Neutrosophic 3D-Image Processing

Florentin Smarandache

elements (100%) of the set X. So, they are classical Axioms.

We can define a Left NeutroWeakDistributivity and a Left Neutro(Strong)Distributivity {and correspondingly Left AntiWeakDistributivity and a Left Anti(Strong)Distributivity} for a HyperAlgebra...

The Left WeakDistributivity (1) is a Left NeutroStrongDistributivity if there is at least one equal sign ( = ) for some x, y, z {i.e. , , ,x y z X  x*(y#z) = (x*y)#(x*z)}.

If there is no equal (=) sign for no triplet x, y, z, {i.e. , , ,x y z X  x*(y#z) ≠ (x*y)#(x*z)} then the Left WeakDistributivity (1) is a Left AntiStrongDistributivity.

Neutrosophic 3D-Image Processing and Identification

Florentin Smarandache to Yanhui Guo

I wanted to ask you, or to propose you the following idea: now there is a 3D-scanning, so 3D-image, so 3Dimage processing and identification. Could you approach the neutrosophic 3D-image processing / identification? Did you also do colored image processing / identification?

For example you have 5 colors into an image, C1, C2, C3, C4, C5.

Each pixel (or element) is characterized by degrees of C1, C2, C3, C4, C5.

We may write:

x(d1, d2, d3, d4, d5),