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Image Textures and Gibbs Random Fields. Computational Imaging and Vision Springer, (1999)P System Implementation of Dynamic Programming Stereo., , and . Journal of Mathematical Imaging and Vision, 47 (1-2): 13-26 (2013)Non-Markov Gibbs image model with almost local pairwise pixel interactions.. TFCV, page 89-98. Springer, (1996)Matching Multiple Views by the Least Square Correlation., and . Theoretical Foundations of Computer Vision, volume 2032 of Lecture Notes in Computer Science, page 105-114. Springer, (2000)Accurate 3D Modelling by Fusion of Potentially Reliable Active Range and Passive Stereo Data., , , and . CAIP, volume 5702 of Lecture Notes in Computer Science, page 848-855. Springer, (2009)Accurate Identification of a Markov-Gibbs Model for Texture Synthesis by Bunch Sampling., and . CAIP, volume 4673 of Lecture Notes in Computer Science, page 979-986. Springer, (2007)Supervised Texture Segmentation by Maximising Conditional Likelihood.. EMMCVPR, volume 2134 of Lecture Notes in Computer Science, page 169-184. Springer, (2001)Intramyocardial strain estimation from cardiac cine MRI., , , and . Int. J. Comput. Assist. Radiol. Surg., 10 (8): 1299-1312 (2015)Fast Unsupervised Segmentation of 3D Magnetic Resonance Angiography., , , , and . ICIP, page 93-96. IEEE, (2006)A new framework for automated segmentation of left ventricle wall from contrast enhanced cardiac magnetic resonance images., , , and . ICIP, page 2289-2292. IEEE, (2011)