3-D motion estimation, understanding, and prediction from nosiy image sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
Error characterization of the factorization method
Computer Vision and Image Understanding
Introductory Techniques for 3-D Computer Vision
Introductory Techniques for 3-D Computer Vision
DEFORMOTION: Deforming Motion, Shape Average and the Joint Registration and Segmentation of Images
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Gait Analysis for Recognition and Classification
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Uncertainty Modeling and Model Selection for Geometric Inference
IEEE Transactions on Pattern Analysis and Machine Intelligence
The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Measure of Deformability of Shapes, with Applications to Human Motion Analysis
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Direct Method for 3D Factorization of Nonrigid Motion Observed in 2D
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Factorization-Based Approach to Articulated Motion Recovery
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Articulated Structure from Motion by Factorization
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Matching Shape Sequences in Video with Applications in Human Movement Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A model for dynamic shape and its applications
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Modelling the effects of walking speed on appearance-based gait recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Non-rigid face modelling using shape priors
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
Identification of humans using gait
IEEE Transactions on Image Processing
Activity representation using 3D shape models
Journal on Image and Video Processing - Anthropocentric Video Analysis: Tools and Applications
Automatic estimation of the number of deformation modes in non-rigid SfM with missing data
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
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In this paper we develop a theory for characterizing how deformable a shape is given a sequence of its observations. We define a term called ''deformability index'' (DI) for shapes. The novelty of the proposed method lies in its ability to separate the effects of observation noise from the underlying non-rigid deformation process. The DI is computed from the tracked positions of a sequence of deformable shapes, using an affine camera model. Our method assumes that a deformable shape sequence can be represented by a linear combination of rigid basis shapes, where the weights assigned to each basis shape change with time. The tracked points obtained from the 2D shape sequence are transformed to a 3D shape space, whose dimension is then estimated using spectral analysis methods. The dimension of this shape space determines the number of basis shapes needed to represent the shape sequence, which, in turn, determines the deformability index. Our method is different from existing techniques since it is non-iterative, does not require setting an arbitrary threshold and is able to precisely model the effects of noise in the feature positions. Rigid 3D transformations of the shape are taken into account in estimating the DI; however, the method does not require estimation of 3D structure or motion. Experimental results show that the DI is in accordance with our intuitive judgment. Applications and comparative analysis on 3D deformable object modeling are also presented.