@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 = {

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.

}, 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 {48178, title = {Mapping auto-context decision forests to deep ConvNets for semantic segmentation.}, booktitle = {British Machine Vision Conference}, year = {2016}, month = {2016 Sep 19}, address = {York}, abstract = {

In this paper, we propose a mapping from the Auto-context model to a deep Convolutional Neural Network (ConvNet), bridging the gap be- tween these two models, and helping address the challenge of training ConvNets with limited training data.\ 

}, url = {http://www.bmva.org/bmvc/2016/abstracts.pdf}, author = {Richmond, David L and Kainmueller, Dagmar and Yang, Michael Y and Myers, Eugene W and Rother, Carsten} } @article {48164, title = {Active graph matching for automatic joint segmentation and annotation of C. elegans.}, journal = {Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention}, volume = {17}, year = {2014}, month = {2014}, pages = {81-8}, abstract = {

In this work we present a novel technique we term active graph matching, which integrates the popular active shape model into a sparse graph matching problem. This way we are able to combine the benefits of a global, statistical deformation model with the benefits of a local deformation model in form of a second-order random field. We present a new iterative energy minimization technique which achieves empirically good results. This enables us to exceed state-of-the art results for the task of annotating nuclei in 3D microscopic images of C. elegans. Furthermore with the help of the generalized Hough transform we are able to jointly segment and annotate a large set of nuclei in a fully automatic fashion for the first time.

}, keywords = {Algorithms, Animals, Caenorhabditis elegans, Cell Nucleus, Image Enhancement, Image Interpretation, Computer-Assisted, Microscopy, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, Subtraction Technique}, author = {Kainmueller, Dagmar and Jug, Florian and Rother, Carsten and Myers, Gene} }