A real-time image-processing-system-on-chip for security feature detection and classification

Nonlinear spatial transforms and fuzzy pattern classification with unimodal potential functions are already established in signal processing. They have proved to be excellent tools in feature extraction and classification. We propose an inspection method for pattern recognition and classification of two dimensional translation variant security elements such as stripes, kinegrams and others, which are widely used as applications in bank note printing. The system is based on discrete non linear translation invariant circular transforms and fuzzy pattern classification. Nonlinear discrete circular transforms are adaptable transforms, which can be optimized for different application tasks, such as translation variant object analysis and position location. They are mainly used as generators for feature vectors. Even though, the feature vector is theoretically translation invariant, the object movement creates a translation tolerant feature vector, because in real systems and applications many problems can occur, such as signal and optical distortions. Therefore, the features should be further analysed by a fuzzy pattern classifier. Implementation of the transforms and fuzzy pattern classifier in radix-2-structures is possible, allowing fast calculations with a computational complexity of O(N) up to O(N1d(N)). Furthermore, the algorithms can be implemented in one Field Programmable Gate Array (FPGA), which operates with 40 MHz clock rate.