CNNs works with each matrix of pixels independently. RNNs memorize the processed data and apply the accumulated knowledge while making decisions.
Computer Vision Apps and the Industries Reaping Most Benefits
The business interest in computer vision methods has escalated in the last couple of years. No wonder that tech giants like Amazon, Microsoft, Google and Facebook keep investing billions of dollars in computer vision development.
The computer vision market is ripe for innovation and new applications. AI is a versatile technology, so a broad spectrum of industries can adopt it in different ways. Some of the use cases are promoted and visible while others happen behind the scenes. Either way, many products and services have been enhanced by computer vision in:
- Retail and retail security
Retail and retail security
Computer vision apps shape the retail industry, and the Amazon Go store is a fascinating example of the case. These semi-autonomous shops offer a checkout-free shopping experience thanks to the Just Walk Out technology based on computer vision, sensor fusion and deep learning.
Here’s how it works. The technology detects when people take items from the shelves and put them back. It also tracks the items in a virtual cart. Once you’re finished shopping, you can simply leave the store. In a while, Amazon will send you a receipt and charge your Amazon account.
Similarly, another retail giant, Walmart, announced the adoption of computer vision for a cashierless experience. The checkout process is supposed to emulate the one the Amazon Go stores use.
Groceries stores can now use a retail security computer vision app ScanItAll developed by ShopLift. This application aims to reduce theft and other types of losses in chain stores. ScanItAll uses the store’s ceiling video cameras and point-of-sale (POS) systems. POS watches how cashiers scan products at the checkout and labels the ones that were unscanned as a loss.
The automotive industry is no stranger to computer vision and deep learning. Thanks to these technologies, you can enjoy scene analysis, automated lane detection, automated road sign reading that sets speed limits on its own and more. One of the most popular examples is, of course, Tesla’s Autopilot. Computer vision is what helped this driver-assistance system of 2014 become a sensation in 2018.
Volvo also works with computer vision apps and heads towards producing autonomous vehicles. Their City Safety system keeps evolving and has even presented an auto-braking technology in 2018.
Computer vision reshapes healthcare engineering and puts more value into medical imaging. In a recent research, 3D modeling and rapid prototyping have advanced CT and MRI imaging modalities. On top of that, CNNs can be used to detect diseases based on MRI images.
Microsoft’s InnerEye project leverages machine learning and computer vision. The project has a two-fold purpose. First, it delineates cancerous tumors and healthy anatomy. Second, it helps the medical software providers offer radiation oncologists tools for planning radiotherapy treatment.
One more example is Arterys. Their AI assists in diagnostics and speeds up medical workflows. The platform is FDA-approved and applies deep learning to model medical visual data.
While agriculture may not seem like the most digitized of industries, many farmers are already adopting computer vision apps. These technologies help farmers select effective growth methods, spot crop diseases, predict yields and, ultimately, increase profits.
Computer vision applications deal with an exponentially growing amount of imagery data from various industries. Big players like Google, Facebook, Tesla, Microsoft as well as budding startups find new applications for computer vision algorithms each day. Right now, these algorithms are already improving semi-autonomous cars, healthcare engineering, face recognition, customer experience, digital signage, AR, robots and more.
Have you thought about how your business could benefit from computer vision? If so, you need to build a powerful application to make sense of the machine’s output. That’s what we’re here for. Reach out Skelia to shape your ideas into software.