Stand-alone embedded vision system based on fuzzy associative database

In this paper, a design methodology for a stand-alone embedded vision system (SEVS) is presented. The combination of region-based features and fuzzy theory defines the system, which is fast, flexible, and efficient. The proposed system can help to achieve flexible manufacturing goals and enhance safety. The advantages of the proposed system over traditional non-imaging sensors for manufacturing purposes include the recognition of the incoming product prior to determining its position, orientation, and speed. Region-based features – such as, Zernike moments, the first invariant function of central moments, and compactness – are utilized as pose descriptors. Moreover, we study the robustness of the pose descriptors and compare the fuzzy associative database (FAD) with maximum likelihood (ML) and a radial-basis function network to achieve multiple-pose detection. In addition, an ML estimation is employed to train the system automatically. It is demonstrated that the system can reliably recognize products with fairly complex shapes. When a product is successfully recognized, the system provides the essential information to a process controller or programmable logic controller for further action without requiring any particular interface. In the case of unrecognized objects, the system sends an appropriate message to the controller.