Abstract
A new image segmenting method with multi-agent structure is proposed to cope with the image variability and the intrinsic complexity of segmenting tasks. This method includes agents of nine different functions, namely, control, multi-valued segmentation, region splitting, region merging, marking, regional feature calculation, spatial relationship analysis, and confidence calculation. With the exception of carrying on communication in the form of instructions and answers, the communication and coordination of work between agents are performed through the reception and operation of various (original and processing-generated) data on the blackboard. The final segmentation results jointly obtained by the multiple agents take segmented subregion confidence as a component for characterizing the region feature vector. The object identification algorithm will make use of the domain knowledge of objects and features with confidence component for detection and identification of the segmentation results. The flexibility, adaptability and robustness of this method have been confirmed by experimental results.
