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

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Presentation transcript:

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.

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

Bridge Detection Input Output Approach: 1. River Segmentation 2. Bridge Extraction Approach: 1. River Segmentation 2. Bridge Extraction

Extract water regions River Segmentation ? Water/land segmentation

Extract water regions based on texture analysis. (MRF model) River Segmentation Our Approach: 1. Calculate textural parameters 2. ICM algorithm to estimate the MAP 3. Remove noisy regions Our Approach: 1. Calculate textural parameters 2. ICM algorithm to estimate the MAP 3. Remove noisy regions Original Image Results

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

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

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

Extract Bridges along the medial axis of river 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  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.

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