Michael Lee Posted October 7, 2021 Share Posted October 7, 2021 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. 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. 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. 1 Quote Link to comment Share on other sites More sharing options...
Join the conversation
You can post now and register later. If you have an account, sign in now to post with your account.