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Global TRANSFORMATION ESTIMATION VIA LOCAL REGION CONSENSUS

Global TRANSFORMATION ESTIMATION VIA LOCAL REGION CONSENSUS. Erez Farhan. The Challenge of Point M atching. The Challenge of Point Matching. The Challenge of Point Matching. Viewpoint/Pose We focus on this Illumination Occlusions [self and incident] Blur, sensor noise, etc.

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Global TRANSFORMATION ESTIMATION VIA LOCAL REGION CONSENSUS

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  1. Global TRANSFORMATION ESTIMATION VIA LOCAL REGION CONSENSUS Erez Farhan

  2. The Challenge of Point Matching

  3. The Challenge of Point Matching

  4. The Challenge of Point Matching • Viewpoint/Pose • We focus on this • Illumination • Occlusions [self and incident] • Blur, sensor noise, etc.

  5. A standard Solution – invariant local regions

  6. A Standard Solution – Invariant Local Regions • How to make invariant? • Normalize each region to canonic shape • Robust representation (description) • Robust matching methods • The good • Natural for point matching (local) • Good approximation (affine instead of perspective) – > normalization is possible • Underlying transformation estimation (Important for us!) • The bad • False matches • Normalization = estimation from few statistics • We are in a deadlock here! • Low resolution of matches

  7. Breaking the deadlock Anchor region Neighbor candidate

  8. Breaking the deadlock • Initialize with tentative matches from standard algorithm (MSER) • Find neighboring regions that agree on co-planarity • Unions of regions -> Effectively bigger regions • Gains • Better transformation precision • Beyond local approximation • Validating region matches • Bonus • Predict More point matches! • Question: • How to decide on merge?

  9. Breaking the deadlock Green – anchor region Red – neighbor candidate

  10. Consensus– predict target locations Predicted neighbor Original match

  11. Consensus– Unite and eat another one! New Unified Anchor Next Neighbor

  12. Improving Predictions Predicted neighbor Original match

  13. Predicting New Points • Unified regions enables estimating planar perspective transform • Every point on the plane can now be predicted • Using the most appropriate group transform • Live example

  14. Questions / Future Ideas? • Work with more complex transforms • (elastic) • Multiple

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