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We describe a mechanism based upon activity manifolds that map image data from more than one view to spatial pose. We learn the manifolds from training data which are motion capture data about real human subjects exercising the target actions. The nature of the training data allows the learned manifolds to conform naturally to multiple constraints, including (1) the body-part articulation constraint;...
Deformable registration of multi-modality medical image remains a challenging research topic. The incorporation of prior information on the expected joint distribution has shown to noticeably improve registration accuracy and robustness. However, direct application of the learned joint histogram makes the algorithm sensitive to the difference between the training data and the test image. This paper...
This work introduces a new representation for Motion Capture data (MoCap) that is invariant under rigid transformation and robust for classification and annotation of MoCap data. This representation relies on distance matrices that fully characterize the class of identical postures up to the body position or orientation. This high dimensional feature descriptor is tailored using PCA and incorporated...
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