K. Cleary, H. Y. Chung, and S. K. Mun, OR 2020: The operating room of the future, Laparoendoscopic and Advanced Surgical Techniques, pp.495-500, 2005.

P. Jannin and X. Morandi, Surgical models for computer-assisted neurosurgery, NeuroImage, vol.37, issue.3, pp.783-91, 2007.
DOI : 10.1016/j.neuroimage.2007.05.034
URL : https://hal.archives-ouvertes.fr/inserm-00185435

B. Bhatia, T. Oates, Y. Xiao, and P. Hu, Real-time identification of operating room state from video, pp.1761-1766, 2007.

N. Padoy, T. Blum, H. Feuner, M. O. Berger, and N. Navab, On-line recognition of surgical activity for monitoring in the operating room, Proc's of the 20 th Conference on Innovative Applications of Artificial Intelligence, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00331390

S. Speidel, G. Sudra, J. Senemaud, M. Drentschew, B. Müller-stich et al., Situation modeling and situation recognition for a context-aware augmented reality system, Progression in biomedical optics and imaging, p.35, 2008.

B. Lo, A. Darzi, and G. Yang, Episode Classification for the Analysis of Tissue/Instrument Interaction with Multiple Visual Cues, International Conference on Medical Image Computing and Computer-Assisted Intervention, 2003.
DOI : 10.1007/978-3-540-39899-8_29

U. Klank, N. Padoy, H. Feussner, and N. Navab, Automatic feature generation in endoscopic images, International Journal of Computer Assisted Radiology and Surgery, vol.3, issue.3-4, pp.3-4, 2008.
DOI : 10.1007/s11548-008-0223-8

T. Blum, H. Feussner, and N. Navab, Modeling and Segmentation of Surgical Workflow from Laparoscopic Video, International Conference on Medical Image Computing and Computer-Assisted Intervention, 2010.
DOI : 10.1007/978-3-642-15711-0_50

S. Voros and G. Hager, Towards “real-time” tool-tissue interaction detection in robotically assisted laparoscopy, 2008 2nd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics, pp.562-567, 2008.
DOI : 10.1109/BIOROB.2008.4762915

C. E. Reiley and G. D. Hager, Decomposition of robotic surgical tasks: an analysis of subtasks and their correlation to skill, M2CAI workshop. MICCAI, 2009.

F. Lalys, L. Riffaud, X. Morandi, and P. Jannin, Surgical Phases Detection from Microscope Videos by Combining SVM and HMM, Medical Comp Vision Workshop, 2010.
DOI : 10.1007/978-3-642-18421-5_6
URL : https://hal.archives-ouvertes.fr/inserm-00616981

M. Swain and D. Ballard, Color indexing, International Journal of Computer Vision, vol.31, issue.1, pp.11-32, 1991.
DOI : 10.1007/BF00130487

V. Hough, Machine Analysis of Bubble Chamber Pictures, Proc. Int. Conf. High Energy Accelerators and Instrumentation, 1959.

A. Smeulders, M. Worrin, S. Santini, A. Gupta, and R. Jain, Content-based image retrieval at the end of the early years, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.12, pp.1349-1380, 2000.
DOI : 10.1109/34.895972

D. Lowe, Object recognition from local scale-invariant features, Proceedings of the Seventh IEEE International Conference on Computer Vision, pp.1150-1157, 1999.
DOI : 10.1109/ICCV.1999.790410

H. Bay, T. Tuytelaars, and L. Van-gool, SURF: Speeded Up Robust Features, Computer Vision ? ECCV, 2006.

C. Harris and M. Stephens, A combined corner and edge detector. Alvey vision conference, 1988.

M. Agrawal and K. Konolige, CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching, European Conf Comput Vision, ECCV'08, pp.102-115, 2008.
DOI : 10.1007/978-3-540-88693-8_8

P. Viola and M. Jones, Rapid real-time face detection, IJCV, pp.137-154, 2004.

Y. Freund and R. Schapire, A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting, Proc. of the 2 nd European conf on Computational Learning Theory, 1995.
DOI : 10.1006/jcss.1997.1504

C. Papageorgiou, M. Oren, and T. Poggio, A general framework for object detection, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271), 1998.
DOI : 10.1109/ICCV.1998.710772

R. Haralick, K. Shanmugam, and I. Dinstein, Textural Features for Image Classification, IEEE Transactions on Systems, Man, and Cybernetics, vol.3, issue.6, pp.61-621, 1973.
DOI : 10.1109/TSMC.1973.4309314

M. Hu, Visual pattern recognition by moment invariants, Trans Inf Theory, vol.8, issue.2, pp.79-87, 1962.

N. Ahmed, T. Natarajan, and R. Rao, Discrete Cosine Transform, IEEE Transactions on Computers, vol.23, issue.1, pp.90-93, 1974.
DOI : 10.1109/T-C.1974.223784

R. Duda and P. Hart, Pattern classification and scene analysis, 1973.

M. W. Mak and S. Y. Kung, Fusion of feature selection methods for pairwise scoring SVM, Neurocomputing, vol.71, issue.16-18, pp.3104-3113, 2008.
DOI : 10.1016/j.neucom.2008.04.024

I. Guyon, J. Weston, S. Barhill, and V. Vapnik, Gene selection for cancer classification using support vector machine, Machine Learning, pp.389-422, 2002.

R. W. Hamming, Coding and Information Theory, 1980.

F. Lalys, L. Riffaud, X. Morandi, and P. Jannin, Automatic Phases Recognition in Pituitary Surgeries by Microscope Images Classification, 1th Int Conf Inform Proc Comp Assist Interv, IPCAI'2010, 2010.
DOI : 10.1007/978-3-642-13711-2_4
URL : https://hal.archives-ouvertes.fr/inserm-00616977

L. Rabiner, A tutorial on Hidden Markov Models and selected applications in speech recognition, Proc of IEEE, vol.77, issue.2, 1989.

A. Viterbi, Errors bounds for convolutional codes, IEEE TIT, vol.13, issue.2, pp.260-269, 1967.

E. Keogh and M. Pazzani, An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback. Prediction of the future: AI approaches to time-series problems, pp.44-51, 1998.

K. Wang and T. Gasser, Alignment of curves by dynamic time warping, Annals of Statistics, vol.25, issue.3, pp.1251-1276, 1997.

V. Niennattrakkul and C. Ratanamahatana, Learning DTW global constraint for time series classification, Artificial Intelligence papers, 1999.