@conference {48179,
	title = {Convexity shape constraints for image segmentation.},
	booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
	year = {2016},
	month = {2016 Jun 27},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Las Vegas, NV},
	abstract = {<p>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.</p>
},
	doi = {10.1109/CVPR.2016.50},
	author = {Royer, Lo{\"\i}c A and Richmond, David L and Rother, Carsten and Andres, Bjoern and Kainmueller, Dagmar}
}
@conference {5685,
	title = {Efficient automatic 3D-reconstruction of branching neurons from EM data.},
	booktitle = {Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on},
	year = {2012},
	month = {2012},
	abstract = {<p>We present an approach for the automatic reconstruction of neurons from 3D stacks of electron microscopy sections. The core of our system is a set of possible assignments, each of which proposes with some cost a link between neuron regions in consecutive sections. These can model the continuation, branching, and end of neurons. The costs are trainable on positive assignment samples. An optimal and consistent set of assignments is found for the whole volume at once by solving an integer linear program. This set of assignments determines both the segmentation into neuron regions and the correspondence between such regions in neighboring slices. For each picked assignment, a confidence value helps to prioritize decisions to be reviewed by a human expert. We evaluate the performance of our method on an annotated volume of neural tissue and compare to the current state of the art [26]. Our method is superior in accuracy and can be trained using a small number of samples. The observed inference times are linear with about 2 milliseconds per neuron and section.</p>
},
	author = {Funke, Jan and Andres, Bjoern and Hamprecht, Fred and Cardona, Albert and Cook, Matthew}
}
