Richard Szeliski • Ramin Zabih • Daniel Scharstein • Olga Veksler •
Vladimir Kolmogorov • Aseem Agarwala • Marshall Tappen • Carsten Rother

This site contains the results (plots and images) and code accompanying our paper

A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors,
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 30(6):1068-1080, June 2008.

The first version, corresponding to our ECCV 06 paper, is still available here.

 

 

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For an up-to-date and more comprehensive study see:

• A comparative study of modern inference techniques for discrete energy minimization problems by Kappes, Andres, et al., IJCV 2015.
• OpenGM2, the corresponding benchmark and code

 

 

 

Support for this work was provided in part by NSF grant IIS-0413169. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.