Image segmentation is a highly scene dependent and problem dependent decision making or pattern recognition process. Knowledge about the class of imagess to be processed and the tasks to be performed, plays an important role. Two approaches that explicitly incorporate such knowledge are advanced for the class of images containing polygonal shapes. They can be generalized to other shapes by change of pre-processing steps. Inference is both data driven and goal driven. It is guided by meta rules that are fired by the outputs of preprocessing. Effective suppression of noise is achieved. The methods illustrate the potential of AI techniques and tools for low-level image understanding tasks.