Learning low-level vision Computer Examples by Michael Ross

Learning low-level vision

Computer Examples

by Michael Ross

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.

Ising model

True scene. Noise corrupted. Reconstructed.

Ising model with Gaussian noise

True scene. Noise corrupted. Reconstructed.

Learned optical flow

Learned optical flow

Learned optical flow

Super-resolution

Super-resolution

Super-resolution

Super-resolution

Super-resolution

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.

Segmentation

Signal: horizontal & verticalgradients.

Scene: edge detected bymotion.

Segmentation

...

Segmentation

Signal: horizontal & verticalgradients.

Scene: edge detected bybelief propagation.

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.