WEBVTT 00:00:01.114 --> 00:00:08.606 So over the past few centuries, microscopes have revolutionized our world. 00:00:09.036 --> 00:00:14.252 They revealed to us a tiny world of objects, life and structures 00:00:14.252 --> 00:00:17.158 that are too small for us to see with our naked eyes. 00:00:17.158 --> 00:00:20.177 They are a tremendous contribution to science and technology. 00:00:20.177 --> 00:00:23.404 Today I'd like to introduce you to a new type of microscope, 00:00:23.404 --> 00:00:25.982 a microscope for changes. 00:00:25.982 --> 00:00:28.884 It doesn't use optics like a regular microscope 00:00:28.884 --> 00:00:30.881 to make small objects bigger, 00:00:30.881 --> 00:00:35.257 but instead it uses a video camera and image processing 00:00:35.257 --> 00:00:40.513 to reveal to us the tiniest motions and color changes in objects and people, 00:00:40.513 --> 00:00:44.355 changes that are impossible for us to see with our naked eyes. 00:00:44.355 --> 00:00:48.475 And it lets us look at our world in a completely new way. NOTE Paragraph 00:00:48.475 --> 00:00:50.385 So what do I mean by color changes? 00:00:50.385 --> 00:00:53.217 Our skin, for example, changes its color very slightly 00:00:53.217 --> 00:00:55.214 when the blood flows under it. 00:00:55.214 --> 00:00:57.611 That change is incredibly subtle, 00:00:57.611 --> 00:00:59.674 which is why, when you look at other people, 00:00:59.674 --> 00:01:01.925 when you look at the person sitting next to you, 00:01:01.925 --> 00:01:05.500 you don't see their skin or their face changing color. 00:01:05.500 --> 00:01:09.860 When we look at this video of Steve here, it appears to us like a static picture, 00:01:09.860 --> 00:01:13.720 but once we look at this video through our new, special microscope, 00:01:13.720 --> 00:01:16.320 suddenly we see a completely different image. 00:01:16.320 --> 00:01:20.250 What you see here are small changes in the color of Steve's skin, 00:01:20.250 --> 00:01:24.686 magnified 100 times so that they become visible. 00:01:24.686 --> 00:01:27.953 We can actually see a human pulse. 00:01:27.953 --> 00:01:31.180 We can see how fast Steve's heart is beating, 00:01:31.180 --> 00:01:36.535 but we can also see the actual way that the blood flows in his face. 00:01:36.544 --> 00:01:39.175 And we can do that not just to visualize the pulse, 00:01:39.175 --> 00:01:42.646 but also to actually recover our heart rates, 00:01:42.646 --> 00:01:44.439 and measure our heart rates. 00:01:44.439 --> 00:01:48.892 And we can do it with regular cameras and without touching the patients. 00:01:48.892 --> 00:01:54.509 So here you see the pulse and heart rate we extracted from a neonatal baby 00:01:54.509 --> 00:01:57.390 from a video we took with a regular DSLR camera, 00:01:57.390 --> 00:01:59.206 and the heart rate measurement we get 00:01:59.206 --> 00:02:04.017 is as accurate as the one you'd get with a standard monitor in a hospital. 00:02:04.017 --> 00:02:06.659 And it doesn't even have to be a video we recorded. 00:02:06.659 --> 00:02:09.654 We can do it essentially with other videos as well. 00:02:09.654 --> 00:02:13.555 So I just took a short clip from "Batman Begins" here 00:02:13.555 --> 00:02:15.459 just to show Christian Bale's pulse. 00:02:15.459 --> 00:02:17.281 (Laughter) 00:02:17.281 --> 00:02:19.404 And you know, presumably he's wearing makeup, 00:02:19.404 --> 00:02:21.357 the lighting here is kind of challenging, 00:02:21.357 --> 00:02:24.308 but still, just from the video, we're able to extract his pulse 00:02:24.308 --> 00:02:26.326 and show it quite well. NOTE Paragraph 00:02:26.326 --> 00:02:28.246 So how do we do all that? 00:02:28.246 --> 00:02:32.844 We basically analyze the changes in the light that are recorded 00:02:32.844 --> 00:02:35.115 at every pixel in the video over time, 00:02:35.115 --> 00:02:36.913 and then we crank up those changes. 00:02:36.913 --> 00:02:39.075 We make them bigger so that we can see them. 00:02:39.075 --> 00:02:40.977 The tricky part is that those signals, 00:02:40.977 --> 00:02:43.910 those changes that we're after, are extremely subtle, 00:02:43.910 --> 00:02:46.689 so we have to be very careful when you try to separate them 00:02:46.689 --> 00:02:50.520 from noise that always exists in videos. 00:02:50.520 --> 00:02:53.515 So we use some clever image processing techniques 00:02:53.515 --> 00:02:57.509 to get a very accurate measurement of the color at each pixel in the video, 00:02:57.509 --> 00:03:00.179 and then the way the color changes over time, 00:03:00.179 --> 00:03:02.872 and then we amplify those changes. 00:03:02.872 --> 00:03:06.852 We make them bigger to create those types of enhanced videos, or magnified videos, 00:03:06.852 --> 00:03:09.024 that actually show us those changes. NOTE Paragraph 00:03:09.024 --> 00:03:13.262 But it turns out we can do that not just to show tiny changes in color, 00:03:13.262 --> 00:03:15.503 but also tiny motions, 00:03:15.503 --> 00:03:19.079 and that's because the light that gets recorded in our cameras 00:03:19.079 --> 00:03:21.889 will change not only if the color of the object changes, 00:03:21.889 --> 00:03:24.257 but also if the object moves. 00:03:24.257 --> 00:03:27.893 So this is my daughter when she was about two months old. 00:03:27.893 --> 00:03:30.892 It's a video I recorded about three years ago. 00:03:30.892 --> 00:03:34.100 And as new parents, we all want to make sure our babies are healthy, 00:03:34.100 --> 00:03:36.642 that they're breathing, that they're alive, of course. 00:03:36.642 --> 00:03:38.784 So I too got one of those baby monitors 00:03:38.784 --> 00:03:41.253 so that I could see my daughter when she was asleep. 00:03:41.253 --> 00:03:44.780 And this is pretty much what you'll see with a standard baby monitor. 00:03:44.780 --> 00:03:48.462 You can see the baby's sleeping, but there's not too much information there. 00:03:48.474 --> 00:03:50.078 There's not too much we can see. 00:03:50.078 --> 00:03:52.902 Wouldn't it be better, or more informative, or more useful, 00:03:52.902 --> 00:03:55.892 if instead we could look at the view like this. 00:03:55.892 --> 00:04:02.248 So here I took the motions and I magnified them 30 times, 00:04:02.248 --> 00:04:06.074 and then I could clearly see that my daughter was indeed alive and breathing. 00:04:06.074 --> 00:04:08.327 (Laughter) 00:04:08.327 --> 00:04:10.249 Here is a side-by-side comparison. 00:04:10.249 --> 00:04:12.732 So again, in the source video, in the original video, 00:04:12.732 --> 00:04:14.368 there's not too much we can see, 00:04:14.368 --> 00:04:18.075 but once we magnify the motions, the breathing becomes much more visible. 00:04:18.075 --> 00:04:20.145 And it turns out, there's a lot of phenomena 00:04:20.145 --> 00:04:23.768 we can reveal and magnify with our new motion microscope. 00:04:23.768 --> 00:04:28.332 We can see how our veins and arteries are pulsing in our bodies. 00:04:28.332 --> 00:04:30.960 We can see that our eyes are constantly moving 00:04:30.960 --> 00:04:32.847 in this wobbly motion. 00:04:32.847 --> 00:04:34.356 And that's actually my eye, 00:04:34.356 --> 00:04:37.421 and again this video was taken right after my daughter was born, 00:04:37.421 --> 00:04:41.623 so you can see I wasn't getting too much sleep. (Laughter) 00:04:41.623 --> 00:04:44.339 Even when a person is sitting still, 00:04:44.339 --> 00:04:46.383 there's a lot of information we can extract 00:04:46.383 --> 00:04:49.912 about their breathing patterns, small facial expressions. 00:04:49.912 --> 00:04:51.537 Maybe we could use those motions 00:04:51.537 --> 00:04:54.691 to tell us something about our thoughts or our emotions. 00:04:54.691 --> 00:04:57.946 We can also magnify small mechanical movements, 00:04:57.946 --> 00:04:59.501 like vibrations in engines, 00:04:59.501 --> 00:05:03.193 that can help engineers detect and diagnose machinery problems, 00:05:03.193 --> 00:05:07.931 or see how our buildings and structures sway in the wind and react to forces. 00:05:07.931 --> 00:05:12.512 Those are all things that our society knows how to measure in various ways, 00:05:12.512 --> 00:05:14.965 but measuring those motions is one thing, 00:05:14.965 --> 00:05:17.241 and actually seeing those motions as they happen 00:05:17.241 --> 00:05:19.795 is a whole different thing. NOTE Paragraph 00:05:19.795 --> 00:05:22.836 And ever since we discovered this new technology, 00:05:22.836 --> 00:05:26.789 we made our code available online so that others could use and experiment with it. 00:05:26.789 --> 00:05:28.664 It's very simple to use. 00:05:28.664 --> 00:05:30.708 It can work on your own videos. 00:05:30.708 --> 00:05:33.901 Our collaborators at Quanta Research even created this nice website 00:05:33.901 --> 00:05:36.579 where you can upload your videos and process them online, 00:05:36.579 --> 00:05:40.395 so even if you don't have any experience in computer science or programming, 00:05:40.395 --> 00:05:43.331 you can still very easily experiment with this new microscope. 00:05:43.331 --> 00:05:45.735 And I'd like to show you just a couple of examples 00:05:45.735 --> 00:05:48.470 of what others have done with it. NOTE Paragraph 00:05:48.470 --> 00:05:53.787 So this video was made by a YouTube user called Tamez85. 00:05:53.787 --> 00:05:55.250 I don't know who that user is, 00:05:55.250 --> 00:05:57.595 but he, or she, used our code 00:05:57.595 --> 00:06:01.310 to magnify small belly movements during pregnancy. 00:06:01.310 --> 00:06:02.912 It's kind of creepy. 00:06:02.912 --> 00:06:04.525 (Laughter) 00:06:04.525 --> 00:06:09.486 People have used it to magnify pulsing veins in their hands. 00:06:09.486 --> 00:06:13.268 And you know it's not real science unless you use guinea pigs, 00:06:13.268 --> 00:06:16.658 and apparently this guinea pig is called Tiffany, 00:06:16.658 --> 00:06:19.607 and this YouTube user claims it is the first rodent on Earth 00:06:19.607 --> 00:06:22.295 that was motion-magnified. NOTE Paragraph 00:06:22.295 --> 00:06:24.483 You can also do some art with it. 00:06:24.483 --> 00:06:27.501 So this video was sent to me by a design student at Yale. 00:06:27.501 --> 00:06:29.638 She wanted to see if there's any difference 00:06:29.638 --> 00:06:31.160 in the way her classmates move. 00:06:31.160 --> 00:06:35.369 She made them all stand still, and then magnified their motions. 00:06:35.369 --> 00:06:38.747 It's like seeing still pictures come to life. 00:06:38.747 --> 00:06:41.180 And the nice thing with all those examples 00:06:41.180 --> 00:06:43.476 is that we had nothing to do with them. 00:06:43.476 --> 00:06:47.330 We just provided this new tool, a new way to look at the world, 00:06:47.330 --> 00:06:52.462 and then people find other interesting, new and creative ways of using it. NOTE Paragraph 00:06:52.462 --> 00:06:54.226 But we didn't stop there. 00:06:54.226 --> 00:06:57.477 This tool not only allows us to look at the world in a new way, 00:06:57.477 --> 00:06:59.845 it also redefines what we can do 00:06:59.845 --> 00:07:03.026 and pushes the limits of what we can do with our cameras. 00:07:03.026 --> 00:07:05.255 So as scientists, we started wondering, 00:07:05.255 --> 00:07:09.040 what other types of physical phenomena produce tiny motions 00:07:09.040 --> 00:07:11.943 that we could now use our cameras to measure? 00:07:11.943 --> 00:07:15.944 And one such phenomenon that we focused on recently is sound. 00:07:15.944 --> 00:07:18.049 Sound, as we all know, is basically changes 00:07:18.049 --> 00:07:20.232 in air pressure that travel through the air. 00:07:20.232 --> 00:07:23.853 Those pressure waves hit objects and they create small vibrations in them, 00:07:23.853 --> 00:07:26.385 which is how we hear and how we record sound. 00:07:26.385 --> 00:07:30.053 But it turns out that sound also produces visual motions. 00:07:30.053 --> 00:07:32.886 Those are motions that are not visible to us 00:07:32.886 --> 00:07:35.887 but are visible to a camera with the right processing. 00:07:35.887 --> 00:07:37.460 So here are two examples. 00:07:37.460 --> 00:07:40.074 This is me demonstrating my great singing skills. 00:07:41.064 --> 00:07:42.698 (Singing) 00:07:42.698 --> 00:07:44.134 (Laughter) 00:07:44.134 --> 00:07:47.120 And I took a high-speed video of my throat while I was humming. 00:07:47.120 --> 00:07:48.884 Again, if you stare at that video, 00:07:48.884 --> 00:07:50.960 there's not too much you'll be able to see, 00:07:50.960 --> 00:07:55.292 but once we magnify the motions 100 times, we can see all the motions and ripples 00:07:55.292 --> 00:07:58.566 in the neck that are involved in producing the sound. 00:07:58.566 --> 00:08:01.306 That signal is there in that video. NOTE Paragraph 00:08:01.306 --> 00:08:03.976 We also know that singers can break a wine glass 00:08:03.976 --> 00:08:05.439 if they hit the correct note. 00:08:05.439 --> 00:08:07.204 So here, we're going to play a note 00:08:07.204 --> 00:08:09.730 that's in the resonance frequency of that glass 00:08:09.730 --> 00:08:11.778 through a loudspeaker that's next to it. 00:08:11.778 --> 00:08:16.197 Once we play that note and magnify the motions 250 times, 00:08:16.197 --> 00:08:18.535 we can very clearly see how the glass vibrates 00:08:18.535 --> 00:08:22.105 and resonates in response to the sound. 00:08:22.105 --> 00:08:24.525 It's not something you're used to seeing every day. 00:08:24.525 --> 00:08:28.054 But this made us think. It gave us this crazy idea. 00:08:28.054 --> 00:08:33.662 Can we actually invert this process and recover sound from video 00:08:33.662 --> 00:08:37.697 by analyzing the tiny vibrations that sound waves create in objects, 00:08:37.697 --> 00:08:42.474 and essentially convert those back into the sounds that produced them. 00:08:42.474 --> 00:08:46.931 In this way, we can turn everyday objects into microphones. NOTE Paragraph 00:08:46.931 --> 00:08:49.163 So that's exactly what we did. 00:08:49.163 --> 00:08:51.979 So here's an empty bag of chips that was lying on a table, 00:08:51.979 --> 00:08:54.804 and we're going to turn that bag of chips into a microphone 00:08:54.804 --> 00:08:56.395 by filming it with a video camera 00:08:56.395 --> 00:08:59.623 and analyzing the tiny motions that sound waves create in it. 00:08:59.623 --> 00:09:02.419 So here's the sound that we played in the room. NOTE Paragraph 00:09:02.419 --> 00:09:06.853 (Music: "Mary Had a Little Lamb") NOTE Paragraph 00:09:10.007 --> 00:09:13.032 And this is a high-speed video we recorded of that bag of chips. 00:09:13.032 --> 00:09:14.306 Again it's playing. 00:09:14.306 --> 00:09:17.648 There's no chance you'll be able to see anything going on in that video 00:09:17.648 --> 00:09:18.706 just by looking at it, 00:09:18.706 --> 00:09:21.690 but here's the sound we were able to recover just by analyzing 00:09:21.690 --> 00:09:23.873 the tiny motions in that video. NOTE Paragraph 00:09:23.873 --> 00:09:26.682 (Music: "Mary Had a Little Lamb") NOTE Paragraph 00:09:40.985 --> 00:09:42.471 I call it -- Thank you. 00:09:42.471 --> 00:09:47.696 (Applause) 00:09:49.878 --> 00:09:52.223 I call it the visual microphone. 00:09:52.223 --> 00:09:55.613 We actually extract audio signals from video signals. 00:09:55.613 --> 00:09:58.794 And just to give you a sense of the scale of the motions here, 00:09:58.799 --> 00:10:04.135 a pretty loud sound will cause that bag of chips to move less than a micrometer. 00:10:04.135 --> 00:10:06.874 That's one thousandth of a millimeter. 00:10:06.874 --> 00:10:10.435 That's how tiny the motions are that we are now able to pull out 00:10:10.435 --> 00:10:13.678 just by observing how light bounces off objects 00:10:13.678 --> 00:10:15.814 and gets recorded by our cameras. NOTE Paragraph 00:10:15.814 --> 00:10:19.064 We can recover sounds from other objects, like plants. NOTE Paragraph 00:10:19.064 --> 00:10:25.380 (Music: "Mary Had a Little Lamb") NOTE Paragraph 00:10:27.214 --> 00:10:29.211 And we can recover speech as well. 00:10:29.211 --> 00:10:31.788 So here's a person speaking in a room. NOTE Paragraph 00:10:31.788 --> 00:10:35.991 Voice: Mary had a little lamb whose fleece was white as snow, 00:10:35.991 --> 00:10:40.221 and everywhere that Mary went, that lamb was sure to go. NOTE Paragraph 00:10:40.221 --> 00:10:42.980 Michael Rubinstein: And here's that speech again recovered 00:10:42.980 --> 00:10:46.254 just from this video of that same bag of chips. NOTE Paragraph 00:10:46.254 --> 00:10:51.085 Voice: Mary had a little lamb whose fleece was white as snow, 00:10:51.085 --> 00:10:55.944 and everywhere that Mary went, that lamb was sure to go. NOTE Paragraph 00:10:55.944 --> 00:10:58.290 MR: We used "Mary Had a Little Lamb" 00:10:58.290 --> 00:11:00.413 because those are said to be the first words 00:11:00.413 --> 00:11:04.574 that Thomas Edison spoke into his phonograph in 1877. 00:11:04.574 --> 00:11:07.802 It was one of the first sound recording devices in history. 00:11:07.802 --> 00:11:11.129 It basically directed the sounds onto a diaphragm 00:11:11.129 --> 00:11:15.208 that vibrated a needle that essentially engraved the sound on tinfoil 00:11:15.208 --> 00:11:17.483 that was wrapped around the cylinder. NOTE Paragraph 00:11:17.483 --> 00:11:23.426 Here's a demonstration of recording and replaying sound with Edison's phonograph. NOTE Paragraph 00:11:23.426 --> 00:11:26.446 (Video) Voice: Testing, testing, one two three. 00:11:26.446 --> 00:11:29.859 Mary had a little lamb whose fleece was white as snow, 00:11:29.859 --> 00:11:33.528 and everywhere that Mary went, the lamb was sure to go. 00:11:33.528 --> 00:11:36.268 Testing, testing, one two three. 00:11:36.268 --> 00:11:40.424 Mary had a little lamb whose fleece was white as snow, 00:11:40.424 --> 00:11:45.648 and everywhere that Mary went, the lamb was sure to go. NOTE Paragraph 00:11:45.648 --> 00:11:49.665 MR: And now, 137 years later, 00:11:49.665 --> 00:11:53.752 we're able to get sound in pretty much similar quality 00:11:53.752 --> 00:11:57.831 but by just watching objects vibrate to sound with cameras, 00:11:57.831 --> 00:11:59.765 and we can even do that when the camera 00:11:59.765 --> 00:12:03.999 is 15 feet away from the object, behind soundproof glass. NOTE Paragraph 00:12:03.999 --> 00:12:07.219 So this is the sound that we were able to recover in that case. NOTE Paragraph 00:12:07.219 --> 00:12:12.513 Voice: Mary had a little lamb whose fleece was white as snow, 00:12:12.513 --> 00:12:17.272 and everywhere that Mary went, the lamb was sure to go. NOTE Paragraph 00:12:17.404 --> 00:12:21.034 MR: And of course, surveillance is the first application that comes to mind. 00:12:21.034 --> 00:12:24.029 (Laughter) 00:12:24.029 --> 00:12:28.085 But it might actually be useful for other things as well. 00:12:28.085 --> 00:12:30.925 Maybe in the future, we'll be able to use it, for example, 00:12:30.925 --> 00:12:33.177 to recover sound across space, 00:12:33.177 --> 00:12:36.753 because sound can't travel in space, but light can. NOTE Paragraph 00:12:36.753 --> 00:12:39.157 We've only just begun exploring 00:12:39.157 --> 00:12:42.176 other possible uses for this new technology. 00:12:42.176 --> 00:12:45.008 It lets us see physical processes that we know are there 00:12:45.008 --> 00:12:48.564 but that we've never been able to see with our own eyes until now. NOTE Paragraph 00:12:48.564 --> 00:12:49.768 This is our team. 00:12:49.768 --> 00:12:52.647 Everything I showed you today is a result of a collaboration 00:12:52.647 --> 00:12:54.838 with this great group of people you see here, 00:12:54.838 --> 00:12:58.005 and I encourage you and welcome you to check out our website, 00:12:58.005 --> 00:12:59.451 try it out yourself, 00:12:59.451 --> 00:13:02.423 and join us in exploring this world of tiny motions. NOTE Paragraph 00:13:02.423 --> 00:13:04.048 Thank you. NOTE Paragraph 00:13:04.048 --> 00:13:05.302 (Applause)