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

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 True scene. Noise corrupted.Reconstructed.

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

5. Learned optical flow

8. 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.