Abstract
The paper describes a simplified representation of a body structure and a GMM based method for inferring from the motion capture data based on a functional relationship between the points. The proposed representation can be efficiently used for marker-wise processing of the data. The parent-child and sibling relationships are inferred on a coherence of movement and constancy of distances. For creating groups representing specific body parts we propose an incremental multicriterial clustering algorithm employing Gaussian mixture models. To infer body parts hierarchy we propose utilizing a consensus method.
| Original language | English |
|---|---|
| Pages (from-to) | 71-82 |
| Number of pages | 12 |
| Journal | Applied Mathematics and Information Sciences |
| Volume | 10 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2016 |
Keywords
- Body analysis
- Clustering
- Gaussian Mixture Model
- Motion capture
ASJC Scopus subject areas
- Analysis
- Numerical Analysis
- Computer Science Applications
- Computational Theory and Mathematics
- Applied Mathematics
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