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. © 2009 Springer-Verlag.
CITATION STYLE
Al-Huseiny, M. S., Mahmoodi, S., & Nixon, M. S. (2009). Level set gait analysis for synthesis and reconstruction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5876 LNCS, pp. 377–386). https://doi.org/10.1007/978-3-642-10520-3_35
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