Fuzzy Quantile Inference (FQI) is a novel method that builds a simple and efficient connective between probabilistic and fuzzy paradigms and allows the classification of noisy, imprecise and complex motions while using learning samples of suboptimal size. A comparative study focusing on the recognition of multiple stances from 3d motion capture data is conducted. Results show that, when put to the test with a dataset presenting challenges such as real biologically "noisy" data, cross-gait differentials from one individual to another, and relatively high dimensionality (the skeletal representation has 57 degrees of freedom), FQI outperforms sixteen other known time-invariant classifiers. © 2010 Springer-Verlag.
CITATION STYLE
Khoury, M., & Liu, H. (2010). Recognizing 3D human motions using fuzzy quantile inference. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6424 LNAI, pp. 680–691). https://doi.org/10.1007/978-3-642-16584-9_65
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