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Recognition of Auslan Signs
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List of Figures
The joints in their hand together with their associated degrees of freedom.
The tendons that are present in the hand. Again, note the high complexity of the muscles and tendons within the hand, leading to a wide range of possible motions.
The wide number of possible motions that the hand can make.
The continuum between Auslan and Signed English. PSE lies in the middle.
The neutral space for signs. Exact location is not important within this area.
A picture of the internals of the VPL DataGlove, attached to a Macintosh.
The Exos Dextrous Hand Master
The internal structure of a neuron
A neural network -- a collection of neurons connected together.
A graphical representation of the decision tree produced by C4.5
The decision tree discussed so far partition the space as shown.
An example where the limitations of C4.5 come through. If the concept boundary is non-orthogonal, C4.5 has to make some approximations.
The eccentricity in the diagram is defined to be MaxDev/StraightPath. The value is positive if it is on our right as we follow the trajectory, and negative if it is on our left.
Wexelblat's system for use within AHIG.
The structure of the recurrent neural network used by Murakami and Taguchi.
The complete Talking Glove system.
The approach adopted by Kramer to recognise finger spelling.
The complete GRASP system
Screen dump of the
gloveplay
program.
The Auslan Dictionary definition of same.
The user starting the sign
Towards ...
Meeting fingers ...
Away ...
The user indicates the sign is complete.
The bounding box for the sign
same
.
An example of a histogram -- in this case of motion in the x-dimension.
Graph of the relationship between error and the number of divisions used in making the histograms.
The possible rotation values coming from the glove and their encoding and classification into histograms.
Two examples of vectors and the octants they fall into -- one in octant
and the other in octant
.
Original data and time-division approximation. Note the clarity of the time-division approximation -- it also manages to reduce the effects of noise.
The variation in the error rate that occurs with segmentation.
The effect that the number of samples has on the error rate.
The effect of the number of samples on the error rate, this time with a logarithmic x-axis.
The effect of an increasing number of signs on the error rate.
The effect of an increasing number of signs on the error rate using IBL1, this time with a logarithmic x-axis.
What can happen when GRASP is trained on one set of users and then tested on another.
The relationship between the number of samples per sign and the error rate.
The time per test example and its relationship with the number of signs being learnt.
waleed@cse.unsw.edu.au