I call the posts to this blog my notes, which I thought at the time I called them that might absolve me from all criticism from incompleteness, error, relevance, etc. I felt like that was the best thing to do, given my situation. They're my notes, which are there for other people to read, but are not written for other people. If I were blogging about my yacht or fashion, I would have held myself to the regular blogger standard; but, I'm under attack, and I'm injured, so lowering expectations seemed prudent.
The problem is that when I post something while I'm working on it, the already-low standard gets attributed to plain ol' sloppiness. Or, worse: craziness. From just notes, to scribbles and doodles, too. That's the risk I ran with these two recents posts, which are far from finished, but I think warranted early publication:
Still, I wanted to put this information out now, because it's science applied to a problem (i.e., the demon plague) with far too much myth surrounding it. Anything that interacts with us in our world has a physical component to it—whether classified as spirit or not. If you can see it, it emits physical light; if you can hear it, physical sound. And, if it touches you, believe me, it can be touched back, the particulars notwithstanding. That means it's relevant to keep an eye on the problem however you can That, by the way, is what this post is about: detecting the presence of demons via a specific pattern to the standard deviation measurements among multiple subsets of frequencies of images made during periods of high demonic activity.
This pattern can be detected mathematically, which means detection can be automated programmatically using common household electronics (e.g., an iPhone) and free software (e.g., GIMP). An app that takes time-lapse images, processes them and measures the standard deviation with the command-line version of GIMP, and then returns the results based on the pattern I establish here is now doable—and easily, too. A GIMP script that automates detection and indicates whether the image shows demonic activity, is forthcoming.
In the meantime, this post describes the criteria for identifying images made during periods of high demonic activity, and provides instructions for evaluating that criteria via advanced image filtering techniques using GIMP.
Experienced digital photographers of demons can easily identify pics and videos made during periods of high demonic activity by the color noise invariably caused by chroma—a form of EMF radiation always present during those periodic, intermediary spans of time in which demons are the most dangerous and harmful. But, while it may look like most other kinds of color noise, it has several distinct characteristics that differentiate it.
In photos, the difference can be seen in the density and size of the red, green (and yellow) and blue snow, which seems to lay on or cling to or wrap around certain surfaces (versus laying on top of the image like a blanket as other noise does). On video, the difference is most noticeable; chroma exhibits observable, expected (or predictable) behavior around certain objects and subjects and in certain circumstances [see "Zap" emits EMF radiation visible to digital camera], indicating that the color noise is not due to low-light conditions or poor-quality electronics, but is actually due to the way the camera translates the imprint made by chroma—a thing of substance and reality—into image data via its sensor. The fact that the presence of chroma allows for otherwise invisible demons to be seen in digital images [see Sucker demons swarm eyes, face to blind, disfigure] also distinguishes it from other forms of color noise, which never enhances anything, but, rather, mars.
The following still frames were taken from three different videos made during periods of high demonic activity, and, consequently, show the inevitable chroma that accompanies them:
These are the characteristics of chroma, as seen in each of the images above, that distinguish it from regular color noise:
Easier than distinguishing between chroma and color noise
... (consider using all interior samples)...
By contrast, digital video made when demonic activity is relatively lower do not show any noise...:
A third-party sample of a digital image showing demonic activity...:
Evaluating images for demonic activity using GIMP
In all images showing high demonic activity (and, in none of the images showing low demonic activity), the standard deviation (as measured in the histogram palette) leveled off at .001 at wavelet scale 6 or greater—never less; in images showing low demonic activity, wavelet scale 5 or less—never greater. The following images show the wavelet scale at which each image measured a standard deviation of 0.001:
Wavelet scale - Standard deviation 0.001 or 0.002*
The problem is that when I post something while I'm working on it, the already-low standard gets attributed to plain ol' sloppiness. Or, worse: craziness. From just notes, to scribbles and doodles, too. That's the risk I ran with these two recents posts, which are far from finished, but I think warranted early publication:
- Automating mapping of demons hidden in digital media
- Enhancing demon faces made anywhere, at anytime, from anything
Still, I wanted to put this information out now, because it's science applied to a problem (i.e., the demon plague) with far too much myth surrounding it. Anything that interacts with us in our world has a physical component to it—whether classified as spirit or not. If you can see it, it emits physical light; if you can hear it, physical sound. And, if it touches you, believe me, it can be touched back, the particulars notwithstanding. That means it's relevant to keep an eye on the problem however you can That, by the way, is what this post is about: detecting the presence of demons via a specific pattern to the standard deviation measurements among multiple subsets of frequencies of images made during periods of high demonic activity.
This pattern can be detected mathematically, which means detection can be automated programmatically using common household electronics (e.g., an iPhone) and free software (e.g., GIMP). An app that takes time-lapse images, processes them and measures the standard deviation with the command-line version of GIMP, and then returns the results based on the pattern I establish here is now doable—and easily, too. A GIMP script that automates detection and indicates whether the image shows demonic activity, is forthcoming.
In the meantime, this post describes the criteria for identifying images made during periods of high demonic activity, and provides instructions for evaluating that criteria via advanced image filtering techniques using GIMP.
Experienced digital photographers of demons can easily identify pics and videos made during periods of high demonic activity by the color noise invariably caused by chroma—a form of EMF radiation always present during those periodic, intermediary spans of time in which demons are the most dangerous and harmful. But, while it may look like most other kinds of color noise, it has several distinct characteristics that differentiate it.
In photos, the difference can be seen in the density and size of the red, green (and yellow) and blue snow, which seems to lay on or cling to or wrap around certain surfaces (versus laying on top of the image like a blanket as other noise does). On video, the difference is most noticeable; chroma exhibits observable, expected (or predictable) behavior around certain objects and subjects and in certain circumstances [see "Zap" emits EMF radiation visible to digital camera], indicating that the color noise is not due to low-light conditions or poor-quality electronics, but is actually due to the way the camera translates the imprint made by chroma—a thing of substance and reality—into image data via its sensor. The fact that the presence of chroma allows for otherwise invisible demons to be seen in digital images [see Sucker demons swarm eyes, face to blind, disfigure] also distinguishes it from other forms of color noise, which never enhances anything, but, rather, mars.
The following still frames were taken from three different videos made during periods of high demonic activity, and, consequently, show the inevitable chroma that accompanies them:
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| 1 | Image showing high demonic activity | 2 | Image showing high demonic activity |
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| 3 | Image showing high demonic activity | |
- In image #1, chroma appears to cling to the surface of my head as it makes visible the in-the-ether injuries from demonic weapons (the black spots or holes) and the sucker demon-variety demonic entities that look like smudges on my cheek and neck;
- In image #2, chroma illuminates the sucker demons, standing upright on the top of my head and arm of my jacket, as well as the white-glowing demonic entity that sometimes lodges (and shines) in the whites of my eyes to impair my vision;
- In image #3, chroma illuminates the skin-coloring of one of my possessing demons [see also Comparing demonic activity in light and shadow], clinging to the parts of my body like the color of the parts not exhibiting characteristics of possession [see also Human hands and forearms disappear, reappear as demonic].
Easier than distinguishing between chroma and color noise
... (consider using all interior samples)...
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| Low-demonic-activity image #1 (lighted interior sample) | Low-demonic-activity image #2 (nighttime/dark interior sample) |
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| Low-demonic-activity image #3 (daylight sample) | |
A third-party sample of a digital image showing demonic activity...:
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| Whether third-party digital images such as this one were made during a period of high demonic activity can now be verified |
- Open image in GIMP. (Notes about pasting an image into a new image file)
- Run the FFT Forward filter (Filters -- > Generic --> FFT Forward).
- Run the Wavelet decompose filter (Filters --> Generic --> Wavelet Decompose...), scaling the image by 9 layers.
- Select the Wavelet scale 1 layer.
- Open the Histogram, which you will use to read the standard deviation of each wavelet scale.
- Arrow-down each Wavelet scale layer, keeping an eye on the standard deviation.
- Make a note of the Wavelet scale layer in which the standard deviation is 0.001; the number of the layer determines whether the image was made during a period of high demonic activity.
In all images showing high demonic activity (and, in none of the images showing low demonic activity), the standard deviation (as measured in the histogram palette) leveled off at .001 at wavelet scale 6 or greater—never less; in images showing low demonic activity, wavelet scale 5 or less—never greater. The following images show the wavelet scale at which each image measured a standard deviation of 0.001:
Wavelet scale - Standard deviation 0.001 or 0.002*
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| High Demonic Activity Sample Image #1 - Wavelet scale 6 (7 pre-adjustment) |
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| High Demonic Activity Sample Image #2 - Wavelet scale 6 (adjustment not applicable) |
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| High Demonic Activity Sample Image #3 - Wavelet scale 7 (adjustment not applicable) |
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| Low Demonic Activity Sample Image #1 - Wavelet scale 5 (6 pre-adjustment) |
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| Low Demonic Activity Sample Image #2 - Wavelet scale 3 |
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| Low Demonic Activity Sample Image #3 - Wavelet scale 5 |
Applying the wavelet-scale 6 adjustment
An adjustment must be applied to any image measuring 0.001 standard deviation at wavelet scale 6 to prevent false positives, that is, images showing low demonic activity that register as showing high demonic activity. The difference between a wavelet-scale 6 low-activity image and a wavelet-scale 6 high-activity image is in the slope of the standard-deviation curve: in low-activity images, the curve drops steeply, and then levels off slowly—for at least three wavelet scales—measuring between 0.002-0.003 and 0.001. Choosing the median value (as statistics indicates in such cases), i.e., wavelet scale 5 or less, will accurately classify the image as showing low demonic activity.
To distinguish the rare low-demonic-activity image that falls on the border between high and low, choose the median between the first wavelet scale measuring 0.001 and two wavelet scales prior.
the standard deviation for three successive layers is 0.002, 0.002 and 0.001, choose the wavelet scale after the first measurement of 0.002 and before 0.001.
Performing this second check on every image that measure a standard deviation at wavelet scale 6 should not result false negatives for images showing high demonic activity, even if the standard deviation curve levels off slowly, etc.
To verify this, two images showing high demonic activity were chosen where the wavelet scale was 7 and 6, respectively; wavelet scale 7 to show that the adjustment will not raise the wavelet scale above 6, and 6 to show that the curve in such images does not the three-wavelet-scale level-off if the standard deviation reaches 0.001 at or prior to wavelet scale 6.
In High Demonic Activity Sample Image #1, wavelet scale 5 and 6 both measure a standard deviation of 0.002 and wavelet scale 7 measures 0.001, therefore, wavelet scale 6 was chosen, just to demonstrate that, even when the adjustment is applied to images showing high demonic activity, the wavelet scale will never equal less than 6.
In the samples above, an image showing low demonic activity (Low Demonic Activity Sample Image #1) measured a standard deviation of 0.001 at wavelet scale 6, falsely indicating that it was made during a period of high demonic activity. This is likely caused by the sunlight from window, and is why demon-detectors should be installed indoors, away from windows.
The difference, then, can be determined by the standard deviation curve, drops steeply and then levels off slowly, at least for three wavelet scales, measuring between 0.002 and 0.001. To distinguish the rare low-demonic-activity image that falls on the border between high and low, choose the median between the first wavelet scale measuring 0.001 and two wavelet scales prior.
Applying the three-wavelet-scale level-off adjustment to the falsely-categorized image (Low Demonic Activity Sample Image #1), wavelet scale 6 was indicated, in that wavelet scale 4 and 5 both measure a standard deviation of 0.002 and wavelet scale 6 measures 0.001.
Performing this second check on every image that measure a standard deviation at wavelet scale 6 should not result false negatives for images showing high demonic activity, even if the standard deviation curve levels off slowly, etc.
To verify this, two images showing high demonic activity were chosen where the wavelet scale was 7 and 6, respectively; wavelet scale 7 to show that the adjustment will not raise the wavelet scale above 6, and 6 to show that the curve in such images does not the three-wavelet-scale level-off if the standard deviation reaches 0.001 at or prior to wavelet scale 6.
In High Demonic Activity Sample Image #1, wavelet scale 5 and 6 both measure a standard deviation of 0.002 and wavelet scale 7 measures 0.001, therefore, wavelet scale 6 was chosen, just to demonstrate that, even when the adjustment is applied to images showing high demonic activity, the wavelet scale will never equal less than 6.
NOTE | But, even if it did, in most scenarios, more than one still frame will be used for analysis, and it is not likely that all still frames showing high demonic activity will be false-negatives.An application that took time-lapsed images, ran command-line GIMP on each image, specifically, the Fourier filter and then the Wavelet Decompose filter, and then measured the standard deviation via the histogram...could detect the onset of high demonic activity.
