Abstract

As clustering techniques are gaining more important today, we propose a new clustering technique by means of ACFO and cellular automata. The cellular automata uniquely characterizes the condition of a cell at a specific moment by employing the data like the conditions of a reference cell together with its adjoining cell, total number of cells, restraint, transition function and neighbourhood calculation. With an eye on explaining the condition of the cell, morphological functions are executed on the image. In accordance with the four stages of the morphological process, the rural and the urban areas are grouped separately. In order to steer clear of the stochastic turbulences, the threshold is optimized by means of the ACFO. The test outcomes obtained vouchsafe superb performance of the innovative technique. The accomplishment of the new-fangled technique is assessed by using additional number of images and is contrasted with the traditional methods like CFO (Central Force Optimization) and PSO (Particle Swarm Optimization).

Keywords:

ACFO (Adaptive Central Force Optimization), cellular automata, convolution, correlation, morphological operation,

References

Competing interests

The authors have no competing interests.

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Copyright

The authors have no competing interests.