@conference {Royer2016,
	title = {Convexity shape constraints for image segmentation},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	volume = {2016-Decem},
	year = {2016},
	month = {sep},
	pages = {402{\textendash}410},
	abstract = {Segmenting an image into multiple components is a central task in computer vision. In many practical scenarios, prior knowledge about plausible components is available. Incorporating such prior knowledge into models and algorithms for image segmentation is highly desirable, yet can be non-trivial. In this work, we introduce a new approach that allows, for the first time, to constrain some or all components of a segmentation to have convex shapes. Specifically, we extend the Minimum Cost Multicut Problem by a class of constraints that enforce convexity. To solve instances of this NP-hard integer linear program to optimality, we separate the proposed constraints in the branch-and-cut loop of a state-of-the-art ILP solver. Results on photographs and micrographs demonstrate the effectiveness of the approach as well as its advantages over the state-of-the-art heuristic.},
	isbn = {9781467388504},
	issn = {10636919},
	doi = {10.1109/CVPR.2016.50},
	url = {http://arxiv.org/abs/1509.02122},
	author = {Royer, Loic A. and Richmond, David L. and Carsten Rother and Bj{\"o}rn Andres and Kainmueller, Dagmar}
}
@conference {Richmond2016,
	title = {Mapping auto-context decision forests to deep convnets for semantic segmentation},
	booktitle = {British Machine Vision Conference 2016, BMVC 2016},
	volume = {2016-Septe},
	year = {2016},
	month = {jul},
	pages = {144.1{\textendash}144.12},
	abstract = {We consider the task of pixel-wise semantic segmentation given a small set of labeled training images. Among two of the most popular techniques to address this task are Random Forests (RF) and Neural Networks (NN). In this work, we explore the relationship between two special forms of these techniques: stacked RFs (namely Auto-context) and deep Convolutional Neural Networks (ConvNet). Our main contribution is to show that Auto-context can be mapped to a deep ConvNet with novel architecture, and thereby trained end-to-end. This mapping can be viewed as an intelligent initialization of a deep ConvNet, enabling training even in the face of very limited amounts of training data. We also demonstrate an approximate mapping back from the refined ConvNet to a second stacked RF, with improved performance over the original. We experimentally verify that these mappings outperform stacked RFs for two different applications in computer vision and biology: Kinect-based body part labeling from depth images, and somite segmentation in microscopy images of developing zebrafish.},
	doi = {10.5244/C.30.144},
	url = {http://arxiv.org/abs/1507.07583},
	author = {Richmond, David L. and Kainmueller, Dagmar and Yang, Michael Y. and Myers, Eugene W. and Carsten Rother}
}
