Project Proposal (PDF) - Oxford Brookes University

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FP7-ICT-2011-9 STREP proposal 18/01/12 v1 [Dynact] [98] E. Miranda and G. de Cooman. Marginal extension in the theory of coherent lower previsions, International Journal Of Approximate Reasoning 46 (2007) 188-225. [99] G. de Cooman and F. Hermans, Imprecise probability trees: Bridging two theories of imprecise probability, Artificial Intelligence 172 (2008) 1400-1427. [100] G. de Cooman, F. Hermans and E. Quaeghebeur, Imprecise Markov chains and their limit behaviour, Probability in the Engineering and Informational Sciences 23 (2009) 597-635. [101] M. Zaffalon, The naive credal classifier, Journal of Statistical Planning and Inference 105 (2002) 5-21. [102] G. Corani and M. Zaffalon, JNCC2: An extension of naive Bayes classifier suited for small and incomplete data sets, Environmental Modelling & Software 23 (2008) 960-961. [103] G. Corani and M. Zaffalon, JNCC2: The Java Implementation of Naive Credal Classifier 2, Journal of Machine Learning Research 9 (2008) 2695-2698. [104] H. Kuehne, H. Jhuang, E. Garrote, T. Poggio and T. Serre, HMDB: A large video database for human motion recognition, Proc. of ICCV'11, 2011. [105] D. Weinland, R. Ronfard and E. Boyer, A survey of vision-based methods for action representation, segmentation and recognition, Computer Vision and Image Understanding 115 (2011) 224 -241. [106] C. Schuldt, I. Laptev and B. Caputo, Recognizing human actions: A local SVM approach, Proc. of CVPR'04, pp. 32-36, 2004. [107] H. Wang, M.M. Ullah, A. Klaser, I. Laptev and C. Schmid, Evaluation of local spatio-temporal features for action recognition, Proc. of BMVC, 2009. [108] H. Jhuang, T. Serre, L. Wolf and T. Poggio, A biologically inspired system for action recognition, Proc. of ICCV, 2007. [109] M. Rodriguez, J. Ahmed and M. Shah, Action MACH: A spatio-temporal maximum average correlation height lter for action recognition, Proc. of CVPR, 2008. [110] G. Johanssen, Visual perception of biological motion and a model for its analysis, Perception & Psychophysics 14 (1973) 201-211. [111] M. Thirkettle, C.P. Benton and N.E. Scott-Samuel, Contributions of form, motion and task to biological motion perception, Journal of Vision 9 (2004) 1-11. [112] A. Klaser, M. Marszalek, C. Schmid and A. Zisserman, Human focused action localization in video, International Workshop on Sign, Gesture, Activity, 2010. [113] I. Laptev and P. Perez, Retrieving actions in movies, Proc. of ICCV, 2007. [114] Y. Ke, R. Sukthandar and M. Hebert, Volumetric features for video event detection, Int. Journal of Computer Vision 88 (2010) 339-362. [115] M. Fischler and R. Elschlager, The representation and matching of pictorial structures, IEEE Trans. Computer 22 (1973) 67-92. [116] P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan, Object detection with discriminatively trained part based models, IEEE Trans. Pattern Analysis and Machine Intelligence 32 (2010) 1627-1645. [117] H. Wang, A. Klaser, C. Schmid and C.L. Liu, Action Recognition by Dense Trajectories, Proc. of CVPR, pp. 3169-3176, 2011. [118] P. Viola, J. Platt and C. Zhang, Multiple instance boosting for object detection, Proc. of NIPS, 2005. [119] P. Dollar, V. Rabaud, G. Cottrell and S. Belongie, Behavior recognition via sparse spatio-temporal features, Proc. of the IEEE Int. Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pp. 65-72, 2005. [120] G. Willems, T. Tuytelaars and L.V. Gool, An efficient dense and scale-invariant spatio-temporal interest point detector, Proc. of ECCV, pp. 650-663, 2008. [121] P. Scovanner, S. Ali and M. Shah, A 3-dimensional SIFT descriptor and its application to action recognition, Proc. of ACM Multimedia, 2007. [122] A. Klaser, M. Marszalek and C. Schmid, A spatio-temporal descriptor based on 3D-gradients, Proc. of BMVC, 2008. [123] L. Yeet and L. Wolf, Local trinary patterns for human action recognition, Proc. of ICCV, 2009. [124] Q.V. Le, W.Y. Zou, S.Y. Yeung and A.Y. Ng, Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis, Proc. of CVPR, pp. 3361-3368, 2011. [125] P. Felzenszwalb and D. Huttenlocher, Pictorial structures for object recognition, Int. Journal of Computer Vision 61 (2005). [126] S. Andrews, I. Tsochantaridis and T. Hofmann, Support vector machines for multiple-instance learning, Proc. of NIPS, pp. 561-568, 2003. [127] P. Felzenszwalb, D. McAllester and D. Ramanan, A discriminatively trained, multiscale, deformable part model, Proc. of CVPR, 2008. <strong>Proposal</strong> Part B: page [66] of [67]
FP7-ICT-2011-9 STREP proposal 18/01/12 v1 [Dynact] [128] A. Sattar and R. Séguier, HGOAAM: Facial analysis by active appearance model optimized by hybrid genetic algorithm, Journal of Digital Information Management 7:4 (2009) 93-201. [129] A. Sattar and R. Seguier, HMOAM: Hybrid multi-objective genetic optimization for facial analysis by appearance model, Memetic Computing Journal 2:1 (2010) 25-46. [130]http://www.dynamixyz.com/main/index.php? option=com_content&view=article&id=51&Itemid=33&lang=en [131] T. Sénéchal, V. Rapp, H. Salam, R. Seguier, K. Bailly, L. Prevost, Combining LGBP histograms with AAM coefficients in the multi-kernel SVM framework to detect facial action units, Proc. of FG'11, 2011. [132] T. Sénéchal, V. Rapp, H. Salam, R. Seguier, K. Bailly and L. Prevost, Facial action recognition combining heterogeneous features via multi-kernel learning, IEEE Transactions on Systems, Man, and Cybernetics (2012), to appear. [133] http://www.rennes.supelec.fr/immemo/ [134]http://www.dynamixyz.com/main/index.php? option=com_content&view=article&id=53&Itemid=36&lang=en [135] O. Dumas, R. Séguier and S. De Gregorio, 3-D Modeling, US Patent n° 20080267449, 2008. [136] http://www.adnda.com/services_en.php [137] J. Palicot (edited by), Radio engineering: from software radio to cognitive radio, Wiley, 2011. [138] J. K. Aggarwal and M. S. Ryoo, Human Activity Analysis: A Review, ACM Computing Surveys 43:3 (2011) [139] Antonucci, A., Benavoli, A., Zaffalon, M., de Cooman, G., Hermans, F. (2009). Multiple model tracking by imprecise Markov trees. In FUSION 2009: Proceedings of the 12 th IEEE International Conference on Information Fusion. [140] Antonucci, A., de Rosa, R., Giusti, A. (2011). Action Recognition by Imprecise Hidden Markov Models. In Proceedings of the 2011 International Conference on Image Processing, Computer Vision and Pattern Recognition, IPCV 2011. CSREA Press, pp. 474-478. [141] Antonucci, A., de Rosa, R. (2011). Time Series Classification by Imprecise Hidden Markov Models. In Proceedings of the 21th Italian Workshop on Neural Networks (WIRN 2011). [142] A. Van Camp and G. de Cooman, A new method for learning imprecise hidden Markov models, accepted by IPMU 2012. [143] A. Antonucci, M. Cattaneo, and G. Corani. The Naive Hierarchical Credal Classifier. In ISIPTA '11: Proceedings of the seventh International Symposium on Imprecise Probability: Theories and Applications. SIPTA, pp. 21-30, 2011. [144] A. Antonucci, M. Cattaneo, and G. Corani. Likelihood-Based Robust Classification with Bayesian Networks. In IPMU 2012: Proceedings of the 14th International Conference on Information Processing and Management in Knowledge-based Systems. Springer, accepted for publication. [145] J. Rodriguez, C. Alonso, J. Maestro. Support vector machines of interval-based features for time series classification. Knowledge-Based Systems, 18 (4-5), 171-178, 2005. [146] L. Utkin, F. Coolen, Interval-valued regression and classification models in the framework of machine learning. In Proceedings of ISIPTA'11, SIPTA, 2011. <strong>Proposal</strong> Part B: page [67] of [67]
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