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Learning low-level vision

Computer Examples by Michael Ross. Learning low-level vision. Ising model. Each location has a 50% chance of being 'up' or 'down'. There is a 60% chance that a location has the same value as one of its 8-connected neighbors.

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Learning low-level vision

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  1. Computer Examples by Michael Ross Learning low-level vision

  2. Ising model • Each location has a 50% chance of being 'up' or 'down'. • There is a 60% chance that a location has the same value as one of its 8-connected neighbors. • There is an 80% chance that the sensor at a location reports the correct spin.

  3. Ising model Noise corrupted. Reconstructed. True scene.

  4. Ising model with Gaussian noise Noise corrupted. Reconstructed. True scene.

  5. Learned optical flow

  6. Learned optical flow

  7. Learned optical flow

  8. Super-resolution

  9. Super-resolution

  10. Super-resolution

  11. Super-resolution

  12. Super-resolution

  13. Segmentation • An attempt to learn segmentation rules from examples. • Learn sensor models for each feature. • Construct an MRF with interconnected layers, one for each feature. • Allow individually insufficient features to exchange information.

  14. Segmentation Signal: horizontal & vertical gradients. Scene: edge detected by motion.

  15. Segmentation ...

  16. Segmentation Signal: horizontal & vertical gradients. Scene: edge detected by belief propagation.

  17. Segmentation • Issues: takes about 25 minutes to produce result (10 iterations). Why? Considers 100 possible candidates at each location -> ~36 million calculations per iteration. • Simple features are not very predictive at many locations - better features mean that we need to consider fewer candidates. • Benefit: learning reduces the number of assumptions and preconceptions.

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