gao.ppt

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

Transcript of gao.ppt

  • 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.

  • 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

Related Work 4. Bridge Detection Input Output

  • Approach:
  • River Segmentation
  • Bridge Extraction

5.

  • Extract water regions

River Segmentation ? Water/land segmentation 6.

  • Extract water regions based on texture analysis. (MRF model)

River Segmentation

  • Our Approach:
  • Calculate textural parameters
  • ICM algorithm to estimate the MAP
  • Remove noisy regions

Original Image Results 7.

  • Important characteristic of bridge

Bridge Extraction Intersection relationship with river flow

  • Morphological thinning operation
  • Easy to implement
  • Computation time is too long

8.

  • Extract Bridges along the medial axis of river

Bridge Extraction Constrained Delaunay Triangulation (CDT) 9.

  • Extract Bridges along the medial axis of river

Bridge Extraction River boundary CDT and medial axes 10.

  • Extract Bridges along the medial axis of river

Bridge Extraction Radon transformis used here to avlidate ROI ifparallel 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 segmentationprocedure will be refined to avoid the influence of swells in river and building shadows.
  • Better vectorization algorithmsto 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