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originally posted by: leolady
I had to go look up frame rates to educate myself on them and I found the following information:
30 fps is usually what a typical camcorder has
24 fps for motion picture recording like for movies, some camcorders have this too
60 fps are on some camcorders for faster frame rate (to capture fast movements, like sports / action)
120 fps or higher can record video in slow motion
***Their camera had 5602 frames per second (they show this in the video at the 3:04 mark). Probably a very expensive camera.***
I have come to the conclusion that is almost certainly either a troll post to get people worked up, or he/she simply isn't interested in (or perhaps capable of) understanding.
No matter, we have made a t attempt at denying ignorance, sometimes you need to know when to cut your losses and move on.
Researchers at MIT, Microsoft, and Adobe have developed an algorithm that can reconstruct an audio signal by analyzing minute vibrations of objects depicted in video. In one set of experiments, they were able to recover intelligible speech from the vibrations of a potato-chip bag photographed from 15 feet away through soundproof glass.
In other experiments, they extracted useful audio signals from videos of aluminum foil, the surface of a glass of water, and even the leaves of a potted plant. The researchers will present their findings in a paper at this year’s Siggraph, the premier computer graphics conference.
Some boundaries in an image are fuzzier than a single pixel in width, however. So the researchers borrowed a technique from earlier work on algorithms that amplify minuscule variations in video, making visible previously undetectable motions: the breathing of an infant in the neonatal ward of a hospital, or the pulse in a subject’s wrist.
That technique passes successive frames of video through a battery of image filters, which are used to measure fluctuations, such as the changing color values at boundaries, at several different orientations — say, horizontal, vertical, and diagonal — and several different scales.
The researchers developed an algorithm that combines the output of the filters to infer the motions of an object as a whole when it’s struck by sound waves. Different edges of the object may be moving in different directions, so the algorithm first aligns all the measurements so that they won’t cancel each other out. And it gives greater weight to measurements made at very distinct edges — clear boundaries between different color values.
Source Code?
I know what source code is, I was asking why do you need it. You haven't explained what you would be looking for in the source code that would show that you can wave a bag of chips around and it couldn't be captured in frame.
originally posted by: ZetaRediculian
a reply to: neoholographic
This your time to shine...
originally posted by: neoholographic
It's your claim that waving a bag of chips will disrupt this technology. I simply asked to show how waving a bag of chips can't be captured in frame. Again, you and evil bob made the silly claim.
“When sound hits an object, it causes the object to vibrate,” says Abe Davis, a graduate student in electrical engineering and computer science at MIT and first author on the new paper. “The motion of this vibration creates a very subtle visual signal that’s usually invisible to the naked eye. People didn’t realize that this information was there.”
Reconstructing audio from video requires that the frequency of the video samples — the number of frames of video captured per second — be higher than the frequency of the audio signal. In some of their experiments, the researchers used a high-speed camera that captured 2,000 to 6,000 frames per second. That’s much faster than the 60 frames per second possible with some smartphones, but well below the frame rates of the best commercial high-speed cameras, which can top 100,000 frames per second.
“We’re recovering sounds from objects,” he says. “That gives us a lot of information about the sound that’s going on around the object, but it also gives us a lot of information about the object itself, because different objects are going to respond to sound in different ways.” In ongoing work, the researchers have begun trying to determine material and structural properties of objects from their visible response to short bursts of sound.
The researchers developed an algorithm that combines the output of the filters to infer the motions of an object as a whole when it’s struck by sound waves. Different edges of the object may be moving in different directions, so the algorithm first aligns all the measurements so that they won’t cancel each other out. And it gives greater weight to measurements made at very distinct edges — clear boundaries between different color values.