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Bridge Extraction based on Constrained Delaunay Triangulation

Bridge Extraction based on Constrained Delaunay Triangulation. Feng Gao Lei Hu Zhaofeng He. Bridge Extraction. Panchromatic Image: High reolution provides detailed descriptions. Challenge: Bridges are surrounded by complex backgrounds. Related Work. Textual and geometric information

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Bridge Extraction based on Constrained Delaunay Triangulation

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  1. Bridge Extraction based on Constrained Delaunay Triangulation Feng Gao Lei Hu Zhaofeng He

  2. Bridge Extraction Panchromatic Image: High reolution provides detailed descriptions. Challenge: Bridges are surrounded by complex backgrounds.

  3. Related Work Textual and geometric information [R. Trias-Sanz04] • Effective for small high-resolution images • Computation of texutre parameters takes a significant amount of time Boolean and/or logical low-level operator [D. Chaudhuri08] • Effective for small bridges • Not appropriate for high-resolution images

  4. Bridge Detection Output Input • Approach: • River Segmentation • Bridge Extraction

  5. River Segmentation • Extract water regions Water/land segmentation ?

  6. River Segmentation • Extract water regions based on texture analysis. (MRF model) Original Image Results • Our Approach: • Calculate textural parameters • ICM algorithm to estimate the MAP • Remove noisy regions

  7. Bridge Extraction • Important characteristic of bridge Intersection relationship with river flow • Morphological thinning operation • Easy to implement • Computation time is too long

  8. Bridge Extraction • Extract Bridges along the medial axis of river Constrained Delaunay Triangulation (CDT)

  9. Bridge Extraction • Extract Bridges along the medial axis of river River boundary CDT and medial axes

  10. Bridge Extraction • Extract Bridges along the medial axis of river Radon transform is used here to avlidate ROI if parallel lines are detected { ROI is real bridge region } Else { ROI should be neglected }

  11. Experiments Extensive experiments are performed on high resolution image gathered from Google earth. However, swells and building shadows cause many false alarms.

  12. Future work • River segmentation procedure will be refined to avoid the influence of swells in river and building shadows. • Better vectorization algorithms to make the skeletal description more accurate.

  13. Acknowledgements Special thanks to Shewchuk for providing the Triangle program Special thanks to anonymous reviewers Special thanks to session chair and audience Thank you

  14. Q&A

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