WEBVTT 00:00:15.088 --> 00:00:21.142 So over the past few centuries, microscopes have revolutionized our world. 00:00:23.204 --> 00:00:27.563 They revealed to us a tiny world of objects, life and structures 00:00:27.563 --> 00:00:30.567 that are too small for us to see with our naked eyes. 00:00:30.567 --> 00:00:34.064 They are a tremendous contribution to science and technology. 00:00:34.064 --> 00:00:37.839 Today I'd like to introduce you to a new type of microscope, 00:00:37.839 --> 00:00:40.230 a microscope for changes. 00:00:40.230 --> 00:00:43.438 It doesn't use optics like a regular microscope 00:00:43.438 --> 00:00:45.350 to make small objects bigger, 00:00:45.350 --> 00:00:49.673 but instead it uses a video camera and image processing 00:00:49.673 --> 00:00:54.387 to reveal to us the tiniest motions and color changes in objects and people, 00:00:54.526 --> 00:00:58.368 changes that are impossible for us to see with our naked eyes. 00:00:58.906 --> 00:01:02.970 And it lets us look at our world in a completely new way. 00:01:03.295 --> 00:01:05.715 So what do I mean by color changes? 00:01:07.093 --> 00:01:09.896 Our skin, for example, changes its color very slightly 00:01:09.896 --> 00:01:12.211 when the blood flows under it. 00:01:12.211 --> 00:01:14.443 That change is incredibly subtle, 00:01:14.443 --> 00:01:16.872 which is why, when you look at other people, 00:01:16.872 --> 00:01:19.171 when you look at the person sitting next to you, 00:01:19.171 --> 00:01:21.920 you don't see their skin or their face changing color. 00:01:21.920 --> 00:01:27.150 When we look at this video of Steve here, it appears to us like a static picture, 00:01:27.849 --> 00:01:31.292 but once we look at this video through our new, special microscope, 00:01:31.292 --> 00:01:34.550 suddenly we see a completely different image. 00:01:35.276 --> 00:01:39.200 What you see here are small changes in the color of Steve's skin, 00:01:39.200 --> 00:01:43.087 magnified 100 times so that they become visible. 00:01:43.539 --> 00:01:46.259 We can actually see a human pulse. 00:01:46.554 --> 00:01:49.729 We can see how fast Steve's heart is beating, 00:01:49.729 --> 00:01:53.994 but we can also see the actual way that the blood flows in his face. 00:01:55.152 --> 00:01:58.055 And we can do that not just to visualize the pulse, 00:01:58.055 --> 00:02:01.448 but also to actually recover our heart rates, 00:02:01.448 --> 00:02:03.510 and measure our heart rates. 00:02:03.510 --> 00:02:07.692 And we can do it with regular cameras and without touching the patients. 00:02:07.692 --> 00:02:12.768 So here you see the pulse and heart rate we extracted from a neonatal baby 00:02:12.768 --> 00:02:16.250 from a video we took with a regular DSLR camera, 00:02:16.250 --> 00:02:18.364 and the heart rate measurement we get 00:02:18.364 --> 00:02:22.588 is as accurate as the one you'd get with a standard monitor in a hospital. 00:02:23.384 --> 00:02:26.457 And it doesn't even have to be a video we recorded. 00:02:26.457 --> 00:02:29.391 We can do it essentially with other videos as well. 00:02:29.391 --> 00:02:32.565 So I just took a short clip from "Batman Begins" here 00:02:32.565 --> 00:02:35.226 just to show Christian Bale's pulse. 00:02:35.226 --> 00:02:37.282 (Laughter) 00:02:37.282 --> 00:02:39.417 And you know, presumably he's wearing makeup, 00:02:39.417 --> 00:02:41.389 the lighting here is kind of challenging, 00:02:41.389 --> 00:02:44.392 but still, just from the video, we're able to extract his pulse 00:02:44.392 --> 00:02:46.224 and show it quite well. 00:02:46.224 --> 00:02:47.995 So how do we do all that? 00:02:47.995 --> 00:02:52.315 We basically analyze the changes in the light that are recorded 00:02:52.315 --> 00:02:54.928 at every pixel in the video over time, 00:02:54.928 --> 00:02:56.648 and then we crank up those changes. 00:02:56.648 --> 00:02:59.494 We make them bigger so that we can see them. 00:02:59.494 --> 00:03:01.576 The tricky part is that those signals, 00:03:01.576 --> 00:03:04.359 those changes that we're after, are extremely subtle, 00:03:04.359 --> 00:03:07.353 so we have to be very careful when you try to separate them 00:03:07.353 --> 00:03:10.240 from noise that always exists in videos. 00:03:10.240 --> 00:03:13.682 So we use some clever image processing techniques 00:03:13.682 --> 00:03:17.736 to get a very accurate measurement of the color at each pixel in the video, 00:03:17.736 --> 00:03:20.569 and then the way the color changes over time, 00:03:20.569 --> 00:03:23.227 and then we amplify those changes. 00:03:23.227 --> 00:03:27.081 We make them bigger to create those types of enhanced videos, or magnified videos, 00:03:27.081 --> 00:03:29.552 that actually show us those changes. 00:03:32.007 --> 00:03:36.227 But it turns out we can do that not just to show tiny changes in color, 00:03:36.227 --> 00:03:38.379 but also tiny motions, 00:03:38.379 --> 00:03:42.064 and that's because the light that gets recorded in our cameras 00:03:42.064 --> 00:03:45.189 will change not only if the color of the object changes, 00:03:45.189 --> 00:03:47.305 but also if the object moves. 00:03:47.905 --> 00:03:52.543 So this is my daughter when she was about two months old. 00:03:56.157 --> 00:03:59.397 It's a video I recorded about three years ago. 00:03:59.397 --> 00:04:02.807 And as new parents, we all want to make sure our babies are healthy, 00:04:02.807 --> 00:04:05.373 that they're breathing, that they're alive, of course. 00:04:05.373 --> 00:04:07.465 So I too got one of those baby monitors 00:04:07.465 --> 00:04:10.072 so that I could see my daughter when she was asleep. 00:04:10.072 --> 00:04:13.590 And this is pretty much what you'll see with a standard baby monitor. 00:04:13.590 --> 00:04:15.686 You can see the baby's sleeping, 00:04:15.686 --> 00:04:17.729 but there's not too much information there. 00:04:17.729 --> 00:04:19.516 There's not too much we can see. 00:04:19.516 --> 00:04:22.358 Wouldn't it be better, or more informative, or more useful, 00:04:22.358 --> 00:04:25.261 if instead we could look at the view like this. 00:04:25.261 --> 00:04:30.310 So here I took the motions and I magnified them 30 times, 00:04:31.217 --> 00:04:33.708 and then I could clearly see that my daughter 00:04:33.708 --> 00:04:35.428 was indeed alive and breathing. 00:04:35.428 --> 00:04:37.565 (Laughter) 00:04:38.092 --> 00:04:39.891 Here is a side-by-side comparison. 00:04:39.891 --> 00:04:42.440 So again, in the source video, in the original video, 00:04:42.440 --> 00:04:44.260 there's not too much we can see, 00:04:44.260 --> 00:04:48.212 but once we magnify the motions, the breathing becomes much more visible. 00:04:48.212 --> 00:04:50.801 And it turns out, there's a lot of phenomena 00:04:50.801 --> 00:04:54.474 we can reveal and magnify with our new motion microscope. 00:04:54.474 --> 00:04:58.909 We can see how our veins and arteries are pulsing in our bodies. 00:04:59.752 --> 00:05:02.560 We can see that our eyes are constantly moving 00:05:02.560 --> 00:05:04.776 in this wobbly motion. 00:05:04.776 --> 00:05:06.354 And that's actually my eye, 00:05:06.354 --> 00:05:09.414 and again this video was taken right after my daughter was born, 00:05:09.414 --> 00:05:13.103 so you can see I wasn't getting too much sleep. (Laughter) 00:05:13.539 --> 00:05:16.403 Even when a person is sitting still, 00:05:16.403 --> 00:05:18.997 there's a lot of information we can extract 00:05:18.997 --> 00:05:21.989 about their breathing patterns, small facial expressions. 00:05:22.672 --> 00:05:24.623 Maybe we could use those motions 00:05:24.623 --> 00:05:27.488 to tell us something about our thoughts or our emotions. 00:05:29.003 --> 00:05:32.385 We can also magnify small mechanical movements, 00:05:32.385 --> 00:05:34.337 like vibrations in engines, 00:05:34.337 --> 00:05:38.017 that can help engineers detect and diagnose machinery problems, 00:05:40.130 --> 00:05:45.547 or see how our buildings and structures sway in the wind and react to forces. 00:05:45.547 --> 00:05:50.333 Those are all things that our society knows how to measure in various ways, 00:05:50.333 --> 00:05:52.875 but measuring those motions is one thing, 00:05:52.875 --> 00:05:55.479 and actually seeing those motions as they happen 00:05:55.479 --> 00:05:57.614 is a whole different thing. 00:05:58.450 --> 00:06:02.021 And ever since we discovered this new technology, 00:06:02.021 --> 00:06:03.723 we made our code available online 00:06:03.723 --> 00:06:06.269 so that others could use and experiment with it. 00:06:08.005 --> 00:06:09.809 It's very simple to use. 00:06:09.809 --> 00:06:11.853 It can work on your own videos. 00:06:11.853 --> 00:06:15.357 Our collaborators at Quantum Research even created this nice website 00:06:15.357 --> 00:06:18.036 where you can upload your videos and process them online, 00:06:18.036 --> 00:06:21.603 so even if you don't have any experience in computer science or programming, 00:06:21.603 --> 00:06:24.509 you can still very easily experiment with this new microscope. 00:06:24.509 --> 00:06:26.941 And I'd like to show you just a couple of examples 00:06:26.941 --> 00:06:28.919 of what others have done with it. 00:06:32.363 --> 00:06:37.259 So this video was made by a YouTube user called Tamez85. 00:06:37.259 --> 00:06:38.807 I don't know who that user is, 00:06:38.807 --> 00:06:41.105 but he, or she, used our code 00:06:41.105 --> 00:06:43.910 to magnify small belly movements during pregnancy. 00:06:44.933 --> 00:06:46.420 It's kind of creepy. 00:06:46.420 --> 00:06:48.818 (Laughter) 00:06:48.818 --> 00:06:52.782 People have used it to magnify pulsing veins in their hands. 00:06:53.532 --> 00:06:56.699 And you know it's not real science unless you use guinea pigs, 00:06:58.037 --> 00:07:00.584 and apparently this guinea pig is called Tiffany, 00:07:00.584 --> 00:07:04.007 and this YouTube user claims it is the first rodent on Earth 00:07:04.007 --> 00:07:05.780 that was motion-magnified. 00:07:06.604 --> 00:07:08.811 You can also do some art with it. 00:07:08.811 --> 00:07:12.123 So this video was sent to me by a design student at Yale. 00:07:12.123 --> 00:07:14.516 She wanted to see if there's any difference 00:07:14.516 --> 00:07:16.072 in the way her classmates move. 00:07:16.072 --> 00:07:20.361 She made them all stand still, and then magnified their motions. 00:07:20.361 --> 00:07:23.457 It's like seeing still pictures come to life. 00:07:23.714 --> 00:07:26.077 And the nice thing with all those examples 00:07:26.077 --> 00:07:28.315 is that we had nothing to do with them. 00:07:28.315 --> 00:07:32.165 We just provided this new tool, a new way to look at the world, 00:07:32.165 --> 00:07:36.683 and then people find other interesting, new and creative ways of using it. 00:07:37.735 --> 00:07:39.620 But we didn't stop there. 00:07:40.943 --> 00:07:44.597 This tool not only allows us to look at the world in a new way, 00:07:44.597 --> 00:07:47.034 it also redefines what we can do 00:07:47.034 --> 00:07:50.232 and pushes the limits of what we can do with our cameras. 00:07:50.232 --> 00:07:52.611 So as scientists, we started wondering, 00:07:52.611 --> 00:07:56.299 what other types of physical phenomena produce tiny motions 00:07:56.299 --> 00:07:59.212 that we could now use our cameras to measure? 00:07:59.212 --> 00:08:02.635 And one such phenomenon that we focused on recently is sound. 00:08:03.664 --> 00:08:05.963 Sound, as we all know, is basically changes 00:08:05.963 --> 00:08:08.134 in air pressure that travel through the air. 00:08:08.134 --> 00:08:11.857 Those pressure waves hit objects and they create small vibrations in them, 00:08:11.857 --> 00:08:14.519 which is how we hear and how we record sound. 00:08:14.519 --> 00:08:18.294 But it turns out that sound also produces visual motions. 00:08:18.579 --> 00:08:21.303 Those are motions that are not visible to us 00:08:21.303 --> 00:08:24.229 but are visible to a camera with the right processing. 00:08:24.229 --> 00:08:26.045 So here are two examples. 00:08:26.045 --> 00:08:29.374 This is me demonstrating my great singing skills. 00:08:30.845 --> 00:08:33.603 (Singing) 00:08:33.603 --> 00:08:34.710 (Laughter) 00:08:34.710 --> 00:08:37.706 And I took a high-speed video of my throat while I was humming. 00:08:37.706 --> 00:08:39.355 Again, if you stare at that video, 00:08:39.355 --> 00:08:41.387 there's not too much you'll be able to see, 00:08:41.387 --> 00:08:45.793 but once we magnify the motions 100 times, we can see all the motions and ripples 00:08:45.793 --> 00:08:49.103 in the neck that are involved in producing the sound. 00:08:49.103 --> 00:08:51.528 That signal is there in that video. 00:08:51.528 --> 00:08:54.228 We also know that singers can break a wine glass 00:08:54.228 --> 00:08:56.274 if they hit the correct note. 00:08:56.274 --> 00:08:58.325 So here, we're going to play a note 00:08:58.325 --> 00:09:00.849 that's in the resonance frequency of that glass 00:09:00.849 --> 00:09:03.125 through a loudspeaker that's next to it. 00:09:03.125 --> 00:09:07.568 Once we play that note and magnify the motions 250 times, 00:09:07.568 --> 00:09:10.789 we can very clearly see how the glass vibrates 00:09:10.789 --> 00:09:13.623 and resonates in response to the sound. 00:09:14.132 --> 00:09:16.545 It's not something you're used to seeing every day. 00:09:16.545 --> 00:09:19.408 And we actually have the demo right outside set up, 00:09:19.408 --> 00:09:21.300 so I encourage you to stop by, 00:09:21.300 --> 00:09:24.347 and just play with it yourself, you can actually see it live. 00:09:24.608 --> 00:09:27.768 But this made us think. It gave us this crazy idea. 00:09:28.078 --> 00:09:32.865 Can we actually invert this process and recover sound from video 00:09:33.454 --> 00:09:37.581 by analyzing the tiny vibrations that sound waves create in objects, 00:09:37.581 --> 00:09:41.898 and essentially convert those back into the sounds that produced them. 00:09:42.548 --> 00:09:46.472 In this way, we can turn everyday objects into microphones. 00:09:47.958 --> 00:09:49.595 So that's exactly what we did. 00:09:49.595 --> 00:09:52.462 So here's an empty bag of chips that was lying on a table, 00:09:52.462 --> 00:09:55.234 and we're going to turn that bag of chips into a microphone 00:09:55.234 --> 00:09:57.145 by filming it with a video camera 00:09:57.145 --> 00:10:00.914 and analyzing the tiny motions that sound waves create in it. 00:10:01.479 --> 00:10:04.242 So here's the sound that we played in the room. 00:10:04.242 --> 00:10:07.634 (Music: "Mary Had a Little Lamb") 00:10:12.476 --> 00:10:15.426 And this is a high-speed video we recorded of that bag of chips. 00:10:15.426 --> 00:10:16.528 Again it's playing. 00:10:16.528 --> 00:10:19.886 There's no chance you'll be able to see anything going on in that video 00:10:19.886 --> 00:10:21.000 just by looking at it, 00:10:21.000 --> 00:10:23.962 but here's the sound we were able to recover just by analyzing 00:10:23.962 --> 00:10:26.273 the tiny motions in that video. 00:10:27.127 --> 00:10:30.494 (Music: "Mary Had a Little Lamb") 00:10:44.607 --> 00:10:46.458 I call it -- Thank you. 00:10:46.458 --> 00:10:49.328 (Applause) 00:10:53.834 --> 00:10:56.140 I call it the visual microphone. 00:10:56.140 --> 00:10:59.251 We actually extract audio signals from video signals. 00:10:59.251 --> 00:11:02.435 And just to give you a sense of the scale of the motions here, 00:11:02.435 --> 00:11:06.696 a pretty loud sound will cause that bag of chips 00:11:06.696 --> 00:11:09.266 to move less than a micrometer. 00:11:09.807 --> 00:11:12.485 That's one thousandth of a millimeter. 00:11:12.485 --> 00:11:16.179 That's how tiny the motions are that we are now able to pull out 00:11:16.179 --> 00:11:19.282 just by observing how light bounces off objects 00:11:19.282 --> 00:11:21.704 and gets recorded by our cameras. 00:11:22.208 --> 00:11:25.358 We can recover sounds from other objects, like plants. 00:11:25.986 --> 00:11:29.183 (Music: "Mary Had a Little Lamb") 00:11:34.153 --> 00:11:36.456 And we can recover speech as well. 00:11:36.456 --> 00:11:38.817 So here's a person speaking in a room. 00:11:38.817 --> 00:11:43.632 Voice: Mary had a little lamb whose fleece was white as snow, 00:11:43.632 --> 00:11:47.570 and everywhere that Mary went, that lamb was sure to go. 00:11:48.722 --> 00:11:51.486 Michael Rubinstein: And here's that speech again recovered 00:11:51.486 --> 00:11:54.220 just from this video of that same bag of chips. 00:11:54.220 --> 00:11:59.211 Voice: Mary had a little lamb whose fleece was white as snow, 00:11:59.211 --> 00:12:03.731 and everywhere that Mary went, that lamb was sure to go. 00:12:04.352 --> 00:12:06.907 MR: We used "Mary Had a Little Lamb" 00:12:06.907 --> 00:12:09.252 because those are said to be the first words 00:12:09.252 --> 00:12:13.053 that Thomas Edison spoke into his phonograph in 1877. 00:12:13.053 --> 00:12:16.565 It was one of the first sound recording devices in history. 00:12:16.565 --> 00:12:19.842 It basically directed the sounds onto a diaphragm 00:12:19.842 --> 00:12:24.270 that vibrated a needle that essentially engraved the sound on tinfoil 00:12:24.270 --> 00:12:26.565 that was wrapped around the cylinder. 00:12:26.565 --> 00:12:29.679 Here's a demonstration of recording 00:12:29.679 --> 00:12:32.446 and replaying sound with Edison's phonograph. 00:12:33.549 --> 00:12:36.487 (Video) Voice: Testing, testing, one two three. 00:12:36.487 --> 00:12:39.654 Mary had a little lamb whose fleece was white as snow, 00:12:39.654 --> 00:12:43.491 and everywhere that Mary went, the lamb was sure to go. 00:12:43.491 --> 00:12:46.014 Testing, testing, one two three. 00:12:46.014 --> 00:12:50.103 Mary had a little lamb whose fleece was white as snow, 00:12:50.103 --> 00:12:54.235 and everywhere that Mary went, the lamb was sure to go. 00:12:55.719 --> 00:12:59.081 MR: And now, 137 years later, 00:13:00.334 --> 00:13:03.492 we're able to get sound in pretty much similar quality 00:13:03.492 --> 00:13:07.559 but by just watching objects vibrate to sound with cameras, 00:13:07.853 --> 00:13:09.952 and we can even do that when the camera 00:13:09.952 --> 00:13:13.631 is 15 feet away from the object, behind soundproof glass. 00:13:14.178 --> 00:13:17.475 So this is the sound that we were able to recover in that case. 00:13:17.475 --> 00:13:22.282 Voice: Mary had a little lamb whose fleece was white as snow, 00:13:22.282 --> 00:13:26.993 and everywhere that Mary went, the lamb was sure to go. 00:13:28.111 --> 00:13:31.711 MR: And of course, surveillance is the first application that comes to mind. 00:13:31.711 --> 00:13:33.993 (Laughter) 00:13:33.993 --> 00:13:38.055 But it might actually be useful for other things as well. 00:13:38.095 --> 00:13:41.196 Maybe in the future, we'll be able to use it, for example, 00:13:41.196 --> 00:13:43.557 to recover sound across space, 00:13:43.557 --> 00:13:46.569 because sound can't travel in space, but light can. 00:13:47.166 --> 00:13:49.525 We've only just begun exploring 00:13:49.525 --> 00:13:52.509 other possible uses for this new technology. 00:13:52.509 --> 00:13:55.288 It lets us see physical processes that we know are there 00:13:55.288 --> 00:13:59.575 but that we've never been able to see with our own eyes until now. 00:14:00.677 --> 00:14:01.917 This is our team. 00:14:01.917 --> 00:14:04.767 Everything I showed you today is a result of a collaboration 00:14:04.767 --> 00:14:06.864 with this great group of people you see here, 00:14:06.864 --> 00:14:10.484 and I encourage you and welcome you to check out our website, 00:14:10.484 --> 00:14:12.017 try it out yourself, 00:14:12.017 --> 00:14:15.263 and join us in exploring this world of tiny motions. 00:14:15.263 --> 00:14:16.700 Thank you. 00:14:16.700 --> 00:14:18.726 (Applause)