Robust hand gesture recognition for robotic hand control / Ankit Chaudhary.
Chaudhary, Ankit, author.
2018
TJ211
Available Online

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Title Robust hand gesture recognition for robotic hand control / Ankit Chaudhary.
Author Chaudhary, Ankit, author.
ISBN 9789811047985 (electronic book)
9811047987 (electronic book)
9789811047978
DOI https://doi.org/10.1007/978-981-10-4798-5
Published Singapore : Springer, [2018]
Language English
Description 1 online resource (xxi, 96 pages) : illustrations.
Item Number 10.1007/978-981-10-4798-5 doi
Call Number TJ211
Dewey Decimal Classification 629.8/933
Summary 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.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Digital File Characteristics text file PDF
Source of Description Online resource; title from PDF title page (SpringerLink, viewed June 8, 2017).
Available in Other Form Print version: 9789811047978
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Chapter 1: Introduction
Chapter 2: Scientific Goals
Chapter 3: State of the Art
Chapter 4: Hand Image Segmentation
Chapter 5: Light Invariant Hand Gesture Recognition
Chapter 6: Fingertips Detection
Chapter 7: Bent Finger?s Angles Calculation
Chapter 8: Both Hands? Angles Calculation
Chapter 9: Conclusions.

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