| Summary: | 碩士 === 國立高雄第一科技大學 === 電腦與通訊工程所 === 93 === The Artificial Neural Network (ANN) compares younger with the other sciences. In spite of this, it is worth researching for research workers since our thirst for more clever machines. Therefore, the researchers in different fields such as biology, computer science, electrical engineering, psychology, mathematics, philosophy and mechanical engineering etc., are exploring the ANN in different points of view. For the moment, ANN is generally applying for the graph recognition, classification, and control. It is seldom applying for commercial product.
The foundation of theory in this thesis is that banknotes are made up of particular papers which are close confinement, and the printing inks and pigments used to print banknotes is a fixed ratio in native. Above-mentioned two key points aren't surmounted for false currency groups. Therefore, this thesis combines the two key points and ANN to develop a system suitable for false currency recognition.
This thesis will provide techniques such as Digital Image Processing and Plastic Perception Neural Network for implementation of false currency recognition system in commercial product. We minify the volume and simplify the operation by using the TMS320DSC25 development platform of the Texas Instruments .The input unit uses CMOS sensor for particular optics image retrieving and the output unit uses LCM monitor for displaying the result of the recognition.
This thesis involves Particular Optics Image Retrieving, Object Image Capturing, Noise Suppression and Eigenvalue of Object Image Capturing. Particular Optics Image Retrieving is contributive to retrieving special characteristics hiding behind the bank notes such as Metallic Thread，Watermark，Overptints etc.. On the other side, The general recognition of the bank note is introduced to the whole recognition, This system takes some characteristic segments separated from principal characteristic images of the bank note, and captures eigenvalue of the segments to recognize. The characteristic vectors combine White Run-Length and Pixel Density to display apart from the construction and macrocosm of Object in order to have high accuracy of recognition.
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