@conference {Gulshan2010,
	title = {Geodesic star convexity for interactive image segmentation},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	year = {2010},
	pages = {3129{\textendash}3136},
	abstract = {In this paper we introduce a new shape constraint for interactive image segmentation. It is an extension of Veksler{\textquoteright}s [25] star-convexity prior, in two ways: from a single star to multiple stars and from Euclidean rays to Geodesic paths. Global minima of the energy function are obtained subject to these new constraints. We also introduce Geodesic Forests, which exploit the structure of shortest paths in implementing the extended constraints. The star-convexity prior is used here in an interactive setting and this is demonstrated in a practical system. The system is evaluated by means of a "robot user" to measure the amount of interaction required in a precise way. We also introduce a new and harder dataset which augments the existing Grabcut dataset [1] with images and ground truth taken from the PASCAL VOC segmentation challenge [7]. {\textcopyright}2010 IEEE.},
	isbn = {9781424469840},
	issn = {10636919},
	doi = {10.1109/CVPR.2010.5540073},
	author = {Gulshan, Varun and Carsten Rother and Criminisi, Antonio and Blake, Andrew and Zisserman, Andrew}
}
@conference {Shesh2009,
	title = {3D-aware image editing for out of bounds photography},
	booktitle = {Proceedings - Graphics Interface},
	year = {2009},
	pages = {47{\textendash}54},
	abstract = {In this paper, we propose algorithms to manipulate 2D images in a way that is consistent with the 3D geometry of the scene that they capture. We present these algorithms in the context of creating "Out of Bounds" (OOB) images - compelling, depth-rich images generated from single, conventional 2D photographs (fig. 1). Starting from a single image our tool enables rapid OOB prototyping; i.e. the ability to quickly create and experiment with many different variants of the OOB effect before deciding which one best expresses the users{\textquoteright} artistic intentions. We achieve this with a flexible work-flow driven by an intuitive user interface. The rich 3D perception of the final composition is achieved by exploiting two strong cues - occlusions and shadows. A realistic-looking 3D frame is interactively inserted in the scene between segmented foreground objects and the background to generate novel occlusions and enhance the scene{\textquoteright}s perception of depth. This perception is further enhanced by adding new, realistic cast shadows. The key contributions of this paper are: (i) new algorithms for inserting simple 3D objects like frames in 2D images requiring minimal camera calibration, and (ii) new techniques for the realistic synthesis of cast shadows, even for complex 3D objects. These algorithms, although presented for OOB photography, may be directly used in general image composition tasks. With our tool, untrained users can turn ordinary photos into compelling OOB images in seconds. In contrast with existing workflows, at any time the artist can modify any aspect of the composition while avoiding time-consuming pixel painting operations. Such a tool has important commercial applications, and is much more suitable for OOB prototyping than existing image editors.},
	isbn = {9781568814704},
	issn = {07135424},
	url = {http://www.flickr.com/groups/oob/},
	author = {Shesh, Amit and Criminisi, Antonio and Carsten Rother and Smyth, Gavin}
}
@article {Shotton2009,
	title = {TextonBoost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context},
	journal = {International Journal of Computer Vision},
	volume = {81},
	number = {1},
	year = {2009},
	pages = {2{\textendash}23},
	abstract = {This paper details a new approach for learning a discriminative model of object classes, incorporating texture, layout, and context information efficiently. The learned model is used for automatic visual understanding and semantic segmentation of photographs. Our discriminative model exploits texture-layout filters, novel features based on textons, which jointly model patterns of texture and their spatial layout. Unary classification and feature selection is achieved using shared boosting to give an efficient classifier which can be applied to a large number of classes. Accurate image segmentation is achieved by incorporating the unary classifier in a conditional random field, which (i) captures the spatial interactions between class labels of neighboring pixels, and (ii) improves the segmentation of specific object instances. Efficient training of the model on large datasets is achieved by exploiting both random feature selection and piecewise training methods. High classification and segmentation accuracy is demonstrated on four varied databases: (i) the MSRC 21-class database containing photographs of real objects viewed under general lighting conditions, poses and viewpoints, (ii) the 7-class Corel subset and (iii) the 7-class Sowerby database used in He et al. (Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 695-702, June 2004), and (iv) a set of video sequences of television shows. The proposed algorithm gives competitive and visually pleasing results for objects that are highly textured (grass, trees, etc.), highly structured (cars, faces, bicycles, airplanes, etc.), and even articulated (body, cow, etc.). {\textcopyright} 2007 Springer Science+Business Media, LLC.},
	keywords = {Boosting, Conditional random field, Context, image understanding, Layout, Object recognition, Piecewise training, Segmentation, Semantic image segmentation, Textons, Texture},
	issn = {09205691},
	doi = {10.1007/s11263-007-0109-1},
	url = {http://jamie.shotton.org/work/code.html},
	author = {Shotton, Jamie and Winn, John and Carsten Rother and Criminisi, Antonio}
}
@conference {Lalonde2007,
	title = {Photo clip art},
	booktitle = {Proceedings of the ACM SIGGRAPH Conference on Computer Graphics},
	year = {2007},
	abstract = {We present a system for inserting new objects into existing photographs by querying a vast image-based object library, pre-computed using a publicly available Internet object database. The central goal is to shield the user from all of the arduous tasks typically involved in image compositing. The user is only asked to do two simple things: 1) pick a 3D location in the scene to place a new object; 2) select an object to insert using a hierarchical menu. We pose the problem of object insertion as a data-driven, 3D-based, context-sensitive object retrieval task. Instead of trying to manipulate the object to change its orientation, color distribution, etc. to fit the new image, we simply retrieve an object of a specified class that has all the required properties (camera pose, lighting, resolution, etc) from our large object library. We present new automatic algorithms for improving object segmentation and blending, estimating true 3D object size and orientation, and estimating scene lighting conditions. We also present an intuitive user interface that makes object insertion fast and simple even for the artistically challenged. {\textcopyright} 2007 ACM.},
	keywords = {3D scene reasoning, Blending and compositing, Computational photography, Image databases, Object insertion},
	doi = {10.1145/1275808.1276381},
	url = {http://graphics.cs.cmu.edu/projects/photoclipart/},
	author = {Lalonde, Jean Fran{\c c}ois and Hoiem, Derek and Efros, Alexei A and Carsten Rother and Winn, John and Criminisi, Antonio}
}
@article {Kolmogorov2006,
	title = {Probabilistic fusion of stereo with color and contrast for bilayer segmentation},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {28},
	number = {9},
	year = {2006},
	pages = {1480{\textendash}1492},
	abstract = {This paper describes models and algorithms for the real-time segmentation of foreground from background layers in stereo video sequences. Automatic separation of layers from color/contrast or from stereo alone is known to be error-prone. Here, color, contrast, and stereo matching information are fused to infer layers accurately and efficiently. The first algorithm, Layered Dynamic Programming (LDP), solves stereo in an extended six-state space that represents both foreground/background layers and occluded regions. The stereo-match likelihood is then fused with a contrast-sensitive color model that is learned on-the-fly and stereo disparities are obtained by dynamic programming. The second algorithm, Layered Graph Cut (LGC), does not directly solve stereo. Instead, the stereo match likelihood is marginalized over disparities to evaluate foreground and background hypotheses and then fused with a contrast-sensitive color model like the one used in LDP. Segmentation is solved efficiently by ternary graph cut. Both algorithms are evaluated with respect to ground truth data and found to have similar performance, substantially better than either stereo or color/contrast alone. However, their characteristics with respect to computational efficiency are rather different. The algorithms are demonstrated in the application of background substitution and shown to give good quality composite video output. {\textcopyright} 2006 IEEE.},
	keywords = {3D/stereo scene analysis, Computer vision, Dynamic programming, Image processing and computer vision, Parameter learning},
	issn = {01628828},
	doi = {10.1109/TPAMI.2006.193},
	url = {http://research.microsoft.com/vision/cambridge},
	author = {Kolmogorov, Vladimir and Criminisi, Antonio and Blake, Andrew and Cross, Geoffrey and Carsten Rother}
}
@conference {Shotton2006,
	title = {TextonBoost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {3951 LNCS},
	year = {2006},
	pages = {1{\textendash}15},
	abstract = {This paper proposes a new approach to learning a discriminative model of object classes, incorporating appearance, shape and context information efficiently. The learned model is used for automatic visual recognition and semantic segmentation of photographs. Our discriminative model exploits novel features, based on textons, which jointly model shape and texture. Unary classification and feature selection is achieved using shared boosting to give an efficient classifier which can be applied to a large number of classes. Accurate image segmentation is achieved by incorporating these classifiers in a conditional random field. Efficient training of the model on very large datasets is achieved by exploiting both random feature selection and piecewise training methods. High classification and segmentation accuracy are demonstrated on three different databases: i) our own 21-object class database of photographs of real objects viewed under general lighting conditions, poses and viewpoints, ii) the 7-class Corel subset and iii) the 7-class Sowerby database used in [1]. The proposed algorithm gives competitive results both for highly textured (e.g. grass, trees), highly structured (e.g. cars, faces, bikes, aeroplanes) and articulated objects (e.g. body, cow). {\textcopyright} Springer-Verlag Berlin Heidelberg 2006.},
	keywords = {aeroplanes) and articulated objects (eg body, bikes, cow), faces, g grass, highly structured (eg cars, trees)},
	isbn = {3540338322},
	issn = {16113349},
	doi = {10.1007/11744023_1},
	url = {http://research.microsoft.com/vision/cambridge/recognition/.},
	author = {Shotton, Jamie and Winn, John and Carsten Rother and Criminisi, Antonio}
}
