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Analyse de formes et de textures : application à l'authentification et à la gradation de pièces de monnaies

Abstract : Coins have been collected and studied since ancient times. Today, coin collection has become a hobby for anyone who wants to participate in. Coin grading is a way of determining the physical condition of a coin to provide an indicator for its market value. For grading coins on a large scale and in a relatively objective way, the GENI company cooperates with the laboratory LIRIS to automate the process by using coin photos.The main objective of this thesis is to grade coins from well-conditioned photos. The project is composed of four steps: coin segmentation from raw photos, monetary type identification, coin date detection and recognition and, coin grading.The first step is to extract coins from raw photos with a high precision. With a deformable geometric model, we segment precisely round coins, many-sided coins, wavy edged coins and holed coins by recognizing their shapes.The second step consists of coin recognition or monetary type identification. We match the query coin to the most identical type reference by using two similarity scores. The first similarity score is based on local features of relief contours. The second similarity score is a semi-global measure that highlights the difference between relief patterns.The third step is to detect and recognize coin dates. However, the fact that such characters have the same color as the background makes traditional optical character recognition methods difficult to apply. After extracting the date zone and cropping it into digit images, we propose a learning-free method to recognize those digits by analyzing their “topological” features.In the last step, the grading process is carried out by quantification of "unexpected elements" such as scratches and dirty marks. The coin to grade is registered to a reference coin. Then, large “unexpected elements” are detected in some regions of interest. However, some “micro-scratches” are difficult to extract individually but all together make a "grainy" surface. To deal with it, we use Deep Learning techniques to classify those grainy zones containing such “micro-scratches”. The result of our system, which is close to the manual expert one, is considered as a useful help for numismatists.
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Submitted on : Tuesday, September 24, 2019 - 12:47:10 PM
Last modification on : Wednesday, July 8, 2020 - 12:42:12 PM


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  • HAL Id : tel-02293022, version 2


Xingyu Pan. Analyse de formes et de textures : application à l'authentification et à la gradation de pièces de monnaies. Vision par ordinateur et reconnaissance de formes [cs.CV]. Université de Lyon, 2018. Français. ⟨NNT : 2018LYSE2030⟩. ⟨tel-02293022v2⟩



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