Cellular automata based algorithm for image density classification task

In this paper is presented a solution based on a bi-dimensional cellular automata (CA) for image density classification task (DCT). The two necessary properties: density preserving and translation are combined together to obtain the DCT solution. These two properties are achieved using a combination of nine fundamental 2D-CA rules and the proposed solution for DCT has two phases: preprocessing phase and decision phase. The project has been implemented in software using C# programming language and experimental results are presented for images with different sizes. This work is totally related to the idea of understanding the impact of the inherently local information processing of CA on their ability to perform a coordinated computation at the global level, as achieved by an evolutionary process.