### The NULL space of reason

This image has an additional dimension add to the analysis and is rotated slighly to show that in perspective. The separation is more clear, but it resolves more easily than this to objects which can be identified by their character to be of a specific "nature". Fur or tree bark or other types of textures isolate themselves to texture spaces and once combined with the dimensional space they become separate objects in an image. In addition the conversion to a triangle based "triangle_strip" using the shading dimension projects a 3D structure which is the .obj file with a texture on the vertices and a normal to the surface. It should not surprise you at all that the data is a continuous spectrum as that is what it is. It is a representation of continuous color and as such is easily differentiable from other types of things which tend to cluster.

These images represent one aspect of dimensional extraction of images or anything actually that has properties. So, anything. Though this is a single aspect of the image it tells quite a bit. When it is combined with other dimensions it solves just like a linear equation in a matrix. When there are enough measurements to limit the dimensions it resolves to a solution. if x+y=10 and x+2y=22 then you have a solution. So it is with this. The more data dimensions that are added the greater the resolving power. It isn't an integer, but is an object space. In this image, it was created in InkScape and as such uses a very limited "random spread" of colors and it is very obvious that this image was computer generated just from the close spacing of the color vectors.

This is only one dimension of the analysis and even so it strips into parts so easily that any person could see that the elements separate to types. In this case it is like a matrix equation of two variables as I have only two major types of things in the image. An equation of two variables is solved with two equality relationships. Here the green section is associated with the sea floor and the yellow is the organism. So even though it is quantitatively imprecise , the two object "spaces" are easily identifiable as two distinct objects. In cases where the images are more of a composite it becomes an equation of more variables and requires more dimensions to "solve". In fact some images do not solve at all with this and in fact I look at some images on the Internet and the same is true, I say "What the hell is that." and some times I never know what it is and that is fine because not everything has meaning. In the image the shape and position of those curves tell me other things about the image, but that is incidental as isolating the two types of "things" is all that is necessary in this context.

In this image the decomposition is not as clear. However when the other dimensions are applied it resolves easily. This does make it clear that this method functions as easily as a matrix product and though it uses some unique methods it does resolve in exactly the same way when enough dimensions are used. These things are very much like matrix math without the obsession for perfection. There may never be a perfect answer. The image is more peoplish than fishy or catty and so I assume person and I could be wrong as it could be a cat dressed up as a fish dressed up as a person and so I am wrong about and so most of the time I am right and that is it, I play the odds to my favor and that is all I can do in a world of infinites upon infinites. The three elements in the graph are the white mark, the shaded green background and the peoplish mountain looking thing which includes hair eyes and skin and without more dimensions it is a thing like a set of frequencies that are merged to be speech, they don't look separate until you do Fourier analysis. I personally can see the two major underlying forms that are merged and the two are similar to the curves above. At first I did not see them, but after displaying them as they separate out with other dimensions it is an obvious merger of two major chords and one minor.