Are Spatial and Global Constraints Really Necessary for Segmentation?

Pascal Fua, Kevin Smith, Aurélien Lucchi, Yunpeng Li, Xavier Boix Bosch
2011
Article de conférence

Résumé

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.

Source officielle

https://infoscience.epfl.ch/record/169178?ln=fr

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