A parallel image processing language based on computational models

The paper concerns computational models and languages for iterative cellular automata for image processing applications. The authors present some formal models, with different computational resources, that can be useful in the solution of certain algorithmic problems. They introduce some concepts such as memory splitting, conditional functions, dynamic neighborhood and supervisor automaton. The models defined lead to a parallel language structure that can express low-level image processing algorithms in a clear and concise way. The language allows a transparent description of the algorithms and can be easily expandable to reflect the needs of people working in different branches of image processing