@conference {Li2019,
	title = {Deep Object Co-segmentation},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {11363 LNCS},
	year = {2019},
	pages = {638{\textendash}653},
	abstract = {This work presents a deep object co-segmentation (DOCS) approach for segmenting common objects of the same class within a pair of images. This means that the method learns to ignore common, or uncommon, background stuff and focuses on common objects. If multiple object classes are presented in the image pair, they are jointly extracted as foreground. To address this task, we propose a CNN-based Siamese encoder-decoder architecture. The encoder extracts high-level semantic features of the foreground objects, a mutual correlation layer detects the common objects, and finally, the decoder generates the output foreground masks for each image. To train our model, we compile a large object co-segmentation dataset consisting of image pairs from the PASCAL dataset with common objects masks. We evaluate our approach on commonly used datasets for co-segmentation tasks and observe that our approach consistently outperforms competing methods, for both seen and unseen object classes.},
	isbn = {9783030208929},
	issn = {16113349},
	doi = {10.1007/978-3-030-20893-6_40},
	author = {Weihao Li and Omid Hosseini Jafari and Carsten Rother}
}
@conference {Ramos2017,
	title = {Detecting unexpected obstacles for self-driving cars: Fusing deep learning and geometric modeling},
	booktitle = {IEEE Intelligent Vehicles Symposium, Proceedings},
	year = {2017},
	month = {dec},
	pages = {1025{\textendash}1032},
	abstract = {The detection of small road hazards, such as lost cargo, is a vital capability for self-driving cars. We tackle this challenging and rarely addressed problem with a vision system that leverages appearance, contextual as well as geometric cues. To utilize the appearance and contextual cues, we propose a new deep learning-based obstacle detection framework. Here a variant of a fully convolutional network is proposed to predict a pixel-wise semantic labeling of (i) free-space, (ii) on-road unexpected obstacles, and (iii) background. The geometric cues are exploited using a state-of-The-Art detection approach that predicts obstacles from stereo input images via model-based statistical hypothesis tests. We present a principled Bayesian framework to fuse the semantic and stereo-based detection results. The mid-level Stixel representation is used to describe obstacles in a flexible, compact and robust manner. We evaluate our new obstacle detection system on the Lost and Found dataset, which includes very challenging scenes with obstacles of only 5 cm height. Overall, we report a major improvement over the state-of-The-Art, with a performance gain of 27.4\%. In particular, we achieve a detection rate of over 90\% for distances of up to 50 m. Our system operates at 22 Hz on our self-driving platform.},
	isbn = {9781509048045},
	doi = {10.1109/IVS.2017.7995849},
	url = {http://arxiv.org/abs/1612.06573},
	author = {Ramos, Sebastian and Gehrig, Stefan and Pinggera, Peter and Franke, Uwe and Carsten Rother}
}
@conference {Brachmann2017,
	title = {DSAC - Differentiable RANSAC for camera localization},
	booktitle = {Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017},
	volume = {2017-Janua},
	year = {2017},
	month = {nov},
	pages = {2492{\textendash}2500},
	abstract = {RANSAC is an important algorithm in robust optimization and a central building block for many computer vision applications. In recent years, traditionally hand-crafted pipelines have been replaced by deep learning pipelines, which can be trained in an end-to-end fashion. However, RANSAC has so far not been used as part of such deep learning pipelines, because its hypothesis selection procedure is non-differentiable. In this work, we present two different ways to overcome this limitation. The most promising approach is inspired by reinforcement learning, namely to replace the deterministic hypothesis selection by a probabilistic selection for which we can derive the expected loss w.r.t. to all learnable parameters. We call this approach DSAC, the differentiable counterpart of RANSAC. We apply DSAC to the problem of camera localization, where deep learning has so far failed to improve on traditional approaches. We demonstrate that by directly minimizing the expected loss of the output camera poses, robustly estimated by RANSAC, we achieve an increase in accuracy. In the future, any deep learning pipeline can use DSAC as a robust optimization component.},
	isbn = {9781538604571},
	doi = {10.1109/CVPR.2017.267},
	url = {http://arxiv.org/abs/1611.05705},
	author = {Brachmann, Eric and Krull, Alexander and Nowozin, Sebastian and Shotton, Jamie and Michel, Frank and Gumhold, Stefan and Carsten Rother}
}
@conference {Zheng2014,
	title = {Dense semantic image segmentation with objects and attributes},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	year = {2014},
	pages = {3214{\textendash}3221},
	abstract = {The concepts of objects and attributes are both important for describing images precisely, since verbal descriptions often contain both adjectives and nouns (e.g. {\textquoteright}I see a shiny red chair{\textquoteright}). In this paper, we formulate the problem of joint visual attribute and object class image segmentation as a dense multi-labelling problem, where each pixel in an image can be associated with both an object-class and a set of visual attributes labels. In order to learn the label correlations, we adopt a boosting-based piecewise training approach with respect to the visual appearance and co-occurrence cues. We use a filtering-based mean-field approximation approach for efficient joint inference. Further, we develop a hierarchical model to incorporate region-level object and attribute information. Experiments on the aPASCAL, CORE and attribute augmented NYU indoor scenes datasets show that the proposed approach is able to achieve state-of-the-art results.},
	keywords = {Attributes, Image segmentation, Object recognition, Scene Understanding},
	isbn = {9781479951178},
	issn = {10636919},
	doi = {10.1109/CVPR.2014.411},
	url = {http://www.robots.ox.ac.uk/\~{}tvg/http://tu-dresden.de/inf/cvld},
	author = {Zheng, Shuai and Cheng, Ming Ming and Warrell, Jonathan and Sturgess, Paul and Vineet, Vibhav and Carsten Rother and Torr, Philip H.S.}
}
@conference {Hornacek2013,
	title = {Depth super resolution by rigid body self-similarity in 3D},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	year = {2013},
	pages = {1123{\textendash}1130},
	abstract = {We tackle the problem of jointly increasing the spatial resolution and apparent measurement accuracy of an input low-resolution, noisy, and perhaps heavily quantized depth map. In stark contrast to earlier work, we make no use of ancillary data like a color image at the target resolution, multiple aligned depth maps, or a database of high-resolution depth exemplars. Instead, we proceed by identifying and merging patch correspondences within the input depth map itself, exploiting patch wise scene self-similarity across depth such as repetition of geometric primitives or object symmetry. While the notion of {\textquoteright}single-image{\textquoteright} super resolution has successfully been applied in the context of color and intensity images, we are to our knowledge the first to present a tailored analogue for depth images. Rather than reason in terms of patches of 2D pixels as others have before us, our key contribution is to proceed by reasoning in terms of patches of 3D points, with matched patch pairs related by a respective 6 DoF rigid body motion in 3D. In support of obtaining a dense correspondence field in reasonable time, we introduce a new 3D variant of Patch Match. A third contribution is a simple, yet effective patch up scaling and merging technique, which predicts sharp object boundaries at the target resolution. We show that our results are highly competitive with those of alternative techniques leveraging even a color image at the target resolution or a database of high-resolution depth exemplars. {\textcopyright} 2013 IEEE.},
	keywords = {dense matching, depth super resolution, optimization},
	issn = {10636919},
	doi = {10.1109/CVPR.2013.149},
	author = {Horn{\'a}{\v c}ek, Michael and Rhemann, Christoph and Gelautz, Margrit and Carsten Rother}
}
@techreport {Schmidt,
	title = {Discriminative Non-blind Deblurring},
	year = {2013},
	abstract = {Non-blind deblurring is an integral component of blind approaches for removing image blur due to camera shake. Even though learning-based deblurring methods exist, they have been limited to the generative case and are compu-tationally expensive. To this date, manually-defined models are thus most widely used, though limiting the attained restoration quality. We address this gap by proposing a dis-criminative approach for non-blind deblurring. One key challenge is that the blur kernel in use at test time is not known in advance. To address this, we analyze existing approaches that use half-quadratic regularization. From this analysis, we derive a discriminative model cascade for image deblurring. Our cascade model consists of a Gaus-sian CRF at each stage, based on the recently introduced regression tree fields. We train our model by loss minimization and use synthetically generated blur kernels to generate training data. Our experiments show that the proposed approach is efficient and yields state-of-the-art restoration quality on images corrupted with synthetic and real blur.},
	author = {Schmidt, Uwe and Carsten Rother and Nowozin, Sebastian and Jancsary, Jeremy and Roth, Stefan}
}
@conference {Nowozin2011a,
	title = {Decision tree fields},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	year = {2011},
	pages = {1668{\textendash}1675},
	abstract = {This paper introduces a new formulation for discrete image labeling tasks, the Decision Tree Field (DTF), that combines and generalizes random forests and conditional random fields (CRF) which have been widely used in computer vision. In a typical CRF model the unary potentials are derived from sophisticated random forest or boosting based classifiers, however, the pairwise potentials are assumed to (1) have a simple parametric form with a pre-specified and fixed dependence on the image data, and (2) to be defined on the basis of a small and fixed neighborhood. In contrast, in DTF, local interactions between multiple variables are determined by means of decision trees evaluated on the image data, allowing the interactions to be adapted to the image content. This results in powerful graphical models which are able to represent complex label structure. Our key technical contribution is to show that the DTF model can be trained efficiently and jointly using a convex approximate likelihood function, enabling us to learn over a million free model parameters. We show experimentally that for applications which have a rich and complex label structure, our model achieves excellent results. {\textcopyright} 2011 IEEE.},
	isbn = {9781457711015},
	doi = {10.1109/ICCV.2011.6126429},
	author = {Nowozin, Sebastian and Carsten Rother and Bagon, Shai and Sharp, Toby and Yao, Bangpeng and Kohli, Pushmeet}
}
@conference {Sellen2007,
	title = {Do life-logging technologies support memory for the past? An experimental study using sensecam},
	booktitle = {Conference on Human Factors in Computing Systems - Proceedings},
	year = {2007},
	pages = {81{\textendash}90},
	abstract = {We report on the results of a study using SenseCam, a "life- logging" technology in the form of a wearable camera, which aims to capture data about everyday life in order to support people{\textquoteright}s memory for past, personal events. We find evidence that SenseCam images do facilitate people{\textquoteright}s ability to connect to their past, but that images do this in different ways. We make a distinction between "remembering" the past, and "knowing" about it, and provide evidence that SenseCam images work differently over time in these capacities. We also compare the efficacy of user-captured images with automatically captured images and discuss the implications of these findings and others for how we conceive of and make claims about life-logging technologies. {\textcopyright} Copyright 2007 ACM.},
	keywords = {Capture, Episodic or autobiographical memory, Images, Life-logging, Personal digital archives, SenseCam},
	isbn = {1595935932},
	doi = {10.1145/1240624.1240636},
	author = {Sellen, Abigail and Fogg, Andrew and Aitken, Mike and Hodges, Steve and Carsten Rother and Wood, Ken}
}
@conference {Rother2005,
	title = {Digital tapestry [automatic image synthesis]},
	booktitle = {Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on},
	volume = {1},
	year = {2005},
	pages = {589{\textendash}596},
	abstract = {This paper addresses the novel problem of automatically synthesizing an output image from a large collection of different input images. The synthesized image, called a digital tapestry, can be viewed as a visual summary or a virtual {\textquoteright}thumbnail{\textquoteright} of all the images in the input collection. The problem of creating the tapestry is cast as a multi-class labeling problem such that each region in the tapestry is constructed from input image blocks that are salient and such that neighboring blocks satisfy spatial compatibility. This is formulated using a Markov random field and optimized via the graph cut based expansion move algorithm. The standard expansion move algorithm can only handle energies with metric terms, while our energy contains non-metric (soft and hard) constraints. Therefore we propose two novel contributions. First, we extend the expansion move algorithm for energy functions with non-metric hard constraints. Secondly, we modify it for functions with "almost" metric soft terms, and show that it gives good results in practice. The proposed framework was tested on several consumer photograph collections, and the results are presented.},
	isbn = {0-7695-2372-2},
	doi = {10.1109/CVPR.2005.130},
	url = {http://research.microsoft.com/ http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1467321\%5Cnhttp://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1467321},
	author = {Carsten Rother and Kumar, Sanjiv and Kolmogorov, Vladimir and Blake, Andrew}
}
