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020 _a9789811047985
_9978-981-10-4798-5
024 7 _a10.1007/978-981-10-4798-5
_2doi
050 4 _aTJ212-225
050 4 _aTJ210.2-211.495
072 7 _aTJFM
_2bicssc
072 7 _aTEC037000
_2bisacsh
072 7 _aTJFM
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082 0 4 _a629.8
_223
100 1 _aChaudhary, Ankit.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_958178
245 1 0 _aRobust Hand Gesture Recognition for Robotic Hand Control
_h[electronic resource] /
_cby Ankit Chaudhary.
250 _a1st ed. 2018.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2018.
300 _aXXI, 96 p. 67 illus., 54 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aChapter 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.
520 _aThis 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.
650 0 _aControl engineering.
_931970
650 0 _aRobotics.
_92393
650 0 _aAutomation.
_92392
650 0 _aArtificial intelligence.
_93407
650 1 4 _aControl, Robotics, Automation.
_931971
650 2 4 _aArtificial Intelligence.
_93407
650 2 4 _aControl and Systems Theory.
_931972
710 2 _aSpringerLink (Online service)
_958179
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811047978
776 0 8 _iPrinted edition:
_z9789811047992
776 0 8 _iPrinted edition:
_z9789811352348
856 4 0 _uhttps://doi.org/10.1007/978-981-10-4798-5
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c80092
_d80092