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Spiricam - Combining camera input and a touch of image processing


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Building on my work with Perloids derived from the noise of software-defined radios, I decided to explore looking at the noise coming from cameras. In principle, camera provide high-bandwidth information and spirits could potentially either interact directly with individual pixel-level sensor elements or with the light in the environment.

The two "tricks" then for getting things to work is to derive as much noise as possible from the camera, and then apply our favorite transformation that either blurs or "smokifies" the noise.

How do you get lots of noise from a camera? Well, one way would be turn up the gain and exposure time, and put the camera in complete darkness. Another more universal method is to simply subtract the current frame from the previous frame. This allows us to point the camera at virtually anything (or nothing at all) and get a noisy pattern.

Here's an example from a webcam. I've mathematically amplified the noise so you can see it. 

image.thumb.png.695c53272ebc16d0257c12f7407111f6.png

 

Now the next trick is to somehow make heads or tails of the noise. Blurring is one possibility, which could be done with something called Gaussian kernel convolution in image manipulation programs like GIMP and Photoshop. However, even better than that is "smokification." That's a word I just made up, but it's like how Perlin noise is created from regular white noise. White noise is fairly featureless and looks like indistinguishable sand, but Perlin noise often produces cloudy / smoky-like textures like the ones you see in Keith's Perlin image experiments.

Now, I've been trying out different variations on Perlin, like my so-called "Perloids" but the general range of useful ones seem to be between "inverse frequency" and "inverse squared frequency" What this means in laymans terms is that we try to transform noise to look as real as possible using essentially a two dimension "graphic equalizer" (you know, the audio version for controlling high, mid, and low frequencies?)

Now imagine amplifying the "bass frequencies" (or the largest features of an image) and keeping the mid-levels in the middle, and dampening out  the "treble/high frequencies" (the smallest features). This is the essence of Perlinizing / smokifying noise - and the results are amazing as we've seen with Perlin noise images, and continue to be with my webcam/noise input.

Incidentally, the original Varanormal France Perlin program uses random numbers generated with a deterministic algorithm, although a non-deterministic seed. How spirits figured out how send messages through that channel puzzles me to this day, but it seems to work. Here I'm using physically-generated noise (from a webcam), so at least it seems plausible that spirits could influence the electronics or the light in the air to send us information.

And now for some pictures. Caveat emptor: I can't prove that anything we see here is really from spirit, and not just active imagination. But I'm hoping over time, the proof will either get clearer, or we'll have too many instances to dismiss.

The first two are from a camera I got in the mail today, and they're a bit disappointing. But what you might see in black and white are four faces followed by one face. The next four small ones are faces. The final one on the 2nd row appears to have at least four faces. 

example1.png.thumb.jpg.78c0b512f16ee115904a9111aa94363f.jpg

example2.thumb.jpg.a84b10f54bf1761ddebfb5b0adb4463e.jpg

The last row is a little different. The first one is a whole body image. The second looks like a humanoid-alien face. The final one is an example of when I point the camera at myself and hold real still. Maybe we'll start being able to view the personalities of our soul?

One very last comment for this post. There's no machine learning tricks in the method (yet?!?), just "simple" math.

 

 

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  • Michael Lee changed the title to Spiricam - Combining camera input and a touch of image processing

In the next pictures, the first three are just me with the distortion of the method, the camera pointed at me.

The next one looks a little like me. #5, #6, and #7, however, showed up over my face and don't look anything like me.

The final one is just one I found elsewhere in the smoky pictures.

image.thumb.png.cf3b51f5b2460f2af582290fdfa4115f.png

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Interesting. So the idea is to provide the outline of a face as a base for, lets say information precipitation, that helps the spirits to do their manipulations at the right points but in the end forming another face that is not yours?

In that case wouldn't it be favorable to use a somehow "neutral" face like a smiley?

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Interesting pics Michael.  I would love to see the two bottom ones upside down.

The wants plan took a hit this week. I would buy myself a couple of misting machines and convert the camera to a tripod or some other stable stand. The plan remains a plan now, seriously taking a hi.  It stays put back another couple of weeks/month.

Inka ~ pussy cat, which I have now held more than I had the opportunity to keep my Em, had a super expensive operation day before yesterday. So once again, my playthings slide down the wants pile. The Needs pile has grown.

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5 hours ago, Andres Ramos said:

Interesting. So the idea is to provide the outline of a face as a base for, lets say information precipitation, that helps the spirits to do their manipulations at the right points but in the end forming another face that is not yours?

In that case wouldn't it be favorable to use a somehow "neutral" face like a smiley?

Spiricam is a general approach. It works just fine with a blank wall or in a closed box. Pointing at someone is a variation that might reveal something about their aura. To be on the safe side, one could put a sheet over their face. 👻

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5 hours ago, Karyn said:

Interesting pics Michael.  I would love to see the two bottom ones upside down.

The wants plan took a hit this week. I would buy myself a couple of misting machines and convert the camera to a tripod or some other stable stand. The plan remains a plan now, seriously taking a hi.  It stays put back another couple of weeks/month.

Inka ~ pussy cat, which I have now held more than I had the opportunity to keep my Em, had a super expensive operation day before yesterday. So once again, my playthings slide down the wants pile. The Needs pile has grown.

Karyn, the beauty of this new technique is it doesn't require mist, smoke, or mixing cleaning solutions 😲 (chemistry joke). We all have a webcam and a computer. I also envision a cellphone app. The software converts the digital noise and the light particles in a room into "smoke." It is like we can now see the invisible air itself.

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16 hours ago, Michael Lee said:

Building on my work with Perloids derived from the noise of software-defined radios, I decided to explore looking at the noise coming from cameras. In principle, camera provide high-bandwidth information and spirits could potentially either interact directly with individual pixel-level sensor elements or with the light in the environment.

The two "tricks" then for getting things to work is to derive as much noise as possible from the camera, and then apply our favorite transformation that either blurs or "smokifies" the noise.

How do you get lots of noise from a camera? Well, one way would be turn up the gain and exposure time, and put the camera in complete darkness. Another more universal method is to simply subtract the current frame from the previous frame. This allows us to point the camera at virtually anything (or nothing at all) and get a noisy pattern.

Here's an example from a webcam. I've mathematically amplified the noise so you can see it. 

image.thumb.png.695c53272ebc16d0257c12f7407111f6.png

 

Now the next trick is to somehow make heads or tails of the noise. Blurring is one possibility, which could be done with something called Gaussian kernel convolution in image manipulation programs like GIMP and Photoshop. However, even better than that is "smokification." That's a word I just made up, but it's like how Perlin noise is created from regular white noise. White noise is fairly featureless and looks like indistinguishable sand, but Perlin noise often produces cloudy / smoky-like textures like the ones you see in Keith's Perlin image experiments.

Now, I've been trying out different variations on Perlin, like my so-called "Perloids" but the general range of useful ones seem to be between "inverse frequency" and "inverse squared frequency" What this means in laymans terms is that we try to transform noise to look as real as possible using essentially a two dimension "graphic equalizer" (you know, the audio version for controlling high, mid, and low frequencies?)

Now imagine amplifying the "bass frequencies" (or the largest features of an image) and keeping the mid-levels in the middle, and dampening out  the "treble/high frequencies" (the smallest features). This is the essence of Perlinizing / smokifying noise - and the results are amazing as we've seen with Perlin noise images, and continue to be with my webcam/noise input.

Incidentally, the original Varanormal France Perlin program uses random numbers generated with a deterministic algorithm, although a non-deterministic seed. How spirits figured out how send messages through that channel puzzles me to this day, but it seems to work. Here I'm using physically-generated noise (from a webcam), so at least it seems plausible that spirits could influence the electronics or the light in the air to send us information.

And now for some pictures. Caveat emptor: I can't prove that anything we see here is really from spirit, and not just active imagination. But I'm hoping over time, the proof will either get clearer, or we'll have too many instances to dismiss.

The first two are from a camera I got in the mail today, and they're a bit disappointing. But what you might see in black and white are four faces followed by one face. The next four small ones are faces. The final one on the 2nd row appears to have at least four faces. 

example1.png.thumb.jpg.78c0b512f16ee115904a9111aa94363f.jpg

example2.thumb.jpg.a84b10f54bf1761ddebfb5b0adb4463e.jpg

The last row is a little different. The first one is a whole body image. The second looks like a humanoid-alien face. The final one is an example of when I point the camera at myself and hold real still. Maybe we'll start being able to view the personalities of our soul?

One very last comment for this post. There's no machine learning tricks in the method (yet?!?), just "simple" math.

 

 

I am fascinated by you description. The principle kf substracting image frames from each other is a cool idea!

What strikes me even more are your explanations about the perlinization of noise. Do I get you right if I see this process as a recombination of random distribution where the "lower frequencies" are dominating the higher ones? Let's say I have a random generator in software that picks numbers from 0 to 99 with equal probability. Then I would apply a transformation function in a way that e.g. the number 20 is picked with just 25% probability of the number 10 (inverse square rule). Is this correct?

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For random number sequences, the frequency means the relative differences between neighboring numbers. The sequence of numbers 3, 95, 26, 62 is bouncing around (high frequency). The seq. 25, 32, 28, 27 is less jumpy (low frequency).

Now imagine pixel neighborhoods. Blurring makes all the pixels almost the same in a neighborhood. But to get finer features, you need a balance: a little bit of blurring at each length scale: neigborhood, village, city, county, state, etc.

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11 hours ago, Michael Lee said:

For random number sequences, the frequency means the relative differences between neighboring numbers. The sequence of numbers 3, 95, 26, 62 is bouncing around (high frequency). The seq. 25, 32, 28, 27 is less jumpy (low frequency).

Now imagine pixel neighborhoods. Blurring makes all the pixels almost the same in a neighborhood. But to get finer features, you need a balance: a little bit of blurring at each length scale: neigborhood, village, city, county, state, etc.

Yes I see. Thanks for clarifying this for me!

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