Issue No. 06 - June (2011 vol. 33)
Romeil Sandhu , Georgia Institute of Technology, Atlanta
Samuel Dambreville , Georgia Institute of Technology, Atlanta and the Boston Consulting Group
Anthony Yezzi , Georgia Institute of Technology, Atlanta
Allen Tannenbaum , Georgia Institute of Technology, Atlanta and Technion, Israel-Institute of Technology, Haifa
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. Thus, the novelty of our method is threefold: First, we present and derive a gradient flow for the task of nonrigid pose estimation and segmentation. Second, due to the possible nonlinear structures of one's training set, we evolve the preimage obtained through kernel PCA for the task of shape analysis. Third, we show that the derivation for shape weights is general. This allows us to use various kernels, as well as other statistical learning methodologies, with only minimal changes needing to be made to the overall shape evolution scheme. In contrast with other techniques, we approach the nonrigid problem, which is an infinite-dimensional task, with a finite-dimensional optimization scheme. More importantly, we do not explicitly need to know the interaction between various shapes such as that needed for skeleton models as this is done implicitly through shape learning. We provide experimental results on several challenging pose estimation and segmentation scenarios.
3D pose estimation, image segmentation, statistical learning, kernel PCA.
A. Yezzi, A. Tannenbaum, S. Dambreville and R. Sandhu, "A Nonrigid Kernel-Based Framework for 2D-3D Pose Estimation and 2D Image Segmentation," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 33, no. , pp. 1098-1115, 2010.