Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Three-Dimensional Shape Analysis Using Moments and Fourier Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Geometric sensing of known planar shapes
International Journal of Robotics Research
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Orthogonal Moment Features for Use With Parametric and Non-Parametric Classifiers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Closed-Loop Object Recognition Using Reinforcement Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robot Vision
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Stand-Alone Vision Sensor Design Based on Fuzzy Associative Database
CRV '04 Proceedings of the 1st Canadian Conference on Computer and Robot Vision
A neural-network appearance-based 3-D object recognition using independent component analysis
IEEE Transactions on Neural Networks
Rotation-invariant neural pattern recognition system with application to coin recognition
IEEE Transactions on Neural Networks
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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.