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A Non-Local Cost Aggregation Method for Stereo Matching Yang, QingXiong ( 杨庆雄 )

A Non-Local Cost Aggregation Method for Stereo Matching Yang, QingXiong ( 杨庆雄 ) City University of Hong Kong. 3. 6. 9. 2. 5. 8. 1. 4. 7. =>. a planar graph. A 2D image ( 3x3 ). 3. 6. 9. 2. 5. 8. 1. 4. 7. Computing minimum spanning tree (MST). 6. 3. 5. 9. 1. 2. 4.

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A Non-Local Cost Aggregation Method for Stereo Matching Yang, QingXiong ( 杨庆雄 )

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  1. A Non-Local Cost Aggregation Method for Stereo Matching Yang, QingXiong (杨庆雄) City University of Hong Kong

  2. 3 6 9 2 5 8 1 4 7 => a planar graph A 2D image (3x3)

  3. 3 6 9 2 5 8 1 4 7 Computing minimum spanning tree (MST)

  4. 6 3 5 9 1 2 4 8 1 1 7 Obtained MST

  5. 6 3 5 9 1 2 4 8 Distance 1 1 7

  6. 6 3 5 9 1 2 4 8 Distance 1 1 7

  7. 6 3 5 9 1 2 4 8 1 Distance 1 7

  8. 6 3 5 9 1 2 4 8 1 Distance 1 7 shortest distance of traveling from one node to another

  9. 6 3 5 9 1 2 4 8 1 Distance 1 7 Similarity:

  10. 6 3 5 9 1 2 4 8 1 Supports received from other nodes: 1 7 That is: after cost aggregation,

  11. A Linear Time Algorithm

  12. 1. Aggregating from leaf nodes to root node: 6 3 5 9 2 4 8 1 7

  13. 1. Aggregating from leaf nodes to root node: 6 3 5 9 2 4 8 1 7

  14. 1. Aggregating from leaf nodes to root node: 6 3 5 9 2 4 8 1 7

  15. 1. Aggregating from leaf nodes to root node: 6 3 5 9 2 4 8 1 7

  16. 1. Aggregating from leaf nodes to root node: 6 3 5 9 2 4 8 1 7

  17. 1. Aggregating from leaf nodes to root node: 6 3 5 9 2 4 8 1 7

  18. 1. Aggregating from leaf nodes to root node: 6 3 5 9 2 4 8 1 7

  19. 1. Aggregating from leaf nodes to root node: 6 3 5 9 2 4 8 1 7

  20. 2. Aggregating from root node to leaf nodes: 6 3 5 9 2 4 8 ( ) 1 7

  21. 2. Aggregating from root node to leaf nodes: 6 3 5 9 2 4 8 ( ) 1 7

  22. 2. Aggregating from root node to leaf nodes: 6 3 5 9 2 4 8 ( ) 1 7

  23. 2. Aggregating from root node to leaf nodes: 6 3 5 9 2 4 8 ( ) 1 7

  24. 2. Aggregating from root node to leaf nodes: 6 3 5 9 2 4 8 ( ) 1 7

  25. 2. Aggregating from root node to leaf nodes: 6 3 5 9 2 4 8 ( ) 1 7

  26. 2. Aggregating from root node to leaf nodes: 6 3 5 9 2 4 8 ( ) 1 7

  27. 2. Aggregating from root node to leaf nodes: 6 3 5 9 2 4 8 ( ) 1 7

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