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Graph Matching for Road Network Retrieval

Graph Matching for Road Network Retrieval. Avik BHATTACHARYA. Image Database at ARIANA. ENST/CNES SPOT5 images SupCom, TUNISIA images Images selected with few global scenarios. Few examples of selected images. Continued …. Queries. Queries with relevance to these databases for

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Graph Matching for Road Network Retrieval

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  1. Graph Matching for Road Network Retrieval Avik BHATTACHARYA

  2. Image Database at ARIANA • ENST/CNES SPOT5 images • SupCom, TUNISIA images • Images selected with few global scenarios

  3. Few examples of selected images

  4. Continued …

  5. Queries • Queries with relevance to these databases for • IKONA System • KIM System

  6. Scenarios • Global scenarios selected at ARIANA • Urban/Semi urban/Urban or Semi urban with complex junction/Urban with perpendicular junctions • Rural areas • Airport • Mountain roads

  7. Scenarios from IGN • A meeting with Sylvain Airault at IGN • Pre-knowledge information from cartographer, i.e., importance of the road, actual width of the road or junction • Physical bounds of road structures, i.e., for crossroads and circular junctions

  8. Ideas of graph matching • Probabilistic arguments towards graph matching. • Could reduce the complexity of the matching problem • Metric based clique comparison • Hamming distance, Levenshtein distance, Hausdroff distance, etc.

  9. A survey of road extraction methods • Linear filtering • Mathematical morphology • Variational methods • Markov fields • Neural networks • Dynamic programming • Multiresolution analysis

  10. 2 Methods Used For Road Extraction in ARIANA, INRIA, Sophia • Variational Method (By Marie Rochery, present Phd Student at INRIA, Sophia). • Stochastic Method (By Caroline Lacoste, past Phd Student at INRIA, Sophia).

  11. 2 Extraction Methods Results

  12. Detector Characteristics • Algorithms with specific goals • Input data, e.g., intensity, edges, lines • Resolution • External knowledge, e.g., GIS, geographical database • Context, e.g., rural/urban roads, linear elements • Output, e.g., pixels, attributes, polygons, segments

  13. The Approach • The node and edge attributes could comprises of : • Spectral properties, e.g., surface characteristics • Geometric properties, e.g., steepness, width, curvature • Topological properties, e.g., road links, networks • Contextual properties, e.g., max width, max curvature

  14. The Approach • Starting from the extracted image (Rochery’s work, Variational method) construct the shock graph. • Shocks Vs Skeleton • “The Shocks form along the reaction axis reduces to traditional skeleton when information regarding type, group, and salience is discarded”

  15. The Approach • The present difficulties • Attempts to locate shocks at grid points suffers from discretization artifacts. • Equivalent graph representation of the road network for both the extraction methods.

  16. Results of graph construction

  17. Results of graph construction

  18. Future Work • Node and Edge attributes • Geometrical attributes ! • Junction angles and resulting histogram and network density • The graph construction from Lacoste’s work (Stochastic method)

  19. Thank You for your patience !

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