BIBLE | The Study Notes of a Demoniac (Matt. 3:10-12; Heb. 10:16, 19, 22-25)

Spreading the gospel is a way to prove to God, man and yourself how committed you are to Christ, in that doing so carries life-threatening risks.

— From Delivering the Gospel is Deadly Work, one of my Bible study notes, on Acts 20:24

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As you can see, I've been studying my Bible and taking notes—I'd be a fool not to:

Although I don't take notes as frequently as I'd like or used to, the ones I do take are quite comprehensive

It's just that I haven't been publishing them to the blog that may make one think otherwise. This post, though, starts anew my once-per-Sunday posting commitment, and should put to rest any concern that my eyes have wondered from the prize.

The title of this note portends to a topic that has seen very little quality treatment. The people who write such titles and talk on such a topic rarely have Jesus at work in their lives. They haven't really done much in life, haven't lived, haven't struggled—like life-and-death struggle—with sin or the consequences therefrom (either from you or someone else).

It's a topic best broached by people who are struggling, people who pray the prayer of Jonah, people being carried across the sand, and people who are being helped by people who help because of Christ.  Anybody can draw a picture of a house, but living in one is much better. Those kinds of people make the best audience, too. I can draw you a map to somewhere, but you won't need unless you're going there.

The topic of your salvation and Christ's role therein needs to be hard-hitting, which can only happen if it applies. There is no room for guessing or estimating or false relating to issue related to your salvation, and your hoped for future citizenship in the Kingdom of Heaven. You have to question why anyone not in your shoes—or someone who has never been in your shoes—would have anything to say about you or your salvation. It's fishy, and I have to think about what the Bible says about fishy: "You will know them by their works." [Matthew 7:16, 20]

So, moving on, then, to notes that will be shown and told to you by and through the mind of Christ and the Spirit of God in ways that only they can, if you're blessed.

Top 3 Signs Jesus is at Work in Your Life:

1. You are feeling the full weight of sin. And now also the axe is laid unto the root of the trees: therefore every tree which bringeth not forth good fruit is hewn down, and cast into the fire.
2. You are acutely aware of your shortcomings, how far you fall short of the standard. I indeed baptize you with water unto repentance: but he that cometh after me is mightier than I, whose shoes I am not worthy to bear:
3. Circumstances are compelling you to change. He shall baptize you with the Holy Ghost, and with fire: Whose fan is in his hand, and he will thoroughly purge his floor, and gather his wheat into the garner; but he will burn up the chaff with unquenchable fire.

— (‭Matthew‬ ‭3‬:‭10-12‬ KJV)

Top 3 Signs of His Success:

1. Every word, thought and action is infused by the Spirit of God and the mind of Christ, blessing each step you take. This is the covenant that I will make with them after those days, saith the Lord, I will put my laws into their hearts, and in their minds will I write them;
2. You feel confidence from a source other than yourself as you move forward in life, and feel unencumbered by your past, and can rightly attribute these things to the love of God. Having therefore, brethren, boldness to enter into the holiest by the blood of Jesus, Let us draw near with a true heart in full assurance of faith, having our hearts sprinkled from an evil conscience, and our bodies washed with pure water. Let us hold fast the profession of our faith without wavering; (for he is faithful that promised;)
3. You are a worthy and acceptable candidate for membership to a community of like-minded persons, who can aid you in your journey along the path of righteousness. And let us consider one another to provoke unto love and to good works: Not forsaking the assembling of ourselves together, as the manner of some is; but exhorting one another: and so much the more, as ye see the day approaching.

— (‭Hebrews‬ ‭10‬:‭16, 19, 22-25‬ KJV)

Minutiae detection solutions needed for small entities' attacks

A recent video showing a flurry of sucker demons invading my body underscores the need for in-home demonic activity detection equipment that is configured to find small entities the size of those little, flying white strands of hell-fury. At least for me it does; no one else seems to be as concerned.

Floating in from the top-left...

...is a sucker demon...

...captured with unusual clarity

But, I don't think that's a lack of diligence, ignorance, complicity, culpability, surrender, stupidity, laziness, irresponsibility, stubbornness, cowardice or fear—or any of the other things I could think of that it looks like—on anyone's part. I think people just can't see them.

This video clip supports that assessment perfectly, in that I think it shows that sucker demons prefer to attack from behind and, of course, benefit from being really small and quiet and slow:



Experimenting with a multi-frequency band-pass filter of my own design, which divides an image into two user-specified frequencies, enhances (either sharpens and/or contrasts) and then combines them with the original again. This is a first step in the development of surface analysis filters similar to that already used by medical imaging devices, that step being isolating the actual surface prior to enhance its details:



The purpose is to put in the hands of non-medical professionals (i.e., the rest of us) a tool for finding cloaked and small things on surfaces that should not be there and/or are harmful. A perfect illustration of such things would be, of course, shown in the first video, and by these still frames taken from the second one:

The first step in finding small entities on surfaces...

...is by isolating the frequencies in the image showing them...

...and then enhancing those frequencies (not shown here)

As you can surmise from the filter testing video, surfaces are identified by first selecting the frequencies of the image containing the surfaces. That requires making adjustments on the part of the user, which requires ease-of-use considerations on the part of the designer (hence the testing). After that, various enhancements can be applied to sharpen and further isolate them, a step not shown in the video or still frames above.

That step will be hard to narrow down, not to mention refine. To give you an idea, here are a few of those options:

• Edge/Line detection and edge linking. Kirsch, pyramid, Sobel, Prewitt, Roberts, Laplacian, Frei-Chen, Hough transform.
• Image segmentation. Fuzzyc mean, histogram thresholding, median-cut, principal components transform/median cut, spherical coordinate transform/center split, gray level quantization, split and merge.
• Morphological filters. Binary iterative morphology, gray-scale and color erosion, dilation, opening, and closing.
• Two-dimensional fast transforms. Fourier (FFT), cosine (DCT), Haar, Walsh, Hadamard, wavelet transforms.
• Frequency domain filters. Highpass, lowpass, bandpass, bandreject, high frequency emphasis, and notch.
• Feature extraction. Binary, RST-invariant, histogram, spectral and texture object features.

TECHNOLOGY | Gaussian + Laplacian, standard deviation high-pass filters = decloaked tissue-eroding entities

A band-pass filter prototype that processes digital media using two, successive low-pass image-processing filters (comprised of Gaussian and Laplacian transforms) revealed a lot more from a video made during an attack than any other video showing such activity—that is, by the way, demonic entities eating my face [see DRAFT | TECHNOLOGY | Demonic maggots erode face, neck (iPhone Photo Editing Extension); see also PICS | Torture by sucker demon].

Although a procedure to decloak and enhance sucker-demon attacks was posted to this blog just a few months ago [see TECHNOLOGY | Finding sucker demon-attacks in digital media], this newer, experimental version uses the same filters common to medical imaging devices that perform surface analysis, yielding far better results.

The same applies to a second, in-development imaging filter, particularly, one based on pixel variance and standard deviation statistics. Following are sample still frames from a video made this afternoon using that filter, captioned with points of interest:

Left temple, top of head, right cheek (various entities)

Right shoulder, left cheek (various entities)

Left temple, top of head, right cheek (various entities)

Eye sockets, chin, jaw bone, nose

Left eye socket (protuding white spikes)

Left jawbone (either entity or device)

Of particular note is the still frame showing either an entity or a device. The body suggests the former (it looks like a lady bug); the perfectly straight legs, the latter (they look like prongs):

If this is a device of some kind, the image is the second of its kind showing one

Something similar can be seen in another image, which is posted SOFTWARE | Chroma-focusing and chroma-mapping Photo Booth effects released, and which I then postulated was an electronics piece of some kind:

I identified a similar looking object shown in an older image as electronics, even though, in the video, it walked up my face; most likely, because of its metal-colored/textured outer shell

Post-processing a video made tonight using a dual-combination of high-pass filters, specifically, Gaussian and Laplacian, yielded great results, too. The following is a screen recording showing the effects of various parameter settings as the video plays:



Here's the RMS filter kernel code (OpenGL ES 3.0) I used:

/*
A Core Image kernel routine that computes a Laplacian effect.
The code looks up the source pixel in the sampler and then subtracts a new pixel based on a Laplace transform.
*/

kernel vec4 coreImageKernel(sampler image, sampler median, float poles)
{
vec4 pixel = unpremultiply(sample(image, samplerCoord(image)));
vec4 mixel = unpremultiply(sample(median, samplerCoord(median)));
pixel.rgb = sqrt(vec3(1.0) / vec3(1.0) * pow(pixel.rgb, vec3(2.0 * poles)));
pixel.rgb -= mixel.rgb;
return premultiply(pixel);
}

Here is the image-processing pipeline in Quartz Composer:

The image-processing pipeline, inside a macro with a single image input and output and published inputs for the Gaussian blur radius amount and the Laplacian poles quantity (right: radius and pole adjustment controls)

Here's the variance/standard deviation filter kernel code used in a customized iPhone camera app that I use to test Core Image filters:

/*

The Core Image Filter version will have the following sliders:

Divisor [-1 to 1, default 0]: Subtract or add to the mean divisor for enhanced detail

Mix [0 to 1, default 0]: Mix between standard deviation with original pixel values

The filter will come in different sizes: 3 x 3, 5 x 5, 7 x 7, and 9 x 9

*/

kernel vec4 coreImageKernel(sampler u, float divisor, float amount)

{

    vec4 pixel = unpremultiply(sample(u, samplerCoord(u)));

    vec2 xy = destCoord();

    vec4 mean  = vec4(0.0);

    vec4 mean2 = vec4(0.0);

    for (int i = -3; i <= 3; i++)

    {

        for (int j = -3; j <= 3; j++ )

        {

            mean  += sample(u, samplerTransform(u, xy+vec2(i, j)));

            mean2 += sample(u, samplerTransform(u, xy+vec2(i, j))) * sample(u, samplerTransform(u, xy+vec2(i, j)));

        }

    }

    float size = 49.0 + divisor;

    mean  /= size;

    mean2 /= size;

    vec4 variance = vec4(sqrt(mean2 - (mean * mean))); // sqrt(pow(pixel - mean, vec4(2.0)));

    vec3 sd       = vec3(mix(variance.rgb, variance.rgb / pixel.rgb, amount));

    return premultiply(normalize(vec4(mix(sd, sd / pixel.rgb, amount), pixel.a)));

}

After the image is processing by the kernel, the result is then subtracted from the original.

Other uses: Revealing your inner demon using the Gaussian + Laplacian high-pass filter
Because possessing demons also emit EMF radiation that causes chroma in digital images, any noise-isolating/enhancing filter, such as the Gaussian + Laplacian high-pass filter, will enhance images of them; like in the past, the camera and/or subject must be in motion for the demon to be seen.

Following are still frames from a third video made today, which were processed by the aforestated filter:

The demon possessing me was the first post to explore this kind of imaging in-depth; search this blog for demon possession for more.

Future development plans

A high-pass filter is one of four basic frequency domain filters, which isolate, sharpen and/or otherwise filter and enhance a specific frequency of an image—in this case, the frequency containing the noise. Generally, the only type of frequency domain filter used specifically for noise is the low-pass filter, which is primarily used to eliminate noise from an image by smoothing a specific image frequency while leaving the remainder intact (unsmoothed).

NOTE | Astute readers may have noted that the Gaussian blur is a low-pass filter; however, what makes it a component of a high-pass filter like the one demonstrated in this post is its subtraction from the original (unblurred) image.

The plan is to include all four types, along with any relevant and necessary variants, with an decloaking iPhone camera app, now in-development.

TECHNOLOGY | Isolating chroma using variance and standard deviation statistics

Coming up
By calculating the distribution of pixels and their variance from the mean (or average), and then displaying only pixels with a wide variance, unusual elements in an image, e.g., demonic entities and related activity, can be isolated or enhanced:

From left, clockwise: Median-calculated standard deviation, adjustable Gaussian blur-standard deviation, 1 x 1 nearest neighbor-averaged standard deviation, global (mean) standard deviation

Check back soon for code that calculates the variance and standard deviation statistics for an image in OpenGL ES 3.0 and OpenCV:

The code and image-processing pipeline for the Gaussian-blur (local adaptive) standard deviation calculation

The post will also present methods for further refinement of images generated by variance and standard deviation statistics:

Other image-processing procedures can be applied to enhance the results of images created using variance/standard deviation statistics