Artificial neural networks for 3D nonrigid motion analysis

Author

Chen, Ting ; Lin, Wei-Chung ; Chen, Chin-Tu

Author_Institution

Dept. of Electr. Eng. & Comput. Sci., Northwestern Univ., Evanston, IL, USA

Volume

4

fYear

1992

fDate

7-11 Jun 1992

Firstpage

420

Abstract

A novel approach to 3D nonrigid motion analysis using artificial neural networks is presented. A set of neural networks is proposed to tackle the problem of nonrigidity in 3D motion estimation. Constraints are specified to ensure a stable and global consistent estimation of local deformations. The assignments of weights between two layers, the initial values of the outputs, and the connections between each network reflect the constraints defined. The objective of the proposed neural networks is to find the optimal deformation matrices that satisfy the constraints for all the points on the surface of the nonrigid object. Experimental results on synthetic and real data are provided

Keywords

motion estimation; recurrent neural nets; 3D motion estimation; 3D nonrigid motion analysis; artificial neural networks; initial values; local deformations; neural networks; nonrigid object; nonrigidity; optimal deformation matrices; weights assignments; Artificial neural networks; Contracts; Heart; Layout; Motion analysis; Motion estimation; Neural networks; Neurofeedback; Radiology; Shearing;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1992. IJCNN., International Joint Conference on

Conference_Location

Baltimore, MD

Print_ISBN

0-7803-0559-0

Type

conf

DOI

10.1109/IJCNN.1992.227308

Filename

227308