Markov random field modeling in computer vision
Markov random field modeling in computer vision
A constant factor approximation algorithm for a class of classification problems
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
Segmentation by Grouping Junctions
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Markov Random Fields with Efficient Approximations
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Approximate classification via earthmover metrics
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
The Hardness of Metric Labeling
FOCS '04 Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science
A Linear Programming Formulation and Approximation Algorithms for the Metric Labeling Problem
SIAM Journal on Discrete Mathematics
MRF's forMRI's: Bayesian Reconstruction of MR Images via Graph Cuts
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
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Metric Labeling problems have been introduced as a model for understanding noisy data with pair-wise relations between the data points. One application of labeling problems with pair-wise relations is image understanding, where the underlying assumption is that physically close pixels are likely to belong to the same object.In this paper we consider a variant of this problem, we will call Parallel Imaging, where instead of directly observing the noisy data, the data undergoes a simple linear transformation first, such as adding different images. This class of problems arises in a wide range of imaging problems. Our study has been motivated by the Parallel Imaging problem in Magnetic Resonance Image (MRI) reconstruction. We give a constant factor approximation algorithm for the case of speedup of two with the truncated linear metric, motivated by the MRI reconstruction problem. Our method uses local search and graph cut techniques.