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A statistical and structural approach for symbol recognition, using XML modelling

A statistical and structural approach for symbol recognition, using XML modelling. Mathieu Delalandre, Pierre Héroux, Sébastien Adam, Eric Trupin, Jean-Marc Ogier. Introduction.

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A statistical and structural approach for symbol recognition, using XML modelling

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  1. A statistical and structural approach for symbol recognition, using XML modelling Mathieu Delalandre, Pierre Héroux, Sébastien Adam, Eric Trupin, Jean-Marc Ogier UFR Sciences et techniques université de Rouen 76130 Mont Saint Aignan

  2. Introduction • Description : System using a statistical and structural approach for technical symbol recognitionwithXML modelling of recognition results Utility map extract XML modelling of recognition results

  3. Treatment steps Loop extraction (1) Blob coloring filtering (2) Feature extraction (3) Statistical classification (4) XML modelling of recognition results Image processing Model reconstruction (5) Recognition processing Structural classification (6) XML Modelling

  4. (1) Loop extraction

  5. (2) Blob coloring filtering

  6. (3)&(4) Feature extraction & Statistical classification (4)Feature extraction *Fourrier-Mellin invariants *Zernike moments *Circular probes (5)Statistical classifier kppv

  7. (5)&(6) Model reconstruction& Structural classification (5)Model reconstruction Connection and/or distance contraints (6)Structural classifcation Similarity criterion based on common sub-graph

  8. Results • Statistical results • 50 loops for training and test set • 99% of loop recognition • Structural results • 9 plan extracts, ~100 loops, ~30 symbols • Without noise on statistical classification • 100% of loop recognition gives 100% of symbol recognition • With noise on statistical classification • 55.4% of loop recognition gives 86.86% of symbol recognition

  9. Perspectives (1) • Exploiting sub-graph isomorphism

  10. Perspectives (2) • Exploiting structural models

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