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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.
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Related Work
Bridge Detection Input Output ,[object Object],[object Object],[object Object]
[object Object],River Segmentation ? Water/land segmentation
[object Object],River Segmentation ,[object Object],[object Object],[object Object],[object Object],Original Image Results
[object Object],Bridge Extraction Intersection relationship with river flow ,[object Object],[object Object],[object Object]
[object Object],Bridge Extraction Constrained Delaunay Triangulation (CDT)
[object Object],Bridge Extraction River boundary CDT and medial axes
[object Object],Bridge Extraction Radon transform  is used here to avlidate ROI if  parallel lines are detected { ROI is real bridge region } Else { ROI should be neglected }
Experiments Extensive experiments are performed on high resolution image gathered from Google earth. However, swells and building shadows cause many false alarms.
Future work ,[object Object],[object Object]
Acknowledgements Special thanks to Shewchuk for providing the Triangle program Special thanks to anonymous reviewers Special thanks to session chair and audience Thank you
Q&A

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Editor's Notes

  1. What is this work about? Detect bridges in remote sensing images. Strength: High resolution provides detailed descriptions Weakness: Complex background
  2. Many researches extract water regions by using histogram threshold, in our view, the segmentation method based on histogram is not practical.