Active shape models—their training and application
Computer Vision and Image Understanding
International Journal of Computer Vision
Discrete-time signal processing (2nd ed.)
Discrete-time signal processing (2nd ed.)
Statistical models in medical image analysis
Statistical models in medical image analysis
Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation
International Journal of Computer Vision
Dynamical Statistical Shape Priors for Level Set-Based Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Numerical Recipes with Source Code CD-ROM 3rd Edition: The Art of Scientific Computing
Numerical Recipes with Source Code CD-ROM 3rd Edition: The Art of Scientific Computing
A Framework for Image Segmentation Using Shape Models and Kernel Space Shape Priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Extraction & Image Processing, Second Edition
Feature Extraction & Image Processing, Second Edition
Novel temporal views of moving objects for gait biometrics
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
IEEE Transactions on Image Processing
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We describe a new technique to extract the boundary of a walking subject, with ability to predict movement in missing frames. This paper uses a level sets representation of the training shapes and uses an interpolating cubic spline to model the eigenmodes of implicit shapes. Our contribution is to use a continuous representation of the feature space variation with time. The experimental results demonstrate that this level set-based technique can be used reliably in reconstructing the training shapes, estimating in-between frames to help in synchronizing multiple cameras, compensating for missing training sample frames, and the recognition of subjects based on their gait.