What is Computer Vision? | The Art of Creating Seeing Machines
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I feel like not too many of you, especially students, know about computer vision. 💻🧿 
And it's, in my opinion, one of the most interesting and economically beneficial fields of the 21st century.

From self-driving cars to holograms of the mixed reality world, detecting theft in retail over to creating visual art - all of that is possible thanks to computer vision.

You'll learn about:
✔️ Computer vision field
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⌚️ Timetable:
00:00 Enter the Computer Vision
00:26 Rant
00:42 Early days
01:20 Machine vs Human
02:00 Mixed Reality
05:04 Self-driving cars
07:18 Art creation
08:24 Deepfakes
09:05 lip-reading, surveillance, OCR, computational photography, etc.
10:50 CV is still an open area of research, adversarial examples
11:27 Outro

Note: I make these without the intent of monetizing them and for purely educational purposes. A lot of the materials I used in this video came from other great creators and it'd be hard to credit all of them but I believe I've made it with YouTube's fairness guidelines in mind, if not somebody should warn me I guess hahaha.

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#computervision #mixedreality #selfdrivingcars #deeplearning
 Ever heard of computer vision? You know, the usual holograms. Isn't that science fiction? You know, the thing that allows you to play guitar while driving? The tag behind L2L4 self-driving cars? The computer vision that showed us what neural networks see under psychedelics? And the same tag that allows you to transfer awesome visual style to video is for images? For some reason, not many of you know about this field. And everybody knows about 50 shades of JavaScript frameworks. Yeah, I'm a full stack developer. Vue.js, Amber.js, Node.js, Android, iOS. But nobody wants to do computer vision. The early days of computer vision started in 50s. But in early 60s, Larry Roberts, who's considered as the godfather of computer vision, wrote this seminal paper titled Machine Perception of Three Dimensional Solids. Back then, we only played with two examples, the so-called blocks world, still trying to figure out how to extract 3D information from 2D drawings. It's interesting how human intuition fails many times miserably throughout human history. In 1966, now everybody says Marvin Minsky, but actually Seymour Puppard, an MIT guy, gave his students a summer project. Go and solve this computer vision thing in three months. Half a century later, we're still trying to solve the same project. Fast forward to 2020, we're still struggling, but we achieved enormous progress, especially from 2012 onward with the arrival of AlexNet. In some narrow domains, machines are already better than humans. So in 2015, this neural network called ResNet achieved lower error on an image classification challenge than humans. What do you see in this image? Is that a cat? This is all debatable. But in these narrow domains, neural nets are better than humans. Aside from image classification, these models are better in lip reading by far and also in art creation. And those are just a few examples. There are many more. Let's see some cool applications of computer vision. I'll explain the usage of computer vision in mixed reality in the context of Microsoft HoloLens, the holographic computer. I actually had the honor of working as a computer vision developer on the HoloLens 2 project. What's mixed reality, you may ask. In short, mixed reality is a spectrum where on one end we have the physical reality, and on the other end of the spectrum we have full immersion, i.e. virtual reality. Everything in between is where the holograms, like the one you see on the screen, live. The device has a couple of computer vision stacks that makes this magic running. It uses cameras, an depth sensor, and a couple of other sensors to extract geometry and create a 3D map of the world around it. So that grid, also called mesh, that you see in the clip is what I'm referring to. It also understands the semantics of the scene, so whether a particular mesh belongs to a wall, a ceiling, or a floor, etc. This geometry and scene understanding lets you place holograms in the world. But you also want to interact with them, so HoloLens needs to understand you. The device understands your eyes, and eyes are such a powerful way to understand someone's intent. It has iris recognition, which enables you to log in really fast, thanks to the computer vision exploiting the nice properties of biometry. Let's see it in action. It does all of this using two infrared cameras and lots of machine learning. And it has eye tracking, which means it knows exactly where you're looking at, which enables a whole range of awesome applications where you can control stuff with your eyes. And you can see in this clip this auto-scrolling feature that allows you to scroll through a page using only your eyes. HoloLens also understands your hands, and allows for instinctual interactions with holograms. It basically infers the 3D mesh around your hands, and that allows developers to figure out when the hands are touching the holograms, which are also defined by their 3D mesh. Check it out. And you can resize them, rotate them, or do whatever you want to do with them. Take a look at this clip. I can just grab this corn to resize it, or I can rotate it, or move it. Now I've used HoloLens many, many times, and I can tell you it really looks the way you can see it here on the screen. Except actually it's much better, because you can see in 3D, but the field of view is kind of narrower, so holograms can get cropped sometimes. There's one more interesting thing I want to show you here, and that's holportation. And the idea is to transfer the 3D information of an object from one distant location into another. And you will see in this video that this dad gets to talk in real time with his daughter, who's somewhere else. Take a look at it. And you can do some pretty interesting things once you get this 3D information. Take a look. Next up, computer vision in self-driving cars. There is this debate going on in the self-driving cars world whether to use LiDAR or not. And LiDAR is this thing similar to RADAR, except that it works in the visible light part of the EM spectrum. So Elon Musk is against LiDAR, most L4 companies are using LiDAR, but everybody agrees that we need to use cameras, which is kind of obvious, as the roads were built for human eyes, right? Self-driving cars use vision extensively, both inside the car for driver state sensing, as well as outside the car. There's just so much vision going on for tracking the outside world. Panoptic segmentation is one technique that is used really often, and it gives out a lot of information. It basically classifies pixels into certain classes like pedestrian, road, or car, say, but also into specific instance. So you differentiate between different instances of the same class. Here is an example of what the car sees. You can mostly see semantic segmentation visualized here. The car tracks driver-built surfaces, lines, intersections, traffic lights, pedestrians, vehicles. You need depth in order to avoid collision, so a lot of information. Here are all of those things visualized. You also want to track the driver, maybe even passengers, to reduce risk of getting into an accident. You can monitor whether the driver is sleepy, not focusing on the road ahead, or whether passengers are just distracting the driver. Here is an example of tracking whether the driver is focusing on the road. And you can take this even further and know exactly what the driver is doing by performing action recognition on the driver. So check this out. Computer vision enables perception, which is just a part of the self-driving pipeline, albeit really crucial and infrastructural one. Perceptual information is further propagated into the planning and control software of the vehicle. Which actually drives the vehicle, but it does not use computer vision at all. Next up, art. There are two techniques I'd like to mention here. One is deep dreaming, and the other one is neural style transfer. The image you see on the screen is an example of deep dreaming. Deep dreaming exploits what is called the pareidolia effect. You know how sometimes when you look at the moon, you see a human face? That's pareidolia. So how it works is we give the network some input, and whatever the network sees, it will be given some input, and whatever the network sees in the image, we just amplify that part. And I'm obviously simplifying things a little bit here, but that's how it works in a nutshell. If we feed the output with some small geometrical transformations applied, like say crop, back to the input, we get these trippy videos. I've got a whole series on neural style transfer, and in a nutshell, you just combine the content image with the style image using neural network as the combiner, and you get this beautiful result. There is this observation that perception is somewhat connected to our creation itself. If you're able to percept like these neural networks are, then you're able to create art. Think about it. Our creation is something we consider to be a deeply human trait. Now let's jump to deep fakes. Deep fakes, the curse child of computer vision, lets you use your face and your voice, but look like somebody else and sound like someone else. Take a look at this video from MIT's introductory lecture to deep learning, where they imitate Barack Obama. Keep in mind that the voice quality was degraded by design. In fact, this entire speech and video are not real, and were created using deep learning and artificial intelligence. Decently complicated thing to create. It involves various computer vision techniques, such as facial landmarks detection, optical flow calculation, taking occlusions into consideration, etc. Just be aware that these are out there. There are many more awesome computer vision applications, like those that help us understand humans, say lip reading, or monitoring pulse through image, a technique called motion magnification. We automatically select and amplify a narrow band of temporal frequencies around the human heart rate. This one could be used to monitor babies, and that could save lives. Here we extract heart rate measurements of a newborn, and confirm their accuracy by comparing them with readings from the hospital monitor. Surveillance is another big application area, applications such as crowd counting. Now this may sound rebellion, but you can use the related tech to monitor traffic and thus improve the traffic. You can also detect victims drowning in the pool, or you can detect the theft in retail. I also like the fact that computer vision is redefining search. So instead of using textual search, you can search using images and find articles that contain those images, or similar images. Definitely check out Google image search if you haven't. I used it a couple of times to figure out where some image originated in the internet. There are obviously many cool applications I haven't mentioned, like Google Earth, that can be used as a health monitor for our planet, say for tracking deforestation over time. Biometry applications such as understanding iris, which I briefly mentioned in the mixed reality section, fingerprints, face, even the way someone walks can be used as a unique identifier of a person, although with varying levels of success. Extracting text, also called optical character recognition, is really useful. You just take a photo of say a whiteboard, and it just automatically extracts all the text for you. This is actually still a difficult problem in computer vision. And finally things we take for granted. Computational photography, HDR, stabilization, autofocus, all those things that help you capture beautiful photographs. They are ingrained into little level camera software so you don't even know they're there. We talked about all the cool apps, but listen, computer vision is still an open research area, and sometimes algorithms are less intelligent than we'd like them to be. A famous example are adversarial examples, where you just tweak the input image pixels in a clever way, and it totally destroyed the algorithm, making it see airliner where there is a pig in the image. I think we came a long way developing all the cameras, low level and high level vision software, and learning algorithms, but we still need to ingrain a true understanding and cognition into these algorithms. If you found this video useful, consider supporting the channel by subscribing and sharing. I work as a full-time machine learning engineer in Microsoft, and I create these in my free time. So I really appreciate when I get the feedback that somebody finds these useful. Until next time, keep learning.
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There are two techniques I'd like to mention here. One is deep dreaming,"}, {"start": 436.79999999999995, "end": 440.56, "text": " and the other one is neural style transfer. The image you see on the screen is an example"}, {"start": 440.56, "end": 444.71999999999997, "text": " of deep dreaming. Deep dreaming exploits what is called the pareidolia effect. You know"}, {"start": 444.71999999999997, "end": 449.35999999999996, "text": " how sometimes when you look at the moon, you see a human face? That's pareidolia. So how"}, {"start": 449.35999999999996, "end": 454.08, "text": " it works is we give the network some input, and whatever the network sees, it will be"}, {"start": 454.08, "end": 459.03999999999996, "text": " given some input, and whatever the network sees in the image, we just amplify that part."}, {"start": 459.68, "end": 463.35999999999996, "text": " And I'm obviously simplifying things a little bit here, but that's how it works in a nutshell."}, {"start": 463.35999999999996, "end": 466.88, "text": " If we feed the output with some small geometrical transformations applied,"}, {"start": 466.88, "end": 471.03999999999996, "text": " like say crop, back to the input, we get these trippy videos."}, {"start": 477.2, "end": 482.0, "text": " I've got a whole series on neural style transfer, and in a nutshell, you just combine the content"}, {"start": 482.0, "end": 487.6, "text": " image with the style image using neural network as the combiner, and you get this beautiful result."}, {"start": 487.6, "end": 492.16, "text": " There is this observation that perception is somewhat connected to our creation itself."}, {"start": 492.16, "end": 497.12, "text": " If you're able to percept like these neural networks are, then you're able to create art."}, {"start": 497.12, "end": 501.6, "text": " Think about it. Our creation is something we consider to be a deeply human trait."}, {"start": 501.6, "end": 507.04, "text": " Now let's jump to deep fakes. Deep fakes, the curse child of computer vision,"}, {"start": 507.04, "end": 512.48, "text": " lets you use your face and your voice, but look like somebody else and sound like someone else."}, {"start": 512.48, "end": 516.64, "text": " Take a look at this video from MIT's introductory lecture to deep learning,"}, {"start": 517.28, "end": 523.12, "text": " where they imitate Barack Obama. Keep in mind that the voice quality was degraded by design."}, {"start": 523.12, "end": 529.12, "text": " In fact, this entire speech and video are not real, and were created using deep learning"}, {"start": 529.12, "end": 532.32, "text": " and artificial intelligence. Decently complicated thing to create."}, {"start": 532.32, "end": 536.72, "text": " It involves various computer vision techniques, such as facial landmarks detection,"}, {"start": 536.72, "end": 542.8000000000001, "text": " optical flow calculation, taking occlusions into consideration, etc. Just be aware that these are"}, {"start": 542.8000000000001, "end": 548.0, "text": " out there. There are many more awesome computer vision applications, like those that help us"}, {"start": 548.0, "end": 555.9200000000001, "text": " understand humans, say lip reading, or monitoring pulse through image, a technique called motion"}, {"start": 555.9200000000001, "end": 560.64, "text": " magnification. We automatically select and amplify a narrow band of temporal frequencies around the"}, {"start": 560.64, "end": 565.52, "text": " human heart rate. This one could be used to monitor babies, and that could save lives."}, {"start": 565.52, "end": 569.68, "text": " Here we extract heart rate measurements of a newborn, and confirm their accuracy by"}, {"start": 569.68, "end": 574.16, "text": " comparing them with readings from the hospital monitor. Surveillance is another big application"}, {"start": 574.16, "end": 581.92, "text": " area, applications such as crowd counting. Now this may sound rebellion, but you can use"}, {"start": 581.92, "end": 587.36, "text": " the related tech to monitor traffic and thus improve the traffic. You can also detect victims"}, {"start": 587.36, "end": 591.92, "text": " drowning in the pool, or you can detect the theft in retail. I also like the fact that computer vision"}, {"start": 591.92, "end": 597.04, "text": " is redefining search. So instead of using textual search, you can search using images and find"}, {"start": 597.04, "end": 601.36, "text": " articles that contain those images, or similar images. Definitely check out Google image search"}, {"start": 601.36, "end": 606.24, "text": " if you haven't. I used it a couple of times to figure out where some image originated in the"}, {"start": 606.24, "end": 610.7199999999999, "text": " internet. There are obviously many cool applications I haven't mentioned, like Google Earth, that can be"}, {"start": 610.7199999999999, "end": 615.76, "text": " used as a health monitor for our planet, say for tracking deforestation over time. Biometry"}, {"start": 615.76, "end": 620.0, "text": " applications such as understanding iris, which I briefly mentioned in the mixed reality section,"}, {"start": 620.0, "end": 625.28, "text": " fingerprints, face, even the way someone walks can be used as a unique identifier of a person,"}, {"start": 625.28, "end": 630.72, "text": " although with varying levels of success. Extracting text, also called optical character recognition,"}, {"start": 630.72, "end": 635.68, "text": " is really useful. You just take a photo of say a whiteboard, and it just automatically extracts"}, {"start": 635.68, "end": 639.92, "text": " all the text for you. This is actually still a difficult problem in computer vision. And finally"}, {"start": 639.92, "end": 645.04, "text": " things we take for granted. Computational photography, HDR, stabilization, autofocus,"}, {"start": 645.04, "end": 649.2, "text": " all those things that help you capture beautiful photographs. They are ingrained into little"}, {"start": 649.2, "end": 654.72, "text": " level camera software so you don't even know they're there. We talked about all the cool apps,"}, {"start": 654.72, "end": 660.08, "text": " but listen, computer vision is still an open research area, and sometimes algorithms are less"}, {"start": 660.08, "end": 664.5600000000001, "text": " intelligent than we'd like them to be. A famous example are adversarial examples, where you just"}, {"start": 664.5600000000001, "end": 670.88, "text": " tweak the input image pixels in a clever way, and it totally destroyed the algorithm, making it see"}, {"start": 670.88, "end": 676.96, "text": " airliner where there is a pig in the image. I think we came a long way developing all the cameras,"}, {"start": 676.96, "end": 683.2800000000001, "text": " low level and high level vision software, and learning algorithms, but we still need to ingrain"}, {"start": 683.2800000000001, "end": 688.24, "text": " a true understanding and cognition into these algorithms. If you found this video useful,"}, {"start": 688.24, "end": 693.76, "text": " consider supporting the channel by subscribing and sharing. I work as a full-time machine learning"}, {"start": 693.76, "end": 699.6, "text": " engineer in Microsoft, and I create these in my free time. So I really appreciate when I get the"}, {"start": 699.6, "end": 710.24, "text": " feedback that somebody finds these useful. Until next time, keep learning."}]