Y. Chen, L. Zhu, C. Lin, A. L. Yuille, and H. Zhang, Rapid inference on a novel and/or graph for object detection, segmentation and parsing, NIPS, 2007.

P. Felzenszwalb, D. Mcallester, and D. Ramanan, A discriminatively trained, multiscale, deformable part model, 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008.
DOI : 10.1109/CVPR.2008.4587597

P. F. Felzenszwalb, R. B. Girshick, and D. Mcallester, Discriminatively trained deformable part models, release 4

P. F. Felzenszwalb, R. B. Girshick, and D. A. Mcallester, Cascade object detection with deformable part models, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010.
DOI : 10.1109/CVPR.2010.5539906

P. F. Felzenszwalb and D. P. Huttenlocher, Distance transforms of sampled functions, 2004.

V. Ferrari, M. J. Marin-jimenez, and A. Zisserman, Progressive search space reduction for human pose estimation, 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008.
DOI : 10.1109/CVPR.2008.4587468

F. Fleuret and D. Geman, Coarse-to-fine face detection. IJCV, 2001.

A. G. Gray and A. W. Moore, Nonparametric Density Estimation: Toward Computational Tractability, SIAM International Conference on Data Mining, 2003.
DOI : 10.1137/1.9781611972733.19

E. Grimson, Object Recognition by Computer, 1991.

D. Huttenlocher, G. Klanderman, W. , and R. , Comparing images using the Hausdorff distance, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.15, issue.9, pp.850-863, 1993.
DOI : 10.1109/34.232073

A. T. Ihler, E. B. Sudderth, W. T. Freeman, and A. S. Willsky, Efficient multiscale sampling from products of gaussian mixtures, NIPS, 2003.

I. Kokkinos, Rapid deformable object detection using dual tree branch and bound, NIPS, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00857520

I. Kokkinos and A. Yuille, HOP: Hierarchical object parsing, 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009.
DOI : 10.1109/CVPR.2009.5206639

I. Kokkinos and A. L. Yuille, Inference and Learning with Hierarchical Shape Models, International Journal of Computer Vision, vol.18, issue.2, pp.201-225, 2011.
DOI : 10.1007/s11263-010-0398-7

URL : https://hal.archives-ouvertes.fr/hal-00857538

C. Lampert, M. Blaschko, and T. Hofmann, Beyond sliding windows: Object localization by efficient subwindow search, 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008.
DOI : 10.1109/CVPR.2008.4587586

C. H. Lampert, An efficient divide-and-conquer cascade for nonlinear object detection, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010.
DOI : 10.1109/CVPR.2010.5540107

A. Lehmann, B. Leibe, and L. V. , Fast PRISM: Branch and Bound Hough Transform for Object Class Detection, International Journal of Computer Vision, vol.57, issue.2, pp.175-197, 2011.
DOI : 10.1007/s11263-010-0342-x

V. Lempitsky, A. Blake, and C. Rother, Image Segmentation by Branch-and-Mincut, ECCV, 2008.
DOI : 10.1007/978-3-540-88693-8_2

P. Moreels, M. Maire, and P. Perona, Recognition by Probabilistic Hypothesis Construction, ECCV, p.55, 2004.
DOI : 10.1007/978-3-540-24670-1_5

M. Pedersoli, A. Vedaldi, and J. Gonzàlez, A coarse-to-fine approach for fast deformable object detection, CVPR, 2011.

B. Sapp, A. Toshev, and B. Taskar, Cascaded Models for Articulated Pose Estimation, ECCV, 2010.
DOI : 10.1007/978-3-642-15552-9_30

P. Viola and M. Jones, Rapid object detection using a boosted cascade of simple features, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, 2001.
DOI : 10.1109/CVPR.2001.990517

S. C. Zhu and D. Mumford, Quest for a Stochastic Grammar of Images. Foundations and Trends in Computer Graphics and Vision, pp.259-362, 2007.