@conference {Brachmann2016,
	title = {Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	volume = {2016-Decem},
	year = {2016},
	pages = {3364{\textendash}3372},
	abstract = {In recent years, the task of estimating the 6D pose of object instances and complete scenes, i.e. camera localization, from a single input image has received considerable attention. Consumer RGB-D cameras have made this feasible, even for difficult, texture-less objects and scenes. In this work, we show that a single RGB image is sufficient to achieve visually convincing results. Our key concept is to model and exploit the uncertainty of the system at all stages of the processing pipeline. The uncertainty comes in the form of continuous distributions over 3D object coordinates and discrete distributions over object labels. We give three technical contributions. Firstly, we develop a regularized, auto-context regression framework which iteratively reduces uncertainty in object coordinate and object label predictions. Secondly, we introduce an efficient way to marginalize object coordinate distributions over depth. This is necessary to deal with missing depth information. Thirdly, we utilize the distributions over object labels to detect multiple objects simultaneously with a fixed budget of RANSAC hypotheses. We tested our system for object pose estimation and camera localization on commonly used data sets. We see a major improvement over competing systems.},
	isbn = {9781467388504},
	issn = {10636919},
	doi = {10.1109/CVPR.2016.366},
	author = {Brachmann, Eric and Michel, Frank and Krull, Alexander and Yang, Michael Ying and Gumhold, Stefan and Carsten Rother}
}
@conference {Brachmann2016a,
	title = {Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	volume = {2016-Decem},
	year = {2016},
	pages = {3364{\textendash}3372},
	abstract = {In recent years, the task of estimating the 6D pose of object instances and complete scenes, i.e. camera localization, from a single input image has received considerable attention. Consumer RGB-D cameras have made this feasible, even for difficult, texture-less objects and scenes. In this work, we show that a single RGB image is sufficient to achieve visually convincing results. Our key concept is to model and exploit the uncertainty of the system at all stages of the processing pipeline. The uncertainty comes in the form of continuous distributions over 3D object coordinates and discrete distributions over object labels. We give three technical contributions. Firstly, we develop a regularized, auto-context regression framework which iteratively reduces uncertainty in object coordinate and object label predictions. Secondly, we introduce an efficient way to marginalize object coordinate distributions over depth. This is necessary to deal with missing depth information. Thirdly, we utilize the distributions over object labels to detect multiple objects simultaneously with a fixed budget of RANSAC hypotheses. We tested our system for object pose estimation and camera localization on commonly used data sets. We see a major improvement over competing systems.},
	isbn = {9781467388504},
	issn = {10636919},
	doi = {10.1109/CVPR.2016.366},
	author = {Brachmann, Eric and Michel, Frank and Krull, Alexander and Yang, Michael Ying and Gumhold, Stefan and Carsten Rother}
}
@incollection {Richmond2015,
	title = {Uncertainty-driven forest predictors for vertebra localization and segmentation},
	volume = {9349},
	year = {2015},
	pages = {653{\textendash}660},
	abstract = {Accurate localization, identification and segmentation of vertebrae is an important task in medical and biological image analysis. The prevailing approach to solve such a task is to first generate pixelindependent features for each vertebra, e.g. via a random forest predictor, which are then fed into an MRF-based objective to infer the optimal MAP solution of a constellation model. We abandon this static, twostage approach and mix feature generation with model-based inference in a new, more flexible, way. We evaluate our method on two data sets with different objectives. The first is semantic segmentation of a 21-part body plan of zebrafish embryos in microscopy images, and the second is localization and identification of vertebrae in benchmark human CT.},
	issn = {16113349},
	doi = {10.1007/978-3-319-24553-9_80},
	author = {Richmond, David and Kainmueller, Dagmar and Glocker, Ben and Carsten Rother and Myers, Gene}
}
@article {Kohli2012,
	title = {User-centric learning and evaluation of interactive segmentation systems},
	journal = {International Journal of Computer Vision},
	volume = {100},
	number = {3},
	year = {2012},
	month = {dec},
	pages = {261{\textendash}274},
	abstract = {Many successful applications of computer vision to image or video manipulation are interactive by nature. However, parameters of such systems are often trained neglecting the user. Traditionally, interactive systems have been treated in the same manner as their fully automatic counterparts. Their performance is evaluated by computing the accuracy of their solutions under some fixed set of user interactions. In this paper, we study the problem of evaluating and learning interactive segmentation systems which are extensively used in the real world. The key questions in this context are how to measure (1) the effort associated with a user interaction, and (2) the quality of the segmentation result as perceived by the user. We conduct a user study to analyze user behavior and answer these questions. Using the insights obtained from these experiments, we propose a framework to evaluate and learn interactive segmentation systems which brings the user in the loop. The framework is based on the use of an active robot user-a simulated model of a human user. We show how this approach can be used to evaluate and learn parameters of state-of-the-art interactive segmentation systems. We also show how simulated user models can be integrated into the popular max-margin method for parameter learning and propose an algorithm to solve the resulting optimisation problem. {\textcopyright} 2012 The Author(s).},
	keywords = {Image segmentation, Interactive systems, Learning},
	issn = {09205691},
	doi = {10.1007/s11263-012-0537-4},
	author = {Kohli, Pushmeet and Nickisch, Hannes and Carsten Rother and Rhemann, Christoph}
}
@conference {Kirk2006,
	title = {Understanding photowork},
	booktitle = {Conference on Human Factors in Computing Systems - Proceedings},
	volume = {2},
	year = {2006},
	pages = {761{\textendash}770},
	abstract = {In this paper we introduce the notion of "photowork" as the activities people perform with their digital photos after capture but prior to end use such as sharing. Surprisingly, these processes of reviewing, downloading, organizing, editing, sorting and filing have received little attention in the literature yet they form the context for a large amount of the {\textquoteright}search{\textquoteright} and {\textquoteright}browse{\textquoteright} activities so commonly referred to in studies of digital photo software. Through a deeper understanding of photowork using field observation and interviews, we seek to highlight its significance as an interaction practice. At the same time, we discover how "search" as it is usually defined may have much less relevance than new ways of browsing for the design of new digital photo tools, in particular, browsing in support of the photowork activities we describe. Copyright 2006 ACM.},
	keywords = {Browsing, Content-based image retrieval, Digital photo albums, Photowork, Searching, Use of images},
	isbn = {1595931783},
	doi = {10.1145/1124772.1124885},
	author = {Kirk, David S and Sellen, Abigail J and Carsten Rother and Wood, Kenneth R}
}
