Gesture Modeling by Hanklet-Based Hidden Markov Model
By Liliana Lo Presti, Marco La Cascia, Stan Sclaroff, Octavia I. Camps. 2014
47 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Human-Computer Interaction, 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 Computer Vision and Pattern Recognition
- Liliana Lo Presti
- Marco La Cascia
- Pietro Morerio
- Octavia I. Camps
- Jacopo Cavazza
- Mario Sznaier
- Vittorio Murino
- Mengran Gou
In Human-Computer Interaction
- Liliana Lo Presti
- Marco La Cascia
- Vittorio Murino
- Pietro Morerio
- Jacopo Cavazza
- Mario Sznaier
- Octavia I. Camps
- Ivan Poupyrev
In Signal Processing
Citations in the corpus
- When Kernel Methods Meet Feature Learning: Log-Covariance Network for Action Recognition From Skeletal Data (2017, methodology): “Comparative analysis We benchmarked with [26] which combined Restricted Boltzmann machines and Hidden Markov Models.”
- Soli (2016, background): “There are a number of powerful classification algorithms that can be used for temporal gesture recognition, such as Hidden Markov Models [LoPresti et al. 2015; Kurakin et al. 2012], Dynamic Time Warping [Wu et al.”
- Efficient Temporal Sequence Comparison and Classification Using Gram Matrix Embeddings on a Riemannian Manifold (2016, methodology): “Performance is compablack against the Hankelets subspace angles [21], Hankelet-based HMM [31], 3D joints [40], and Space-time pose [9] methods.”
- 3D skeleton-based human action classification: A survey (2016, methodology): “Such kind of representation does not allow the adoption of a standard SVM and in [62] a set of hidden Markov models trained with a discriminative approach is used instead.”