Rate-Invariant Analysis of Trajectories on Riemannian Manifolds with Application in Visual Speech Recognition

By Jingyong Su, Anuj Srivastava, Fillipe D. M. de Souza, Sudeep Sarkar. 2014

46 citations recorded by the source corpus.

Subfields: Geometry and Topology, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Geometry and Topology

1. Boulbaba Ben Amor
2. Jingyong Su
3. Anuj Srivastava

In Signal Processing

1. William A. Hoff
2. Brian Reily
3. Fei Han
4. Hao Zhang

Citations in the corpus

• Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, methodology): “used a parameterization-invariant metric [218] for aligning, comparing, and modeling skeleton joint trajectories, which can deal with noise caused by large variability of execution rates within and across humans.”
• Pattern Recognition (2015, unknown): “All the action videos are trimmed.”
• Action Recognition Using Rate-Invariant Analysis of Skeletal Shape Trajectories (2016, methodology): “They use a Gaussian mixture model (GMM) to capture the statistical distribution of the postures and demonstrate the superiority of their approach over the 2D silhouette-based approach.”