Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Automatic control engineering > Robotics
|
Buy Now
Robust Hand Gesture Recognition for Robotic Hand Control (Paperback, Softcover reprint of the original 1st ed. 2018)
Loot Price: R2,886
Discovery Miles 28 860
|
|
Robust Hand Gesture Recognition for Robotic Hand Control (Paperback, Softcover reprint of the original 1st ed. 2018)
Expected to ship within 10 - 15 working days
|
This book focuses on light invariant bare hand gesture recognition
while there is no restriction on the types of gestures.
Observations and results have confirmed that this research work can
be used to remotely control a robotic hand using hand gestures. The
system developed here is also able to recognize hand gestures in
different lighting conditions. The pre-processing is performed by
developing an image-cropping algorithm that ensures only the area
of interest is included in the segmented image. The segmented image
is compared with a predefined gesture set which must be installed
in the recognition system. These images are stored and feature
vectors are extracted from them. These feature vectors are
subsequently presented using an orientation histogram, which
provides a view of the edges in the form of frequency. Thereby, if
the same gesture is shown twice in different lighting intensities,
both repetitions will map to the same gesture in the stored data.
The mapping of the segmented image's orientation histogram is
firstly done using the Euclidian distance method. Secondly, the
supervised neural network is trained for the same, producing better
recognition results. An approach to controlling electro-mechanical
robotic hands using dynamic hand gestures is also presented using a
robot simulator. Such robotic hands have applications in
commercial, military or emergency operations where human life
cannot be risked. For such applications, an artificial robotic hand
is required to perform real-time operations. This robotic hand
should be able to move its fingers in the same manner as a human
hand. For this purpose, hand geometry parameters are obtained using
a webcam and also using KINECT. The parameter detection is
direction invariant in both methods. Once the hand parameters are
obtained, the fingers' angle information is obtained by performing
a geometrical analysis. An artificial neural network is also
implemented to calculate the angles. These two methods can be used
with only one hand, either right or left. A separate method that is
applicable to both hands simultaneously is also developed and
fingers angles are calculated. The contents of this book will be
useful for researchers and professional engineers working on
robotic arm/hand systems.
General
Is the information for this product incomplete, wrong or inappropriate?
Let us know about it.
Does this product have an incorrect or missing image?
Send us a new image.
Is this product missing categories?
Add more categories.
Review This Product
No reviews yet - be the first to create one!
|
|
Email address subscribed successfully.
A activation email has been sent to you.
Please click the link in that email to activate your subscription.