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Conditional Random Fields (CRF) CRF is a discriminant model for sequences data similar to MEMM. It models the dependency between each state and the entire input sequences. Unlike MEMM, CRF overcomes the label bias issue by using global normalizer.

MRF and CRF share the same graphical models, but MRF are generative models which model the joint probability distribution, while CRF are discriminative models which model the conditional probability distribution.

Conditional Random Fields (CRFs) are often used to improve the output of an initial segmentation model, such as a convolutional neural network (CNN). Conventional CRF approaches in medical imaging use manually defined features, such as intensity to improve appearance similarity or location to improve spatial coherence.

Graphical models, especially Conditional Random Fields (CRFs) have been used as refinement layers in deep semantic segmentation architectures. The main objective is to capture low-level detail in regions where class intersections occur. These boundary regions are particularly difficult to segment with precision.

Conditional Random Fields or CRFs are a type of probabilistic graph model that take neighboring sample context into account for tasks like classification.

Conditional random fields (CRFs) are a class of statistical modeling methods often applied in pattern recognition and machine learning and used for structured prediction.

Conditional Random Field (CRF) The purpose of CRF is to refine the coarse output based on the label at each location itself, and the neighboring positions' labels and locations. Fully connected pairwise CRF is considered. Fully connected means all locations are connected as shown in the middle of the figure above.