@article {Lempitsky2012,
	title = {Branch-and-mincut: Global optimization for image segmentation with high-level priors},
	journal = {Journal of Mathematical Imaging and Vision},
	volume = {44},
	number = {3},
	year = {2012},
	pages = {315{\textendash}329},
	abstract = {Efficient global optimization techniques such as graph cut exist for energies corresponding to binary image segmentation from low-level cues. However, introducing a high-level prior such as a shape prior or a color-distribution prior into the segmentation process typically results in an energy that is much harder to optimize. The main contribution of the paper is a new global optimization framework for a wide class of such energies. The framework is built upon two powerful techniques: graph cut and branch-and-bound. These techniques are unified through the derivation of lower bounds on the energies. Being computable via graph cut, these bounds are used to prune branches within a branchand-bound search. We demonstrate that the new framework can compute globally optimal segmentations for a variety of segmentation scenarios in a reasonable time on a modern CPU. These scenarios include unsupervised segmentation of an object undergoing 3D pose change, category-specific shape segmentation, and the segmentation under intensity/color priors defined by Chan-Vese and GrabCut functionals. {\textcopyright} Springer Science+Business Media, LLC 2011.},
	keywords = {Branch-and-bound, Global optimization, Graph Cuts, Image segmentation, Shape priors},
	issn = {09249907},
	doi = {10.1007/s10851-012-0328-0},
	author = {Lempitsky, Victor and Blake, Andrew and Carsten Rother}
}
@conference {Gehler2008,
	title = {Bayesian color constancy revisited},
	booktitle = {26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR},
	year = {2008},
	abstract = {Computational color constancy is the task of estimating the true reflectances of visible surfaces in an image. In this paper we follow a line of research that assumes uniform illumination of a scene, and that the principal step in estimating reflectances is the estimation of the scene illuminant. We review recent approaches to illuminant estimation, firstly those based on formulae for normalisation of the reflectance distribution in an image - so-called grey-world algorithms, and those based on a Bayesian formulation of image formation. In evaluating these previous approaches we introduce a new tool in the form of a database of 568 high-quality, indoor and outdoor images, accurately labelled with illuminant, and preserved in their raw form, free of correction or normalisation. This has enabled us to establish several properties experimentally. Firstly automatic selection of grey-world algorithms according to image properties is not nearly so effective as has been thought. Secondly, it is shown that Bayesian illuminant estimation is significantly improved by the improved accuracy of priors for illuminant and reflectance that are obtained from the new dataset. {\textcopyright}2008 IEEE.},
	isbn = {9781424422432},
	doi = {10.1109/CVPR.2008.4587765},
	author = {Gehler, Peter Vincent and Carsten Rother and Blake, Andrew and Minka, Tom and Sharp, Toby}
}
