Romeil Sandhu, Sam Dambreville, Tony Yezzi, and Allen Tannenbaum
Pattern Analysis and Machine Intelligence, IEEE Transactions on 33.6 (2011): 1098-1115.
Publication year: 2011

In this work, we present a nonrigid approach to jointly solving the tasks of 2D-3D pose estimation and 2D image segmentation. In general, most frameworks that couple both pose estimation and segmentation assume that one has exact knowledge of the 3D object. However, under nonideal conditions, this assumption may be violated if only a general class to which a given shape belongs is given (e.g., cars, boats, or planes). Thus, we propose to solve the 2D-3D pose estimation and 2D image segmentation via nonlinear manifold learning of 3D embedded shapes for a general class of objects or deformations for which one may not be able to associate a skeleton model. In contrast with other techniques, we approach the nonrigid problem, which is an infinite- dimensional task, with a finite-dimensional optimization scheme. We provide experimental results on several challenging pose estimation and segmentation scenarios