Vision Based Hand Gesture Interfaces for Wearable Computing and ...

Chapter 2. Literature Review
also earlier work by Lee and Kunii [102]). Type II constraints reduce the dimen-
sionality by assuming direct correlation between DIP and PIP flexion. Type III
constraints limit the extent of the space again by eliminating generally impossible
configurations and unlikely transitions between configurations. Altogether and
after a PCA, a dimensionality reduction to seven dimensions was achieved while
retaining 95% of all configurations observed in their experiments. Wu and Huang
also published a good high-level survey of the state of the art of hand modeling,
analysis, and recognition [189].
Bray et al. [18] introduce a special gradient descent method called “Stochastic
Meta Descent.” It has adaptive step sizes so that it does not likely get stuck in
a local minimum. Together with an anthropometric model and stereo video they
achieve good results in 3D hand tracking. The processing time is not short enough
for real-time deployment (4.7 seconds/frame on 1GHz Sunfire). Another paper
by the same authors [17] combines the Stochastic Meta Descent with a particle
filtering method, thus making it easier for the deterministic component to march
out of local minima. This improvement is bought with increased computation
time.
Analysis by synthesis describes estimation of model parameters by back-pro-
jection of the model into the image domain, and then adjusting it iteratively
to the closest match between back-projection and observation (see, for example,
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