Changes

Speaker: Nazre Batool -- Date: August 25, 2011

Speaker: Nazre Batool -- Date: August 25, 2011

This talk will present a brief overview of random fi�eld models for computer vision. Markov Random Field (MRF) models have been most popular class of models

This talk will present a brief overview of random field models for computer vision. Markov Random Field (MRF) models have been most popular class of models

for computer vision applications. Recently, new class of models, Conditional Random Fields (CRF), has been introduced. Although CRFs were fi�rst introduced for labeling 1D sequences, they have also been incorporated for 2D images for applications such as labeling and object recognition. Another model, Discriminative Random Field (DRF) model, inspired by CRF, has been applied successfully for image denoising and labeling. In this talk, the key differences between MRF and CRF/DRF will be highlighted. The main diff erence between the two classes of models can be best understood on the basis of generative vs .discriminative probabilistic models based on graphs. Hence, graphical models will also be briefly discussed in the talk.

for computer vision applications. Recently, new class of models, Conditional Random Fields (CRF), has been introduced. Although CRFs were first introduced for labeling 1D sequences, they have also been incorporated for 2D images for applications such as labeling and object recognition. Another model, Discriminative Random Field (DRF) model, inspired by CRF, has been applied successfully for image denoising and labeling. In this talk, the key differences between MRF and CRF/DRF will be highlighted. The main diff erence between the two classes of models can be best understood on the basis of generative vs .discriminative probabilistic models based on graphs. Hence, graphical models will also be briefly discussed in the talk.

==Current Seminar Series Coordinators==

==Current Seminar Series Coordinators==