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10 Ways Augmented Reality Uses Machine Learning

What’s the difference between virtual reality and augmented reality? It might seem like semantics, but it’s important to understand that the two are not the same thing. Virtual reality (VR) immerses users in an entirely made-up world through video games, movie experiences, and even product design, whereas augmented reality (AR) enhances your experience of reality by superimposing graphics over your view of real-world objects—or rather, what your device sees of them through its camera.

Augmented Realitty
Virtual Reality

1) Machine Learning for Face Tracking

Face tracking is a great example of how machine learning can enhance augmented reality. For example, Apple’s new iPhone X uses machine learning to enable Face ID, allowing users to unlock their phone by looking at it rather than entering a passcode. Likewise, Microsoft uses Face Tracking in its HoloLens, enabling users to use their faces as inputs for navigating and interacting with holograms. AR would be far less powerful without machine learning, enabling input methods that take advantage of all 5 senses (as opposed to only two).

2) Object Recognition and Detection

This is a particularly important aspect of augmented reality. It’s how your computer knows that there’s a virtual dragon walking around on your coffee table and not a real one. Object recognition and detection require machine learning for computers to not only be able to recognize objects, but also learn what those objects look like based on new perspectives, lighting conditions, and resolutions. Your phone or laptop already has object-recognition capabilities, but it will only get better over time with further research.
The flip side of object recognition is object detection. When your computer knows what an object looks like, it can tell you if there are any nearby. If a car is barreling toward you, for example, your AR glasses will see it coming and give you time to react accordingly. This isn’t as far-fetched as it sounds; cars with object-detection software on their dashboards are already being produced for manufacturing in 2019. It just so happens that your next car could have AR capabilities built right in!
Besides detecting objects or characters in real time, computers using machine learning can also generate their own virtual content based on what they see.

3) Image Recognition

While augmented reality experiences can include data collected via other means (human input, on-device sensors), image recognition is an area in which machine learning has a big role to play. This is especially true as augmented reality continues to make its way into more areas of our lives. Take Snapchat’s Lens Studio as an example. With it, users can create their own augmented reality experiences using one of Snapchat’s stock avatars or one they’ve created themselves using 3D objects from Facebook’s library of assets. Users can also take a picture from their camera roll and superimpose any object they choose onto it—and all of that is thanks to image recognition provided by machine learning technology.

4) Hand Pose Estimation

Being able to estimate hand pose from a single depth camera is valuable for applications like real-time object manipulation and virtual keyboard typing. Common approaches include: Gradient-based methods: such as those based on Principal Component Analysis (PCA) of depth gradients, or techniques that incorporate calibration information into reconstruction. Model-based approaches: like optical flow, trajectory fitting, or joint bilateral filters. Machine learning-based methods: like variants of convolutional neural networks (CNNs), deep belief networks (DBNs), recurrent neural networks (RNNs), and conditional random fields (CRFs).

5) 3D Reconstruction

One of augmented reality’s biggest strengths is its ability to interact with a physical world in real time. The most common way to do that is through 3D reconstruction and mapping, which means you scan a room, create an accurate replica of it virtually and place objects in that space. Even more impressive is ARKit’s ability to detect horizontal planes—including tables and chairs—in front of you, so virtual objects can interact with them naturally. But what happens when those virtual things aren’t in front of a flat surface? You can then use machine learning to simulate physics based on how light reflects off objects. For example, if you drop a virtual ball, it will bounce off surfaces based on their height and material (to add more realism).

6) Natural Language Processing

One of augmented reality’s biggest hurdles is natural language processing (NLP). This allows AR apps to recognize and respond to text that appears in their environment. So if a user were wearing an AR headset and walking through a city, they could see text overlaid on signs, walls and billboards. The NLP algorithms would be able to read that text and, depending on what it says, could trigger a response or action. For example, if someone walks by a restaurant with an available table then sees No tables available tonight printed across its storefront window, he or she might say Ok Google, show me restaurants with tables open nearby without having to do any additional typing or clicking.

7) Language Translation

One of AR’s most exciting features is its ability to translate foreign languages by simply pointing your phone at words or signs. Google Translate uses machine learning and an enormous repository of language translations to power their translation app, which in turn powers a number of other augmented reality apps. All machine learning has to do is find patterns in millions upon millions of previous translations and then use that information to make new ones—in essence it just follows what most people would do naturally when translating, but does it faster and more accurately than a human ever could. With improvements like neural networks on board, what once seemed like science fiction will soon be part of our daily lives.

8) Facial Expression Recognition

Facial recognition is a computer science term for technology that identifies and tracks faces in a digital image or video. It’s commonly used in security systems so you can unlock your front door using just your face, it’s also widely used by advertisers to make targeted offers to consumers. Facial recognition is an integral part of virtual reality (VR) because it allows you to identify friends and foes through cameras that are built into VR headsets.
Facial recognition is also being used to detect and measure emotions, a feature that can be useful for some VR training applications. For example, by using facial expression recognition technology and software, doctors can train for complex surgeries like heart transplants, which involve emotionally-charged interactions with patients. Facial recognition tech is so good now that it’s now being used in smart TVs to allow you to automatically log in simply by giving a quick glance at your screen. It’s also widely used in CCTV systems so law enforcement officials can identify suspects from large crowds and review video footage of crimes from different angles.

9) Gaze Tracking and Eye Movement Tracking

One of AR’s most useful machine learning applications is gaze tracking and eye movement tracking. By understanding what a user is looking at in real time, virtual objects can be placed in a scene in such a way that they align with what’s naturally being viewed. This works especially well when it comes to placing 3D models into 2D scenes, like those captured on photographs or video footage. Gaze tracking also helps virtual avatars behave more realistically as they track users’ line of sight and move accordingly within an environment. VR controllers can be used as replacement of many AR applications, but AR apps usually require eye tracking data to create augmented reality experience

10) Managing Data-Driven AR Applications

AR apps can deliver a lot of value, but they also create new challenges for mobile app developers. Let’s take a look at how to manage these data-driven AR applications. Tracking Objects : Object tracking is an essential element in developing an AR app that tracks objects and keeps them in place while users move around them.
For example, if a developer wants to create an AR app that will allow users to view paintings and statues as if they were in front of them, object tracking is necessary. Without it, it would be impossible for users to experience that kind of immersive AR content. To achieve object tracking in your AR app, you can use machine learning algorithms. This is what makes most modern AR apps so lifelike—algorithms are constantly adjusting images based on new data gathered by your smartphone’s camera.


Many new technologies, like augmented reality and virtual reality, have started to become available for consumers in recent years. While these devices are still not affordable for everyone, there are many ways that you can use them to improve your work environment and make you more productive. If you’re interested in learning more about how augmented reality can help your business, it’s important to know exactly what type of device is right for you. You should also be aware of what aspects of your business will benefit from using an AR or VR solution and how these technologies fit into your overall goal of making your life better at work. To learn more about how augmented reality benefits businesses large and small, contact us today.

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