Are Spatial and Global Constraints Really Necessary for Segmentation?

Many state-of-the-art segmentation algorithms rely on Markov or Conditional Random Field models designed to enforce spatial and global consistency constraints. This is often accomplished by introducing additional latent variables to the model, which can greatly increase its complexity. As a result, estimating the model parameters or computing the best maximum a posteriori (MAP) assignment becomes a computationally expensive task.


Published in:
2011 IEEE International Conference On Computer Vision (ICCV), 9-16
Presented at:
IEEE International Conference on Computer Vision (ICCV), Barcelona
Year:
2011
Publisher:
IEEE
Keywords:
Laboratories:




 Record created 2011-09-29, last modified 2018-03-17

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