@conference {Levinkov2017,
	title = {Joint graph decomposition \& node labeling: Problem, algorithms, applications},
	booktitle = {Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017},
	volume = {2017-Janua},
	year = {2017},
	pages = {1904{\textendash}1912},
	abstract = {We state a combinatorial optimization problem whose feasible solutions define both a decomposition and a node labeling of a given graph. This problem offers a common mathematical abstraction of seemingly unrelated computer vision tasks, including instance-separating semantic segmentation, articulated human body pose estimation and multiple object tracking. Conceptually, the problem we state generalizes the unconstrained integer quadratic program and the minimum cost lifted multicut problem, both of which are NP-hard. In order to find feasible solutions efficiently, we define two local search algorithms that converge monotonously to a local optimum, offering a feasible solution at any time. To demonstrate their effectiveness in tackling computer vision tasks, we apply these algorithms to instances of the problem that we construct from published data, using published algorithms. We report state-of-the-art application-specific accuracy for the three above-mentioned applications.},
	isbn = {9781538604571},
	doi = {10.1109/CVPR.2017.206},
	author = {Levinkov, Evgeny and Uhrig, Jonas and Tang, Siyu and Omran, Mohamed and Insafutdinov, Eldar and Kirillov, Alexander and Carsten Rother and Brox, Thomas and Schiele, Bernt and Bj{\"o}rn Andres}
}
@article {Eyjolfsdottir2014,
	title = {Detection of social actions in fruit flies},
	journal = {Lecture Notes in Computer Science},
	volume = {8690},
	number = {September 2014},
	year = {2014},
	pages = {772{\textendash}787},
	publisher = {Springer International Publishing},
	address = {Cham},
	abstract = {Spatio-temporal detection of actions and events in video is a challenging problem. Besides the difficulties related to recognition, a major challenge for detection in video is the size of the search space defined by spatio-temporal tubes formed by sequences of bounding boxes along the frames. Recently methods that generate unsupervised detection proposals have proven to be very effective for object detection in still images. These methods open the possibility to use strong but computationally expensive features since only a relatively small number of detection hypotheses need to be assessed. In this paper we make two contributions towards exploiting detection proposals for spatio-temporal detection problems. First, we extend a recent 2D object proposal method, to produce spatio-temporal proposals by a randomized supervoxel merging process. We introduce spatial, temporal, and spatio-temporal pairwise supervoxel features that are used to guide the merging process. Second, we propose a new efficient supervoxel method. We experimentally evaluate our detection proposals, in combination with our new supervoxel method as well as existing ones. This evaluation shows that our supervoxels lead to more accurate proposals when compared to using existing state-of-the-art supervoxel methods.},
	isbn = {978-3-319-10604-5},
	issn = {16113349},
	doi = {10.1007/978-3-319-10605-2},
	url = {http://link.springer.com/10.1007/978-3-319-10605-2 http://www.ncbi.nlm.nih.gov/pubmed/31629782},
	author = {Eyjolfsdottir, Eyrun and Branson, Steve and Burgos-Artizzu, Xavier P. and Hoopfer, Eric D. and Schor, Jonathan and Anderson, David J. and Perona, Pietro},
	editor = {Fleet, David and Pajdla, Tomas and Schiele, Bernt and Tuytelaars, Tinne}
}
@conference {jehle2010,
	title = {Learning of optimal illumination for material classification},
	booktitle = {Pattern Recognition},
	volume = {6376},
	year = {2010},
	pages = {563--572},
	publisher = {Springer},
	organization = {Springer},
	abstract = {We present a method to classify materials in illumination series data. An illumination series is acquired using a device which is capable to generate arbitrary lighting environments covering nearly the whole space of the upper hemisphere. The individual images of the illumination series span a high-dimensional feature space. Using a random forest classifier different materials, which vary in appearance (which itself depends on the patterns of incoming illumination), can be distinguished reliably. The associated Gini feature importance allows for determining the features which are most relevant for the classification result. By linking the features to illumination patterns a proposition about optimal lighting for defect detection can be made, which yields valuable information for the selection and placement of light sources.},
	doi = {10.1007/978-3-642-15986-2_57},
	author = {Markus Jehle and Christoph Sommer and Bernd J{\"a}hne},
	editor = {Goesele, Michael and Roth, Stefan and Schiele, Bernt and Schindler, Konrad and Kuijper, Arjan}
}
