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Hierarchical Temporal Memory
i
HTM)
HTM
Adaboost SVM 86.8% 85.5%
ii
ABSTRACT
In the field of pattern recognition, angle variation plays an
important role in producing
effective recognition results. To overcome the angle variation
problems, this thesis adopts
the Hierarchical Temporal Memory (HTM). Based on the inherent
property of the HTM
algorithm which applies temporal information to organize the
continuous change in time of
image features in constructing their respective “invariant
features”, a multi-angle hand
posture recognition method is hence proposed in this thesis.
We first obtain input images from a webcam. The input images will
then be individually
processed by skin detection, background segmentation, and edge
detection. The processed
results are next combined with a voting method to acquire the
correct hand posture region.
If a forearm exists, a forearm segmentation step will be executed;
otherwise it will be
skipped. After normalization of the output images through the
forearm segmentation step,
the images are forwarded to HTM for learning and training the
classifier model. Our
experimental results show that when using the same set of training
and test data, the
proposed multi-angle hand posture recognition method can achieve
92.5% recognition rate,
.
iii
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