Summary
Kato et al. seek a way to represent hand data that is easier to handle. The problem they present is that hand-tracking data has too many dimensions and is difficult to handle in a feasible manner. They take motion data (bending each finger down to touch the palm) and split it into 100 time instants, with each instant containing bend data for 20 different joints in the hands. So each gesture (whole range of motion), is a 2000-dimension data vector (100 20-d vectors concatenated together).
They try to do feature extraction using PCA and ICA, both. They say ICA is better because it can extract the independent movement of the individual fingers, where PCA the movements of the fingers are not individual. Then they mention hand tracking using particle filtering, where we estimate the next position (?) of the hand using its current position.
Discussion
This paper has no clear purpose. I don't understand what the authors are trying to tell me. Because of that, I don't have much to offer that's not a rant.
PCA is not supposed to give you "feasible" hand positions. It tells you the directions of the highest variance.
1 comment:
This paper sounds like epic fail, judging from your comments. Combined with the comments from our classmates, I'm beginning to wonder if the authors really, really liked to use PCA and ICA algorithms in general.
Post a Comment