gao.ppt

Bridge Extraction based on Constrained Delaunay Triangulation

Feng GaoLei 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 DetectionInput

Output

Approach:1.1. River SegmentationRiver Segmentation2.2. Bridge ExtractionBridge Extraction

Approach:1.1. River SegmentationRiver Segmentation2.2. Bridge ExtractionBridge Extraction

• Extract water regionsRiver Segmentation

?Water/land segmentation

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

River Segmentation

Our Approach:1.1. Calculate textural parametersCalculate textural parameters2.2. ICM algorithm to estimate the MAPICM algorithm to estimate the MAP3.3. Remove noisy regionsRemove noisy regions

Our Approach:1.1. Calculate textural parametersCalculate textural parameters2.2. ICM algorithm to estimate the MAPICM algorithm to estimate the MAP3.3. Remove noisy regionsRemove noisy regions

Original Im

age

Results

• Important characteristic of bridgeBridge Extraction

Intersection relationship with river flow

Morphological thinning operation+ Easy to implement− Computation time is too long

• Extract Bridges along the medial axis of riverBridge 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 riverBridge 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