@conference {Hoiem2007,
	title = {3D LayoutCRF for multi-view object class recognition and segmentation},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	year = {2007},
	abstract = {We introduce an approach to accurately detect and segment partially occluded objects in various viewpoints and scales. Our main contribution is a novel framework for combining object-level descriptions (such as position, shape, and color) with pixel-level appearance, boundary, and occlusion reasoning. In training, we exploit a rough 3D object model to learn physically localized part appearances. To find and segment objects in an image, we generate proposals based on the appearance and layout of local parts. The proposals are then refined after incorporating object-level information, and overlapping objects compete for pixels to produce a final description and segmentation of objects in the scene. A further contribution is a novel instance penalty, which is handled very efficiently during inference. We experimentally validate our approach on the challenging PASCAL{\textquoteright}06 car database. {\textcopyright} 2007 IEEE.},
	isbn = {1424411807},
	issn = {10636919},
	doi = {10.1109/CVPR.2007.383045},
	author = {Hoiem, Derek and Carsten Rother and Winn, John}
}
@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}
}
