Approaches to synthesizing image processing programs

Machine learning techniques are examined as a means of automatically generating image processing programs. Nonstructured techniques such as discovery systems and evolutionary processes are studied because they facilitate the exploration of enormous search spaces without a detailed knowledge base. The success of these methods depends on the algorithm representation and the effectiveness of performance evaluation. Mathematical morphology provides an algebraic representation which is powerful and challenging to program. The qualitative aspects of effective performance measures are also discussed