caa a22 4500 467914516 CHVBK 20180406152858.0 cr unu---uuuuu 170328e20060501xx s 000 0 eng 10.1007/s11263-006-5165-4 doi (NATIONALLICENCE)springer-10.1007/s11263-006-5165-4 Combining Generative and Discriminative Models in a Framework for Articulated Pose Estimation [Elektronische Daten] [RÓMer Rosales, Stan Sclaroff] We develop a method for the estimation of articulated pose, such as that of the human body or the human hand, from a single (monocular) image. Pose estimation is formulated as a statistical inference problem, where the goal is to find a posterior probability distribution over poses as well as a maximum a posteriori (MAP) estimate. The method combines two modeling approaches, one discriminative and the other generative. The discriminative model consists of a set of mapping functions that are constructed automatically from a labeled training set of body poses and their respective image features. The discriminative formulation allows for modeling ambiguous, one-to-many mappings (through the use of multi-modal distributions) that may yield multiple valid articulated pose hypotheses from a single image. The generative model is defined in terms of a computer graphics rendering of poses. While the generative model offers an accurate way to relate observed (image features) and hidden (body pose) random variables, it is difficult to use it directly in pose estimation, since inference is computationally intractable. In contrast, inference with the discriminative model is tractable, but considerably less accurate for the problem of interest. A combined discriminative/generative formulation is derived that leverages the complimentary strengths of both models in a principled framework for articulated pose inference. Two efficient MAP pose estimation algorithms are derived from this formulation; the first is deterministic and the second non-deterministic. Performance of the framework is quantitatively evaluated in estimating articulated pose of both the human hand and human body. Springer Science + Business Media, Inc, 2006 human body pose nationallicence hand pose nationallicence nonrigid and articulated pose estimation nationallicence statistical inference nationallicence generative and discriminative models nationallicence mixture models nationallicence expectation maximization algorithm nationallicence Rosales RÓMer Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 02139, Cambridge, MA, USA aut Sclaroff Stan Image and Video Computing Group, Dept. of Computer Science, Boston University, 02215, Boston, MA, USA aut International Journal of Computer Vision Kluwer Academic Publishers 67/3(2006-05-01), 251-276 0920-5691 67:3<251 2006 67 11263 https://doi.org/10.1007/s11263-006-5165-4 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11263-006-5165-4 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Rosales RÓMer Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 02139, Cambridge, MA, USA aut NATIONALLICENCE 700 1- Sclaroff Stan Image and Video Computing Group, Dept. of Computer Science, Boston University, 02215, Boston, MA, USA aut NATIONALLICENCE 773 0- International Journal of Computer Vision Kluwer Academic Publishers 67/3(2006-05-01), 251-276 0920-5691 67:3<251 2006 67 11263 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer