The Technology And Applications On Banknote Image Analysis And Understanding

Banknote image analysis is of vital importance to the currency supervision. In this thesis, the muti-spectrum images of banknote are systematically investigated for the first time. The algorithms of banknote image analysis are first studied including banknote classification, feature detection/verification (defect feature or anti-counterfeit feature), and quality evaluation, based on which, the system of banknote image processing is then achieved and applied to different enverinments.In banknote classification, a hierarchical recognition strategy is proposed. First, the banknotes are pre-classified by the Grid-GMM Method, and then the Haar-Adaboost classifiers are used for the identification between the similar banknotes. During the pre-calssification, Grid Features are adopted, and a Gaussian mixture model (GMM) based on structural risk minimization (SRM) is engaged to build a more robust classifer. During the similar banknote identification, a kind of Haar-like feature is proposed, which is then refined by the Adaboost method to gain high performance on the classification of the similar images. The exeperimental result reveals that this hierachical method leads to a high capacity for low quality banknote processing and greatly decreases the false reject rate.In banknote feature detection/verification, three algorithms based on image registration/match are proposed. In the first edge-based algorithm, an area-based image registration algorithm is adopted to overlay the sensed and reference banknote images, in which an Edge Intensity Differential (EID) of the two images is constructed from the edge information extracted by the Kirsch operator. The Defect Feature extracted by EID is sensitive to the odd edge-information, and is robust to the global intensity change, which makes it suit for the attrited banknote. In the second algorithm, the homogeneity feature of banknote are introduced to locate the pixels that probably been blurred, the image registration algorithm based on the homogeneity feature is subsequently used to overlay the sensed and reference paper currency image. At last, each probably polluted pixel on the sensed image is compared with its corresponding pixel on the reference image to estimate the deterioration level. In the third algorithm, the homogeneity feature is extended to the chromatic space, which gains a more accurate result on banknote quality evaluation.In banknote deterioroation analysis, the characteristics of banknotes are discussed in detail for the first time. Based on it, a deformation and deterioration (D&D) model of banknote are proposed, in which the banknote deformation is interpreted by a cubic B-spline based Free Form Deformation (FFD) grids and the banknote deterioration is described by a Color Diffusion Model(CDM). In the CDM, banknote deterioration is regarded as a joint effect of General Attrition and Local Defect, and accordingly, the color shift from the sensed image to the reference image can be decomposed into the Attrition Rate and Defect Distance and evaluated separately. Based on the D&D model, two banknote evaluation algorithms are proposed. In the first one, a Banknote Deterioration Energy (BDE) is proposed as the data-driven term in FFD-based image registration, by which the quantitative analysis of banknote deterioration achieved. In the second one, for each kind of banknote image, the static reference image is extended to a dynamic image sequence that changed according to the deterioration degree. Then, the quality evaluation of the sensed image can be interpreted as locating its optimal position in the corresponding sequence. In this three-dimension FFD model, the deterioration degree and deformation state of one banknote can be concerned simultaneity, which will improve the banknote analysis performance greatly.In the banknote analysis system, a high speed Muti-spectrum Contact Image Sensor (CIS) is first introduced, by which one banknote can be alternately sampled under the red, green, and infrared lights, and then a"FPGA+DSP+PC"framework for the banknote analysis system is proposed. In the hardware configuration, the peripherals of the DSP and the logic setting of the FPGA are discussed, including trigger control, exposure contral, line sampling, and the I/O ports setting. In the software design, the image sampling flow are first discussed, and then a sensor compensation algorithm is proposed to equalize the sensitive error of each sampling unit in CIS, and at last, the image analysis scheme is discussed. In the image analysis scheme, the real-time basic function is implemented inside the DSP, such as banknote classification, defect/anti- counterfeit feature detection and quality evaluation, and the other extended function can be implemented in PC. Using this framework, two banknote analysis systems are constructed. The first one is designed for the CF3000 banknote sorter, in which several image analysis modules are associated to achieve a full analysis of the banknotes. The second one is designed for WJD_TKTH07 anti-counterfeit banknote counter, in which several functions are integrated into one image analysis module. The experinmental result reveals that this framework is flexible enough to be applied to different conditions.