Object recognition is one of the key processes in Photogrammetry to generate maps from sensory information, because it is to convert 'data' to 'information.' However, as the size of input data is increased, it also has been one of the bottle neck processes in Photogrammetry. Thus many researchers have been working on developing semi-automated or automated object recognition methods to speed up the process. Some of the developed methods have been proved to be feasible in controlled environments, and others have been applicable for real world applications. However, most of the conventional object recognition methods still require human operators' interventions to correct errors or clarify ambiguous results.
The new object recognition method proposed in this dissertation is to recognize multiple types of objects in parallel so that the ambiguous results would be minimized. Since 1980's, new paradigms in Computer Science such as parallel computing and agent models have emerged with the progress of computer systems. The proposed method is built on one of the paradigms, the agent model. With built-in knowledge and predefined goals, the agent actively searches clues to reach the goals. In a multi-agent model, several agents with specific goals and tasks are deployed, and they are trying to reach the main goal together.
The proposed system consists of the coordinator agent, the recognition agents, and the data agent. The coordinator agent initiates other agents, and the data agent handles and processes input data. While the recognition agents aggressively collect regions for the target objects, sometimes conflicts arise between more than two recognition agents. With the proposed conflict resolution scheme, the conflicts can be resolved, and finally ambiguity can be removed.
Experiments on the proposed system were performed with a multi-spectral image and LIDAR data. Results of feature extraction done by the data agent, and object recognition are presented. The results show that the proposed method successfully recognized target objects(buildings and trees), and the multi-agent model enhances the accuracy of the results.