LaViola - Survey of Haptics

LaViola, J. J. 1999 A Survey of Hand Posture and Gesture Recognition Techniques and Technology. Technical Report. UMI Order Number: CS-99-11., Brown University.

Summary

We read only chapters 3 and 4. LaViola gives a nice summary over the many methods for haptic recognition and many domains where it can be used.

Template matching (like a $1 for haptics) is easy and has been implemented with good accuracy for small sets of gestures. Feature-based classifiers, like Rubine, have been used for very high accuracy rates, as well as segmentation of gestures. PCA can be used to form "eigenpostures" and to simplify data, possibly, for recognition. Obviously, as we've seen many times in class, neural networks and hidden Markov models can both be used to achieve high accuracy for complex data sets, but both require extensive training and some a priori knowledge of the data set (number of hidden layers/units and number of hidden states for nets and HMMs, respectively). Instance based learning, such as k-nearest neighbors, has also been briefly touched upon in the literature, but not much investigation has been performed. Other techniques, like using formal grammars to describe postures/gestures, are also discussed but not much work has been done in these areas.

The application domains for hand gesture recognition is basically all the stuff we've seen in class: sign language, virtual environments, controlling robots/computer systems, and 3D modelling.

Discussion

This was a very nice overview of the field. I'm most interested in exploring:

• Template matching methods and feature based recognition (Sturman and Wexelblat)


• PCA for gesture segmentation


• Using a k-nearest neighbors approach to classification


• Defining a constraint grammar to express a posture/gesture

All my ideas (except for the last) stem around the idea of representing a posture/gesture with a vector of features. Picking good features might be hard, as it is in sketch rec, but I think that it can be done (analogous to PaleoSketch).

BibTeX

@techreport{864649,
author = {Joseph J. LaViola, Jr.},
title = {A Survey of Hand Posture and Gesture Recognition Techniques and Technology},
year = {1999},
source = {http://www.ncstrl.org:8900/ncstrl/servlet/search?formname=detail\&id=oai%3Ancstrlh%3Abrowncs%3ABrownCS%2F%2FCS-99-11},
publisher = {Brown University},
address = {Providence, RI, USA},
}