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Markov Random Fields for Edge Classification

Markov Random Fields for Edge Classification. Grant Schindler CS 7636 Final Project. Problem: Edge Classification. Given vanishing points of a scene, classify each pixel according to vanishing direction. MAP Edge Classifications. Red: VP1 Green: VP2 Blue: VP3 Gray: Other White: Off.

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Markov Random Fields for Edge Classification

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  1. Markov Random Fields for Edge Classification Grant Schindler CS 7636 Final Project

  2. Problem: Edge Classification Given vanishing points of a scene, classify each pixel according to vanishing direction

  3. MAP Edge Classifications Red: VP1Green: VP2 Blue: VP3 Gray: Other White: Off

  4. Bayesian Model p(M | G,V) = p(G | M,V) p(M) / Z M = classifications, G = gradient magnitude/direction, V = vanishing points Independent Prior MRF Prior m Prior: p(m) Likelihood: p(g | m,V) g

  5. Classifications w/MRF Prior Gibbs sampling over 4-neighbor lattice w/ clique potentials defined as: A if i=j, B if i <> j

  6. Gibbs Sampling & MRFs Sample from distribution over labels for one site conditioned on all other sites in its Markov blanket Gibbs sampling approximates posterior distribution over classifications at each site (by iterating and accumulating statistics)

  7. Directional MRF vp Give more weight to potentials of neighbors which lie along the vanishing direction of current model

  8. Original Image

  9. Independent Prior

  10. MRF Prior

  11. Directional MRF Prior

  12. Conclusion, Discussion • This is really the E-Step of an EM process that alternates between edge classification and vanishing point estimation - will MRFs improve the vanishing point estimates? • Questions?

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