learning low-level vision computer examples by michael ross
TRANSCRIPT
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Learning low-level vision
Computer Examples
by Michael Ross
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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.
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Ising model
True scene. Noise corrupted. Reconstructed.
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Ising model with Gaussian noise
True scene. Noise corrupted. Reconstructed.
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Learned optical flow
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Learned optical flow
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Learned optical flow
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Super-resolution
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Super-resolution
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Super-resolution
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Super-resolution
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Super-resolution
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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.
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Segmentation
Signal: horizontal & verticalgradients.
Scene: edge detected bymotion.
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Segmentation
...
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Segmentation
Signal: horizontal & verticalgradients.
Scene: edge detected bybelief propagation.
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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.