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Introduction to Hand Posture Estimation

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Optimisation Algorithms for Hand Posture Estimation

Part of the book series: Algorithms for Intelligent Systems ((AIS))

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Abstract

Computers are an essential tool for the Information Age in the modern world. They are, essentially, a tool to aid the human mind. To be effective, information needs to get to and from the human mind. For much of the evolution of computers, this was text-based, using simple keyboards and monitors.

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References

  1. O’hara K, Harper R, Mentis H, Sellen A, Taylor A, (2013) On the naturalness of touchless: Putting the interaction back into nui. ACM Trans Comput-Hum Interact (TOCHI) 20(1):5

    Article  Google Scholar 

  2. Wu Y, Huang TS (1999) Vision-based gesture recognition: a review. Gesture-based communication in human-computer interaction, Springer, Berlin

    Google Scholar 

  3. Höysniemi J, Hämäläinen P, Turkki L, Rouvi T (2005) Children’s intuitive gestures in vision-based action games. Commun ACM 48(1):44–50

    Article  Google Scholar 

  4. Bhuiyan M, Picking R (2009) Gesture-controlled user interfaces, what have we done and whats next. In: Proceedings of the fifth collaborative research symposium on security, e-learning, internet and networking (SEIN 2009), Darmstadt, Germany, pp 25–29

    Google Scholar 

  5. Francke H, Ruiz-del Solar J, Verschae R (2007) Real-time hand gesture detection and recognition using boosted classifiers and active learning. Advances in image and video technology. Springer, Berlin, pp 533–547

    Google Scholar 

  6. Sturman DJ, Zeltzer D (1994) A survey of glove-based input. IEEE Comput Graph Appl 14(1):30–39

    Article  Google Scholar 

  7. Schlömer T, Poppinga B, Henze N, Boll S (2008) Gesture recognition with a wii controller. In: Proceedings of the 2nd international conference on tangible and embedded interaction. ACM, New York, pp 11–14

    Google Scholar 

  8. Dipietro L, Sabatini AM, Dario P (2008) A survey of glove-based systems and their applications. IEEE Trans Syst Man Cybern Part C Appl Rev 38(4):461–482

    Article  Google Scholar 

  9. Wachs JP, Kölsch M, Stern H, Edan Y (2011) Vision-based hand-gesture applications. Commun ACM 54(2):60–71

    Article  Google Scholar 

  10. Garg P, Aggarwal N, Sofat S (2009) Vision based hand gesture recognition. World Acad Sci Eng Technol 49(1):972–977

    Google Scholar 

  11. Murthy GRS, Jadon RS (2009) A review of vision based hand gestures recognition. Int J Inf Technol Knowl Manag 2(2):405–410

    Google Scholar 

  12. Jiang B, Martinez B, Valstar MF, Pantic M (2014) Decision level fusion of domain specific regions for facial action recognition. In: 2014 22nd International Conference on Pattern Recognition (ICPR). IEEE, pp 1776–1781

    Google Scholar 

  13. Shekhar S, Akshat J, Deepak K (2012) Recognizing and interpreting sign language gesture for human robot interaction. Int J Comput Appl 52(11):24–31

    Google Scholar 

  14. Breuer P, Eckes C, Müller S (2007) Hand gesture recognition with a novel ir time-of-flight range camera-a pilot study. Computer Vision/Computer Graphics Collaboration Techniques. Springer, Berlin, pp 247–260

    Chapter  Google Scholar 

  15. Licsár A, Szirányi T (2004) Hand gesture recognition in camera-projector system. Computer Vision in Human-Computer Interaction. Springer, Berlin, pp 83–93

    Chapter  Google Scholar 

  16. Matsumoto Y, Zelinsky A (2000) An algorithm for real-time stereo vision implementation of head pose and gaze direction measurement. In: Proceedings of the fourth IEEE international conference on automatic face and gesture recognition. IEEE, pp 499–504

    Google Scholar 

  17. Manders C, Farbiz F, Chong JH, Tang KY, Chua GG, Loke MH, Yuan ML (2008) Robust hand tracking using a skin tone and depth joint probability model. In: 2008 8th IEEE international conference on automatic face & gesture recognition, FG’08. IEEE, pp 1–6

    Google Scholar 

  18. Mitra S, Acharya T (2007) Gesture recognition: a survey. IEEE Trans Syst Man Cybern Part C Appl Rev 37(3):311–324

    Article  Google Scholar 

  19. Smith AVW, Sutherland AI, Lemoine A, Mcgrath S (2000) Hand gesture recognition system and method. US Patent 6,128,003, 3 Oct 2000

    Google Scholar 

  20. Bourke AK, Obrien JV, Lyons GM (2007) Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait Posture 26(2):194–199

    Article  Google Scholar 

  21. Rautaray SS, Agrawal A (2015) Vision based hand gesture recognition for human computer interaction: a survey. Artif Intell Rev 43(1):1–54

    Article  Google Scholar 

  22. Yang M-H, Ahuja N, Tabb M (2002) Extraction of 2d motion trajectories and its application to hand gesture recognition. IEEE Trans Pattern Anal Mach Intell 24(8):1061–1074

    Article  Google Scholar 

  23. Murakami K, Taguchi H (1991) Gesture recognition using recurrent neural networks. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, New York, pp 237–242

    Google Scholar 

  24. Stergiopoulou E, Papamarkos N (2009) Hand gesture recognition using a neural network shape fitting technique. Eng Appl Artif Intell 22(8):1141–1158

    Article  Google Scholar 

  25. Dardas NH, Georganas ND (2011) Real-time hand gesture detection and recognition using bag-of-features and support vector machine techniques. IEEE Trans Instrum Meas 60(11):3592–3607

    Article  Google Scholar 

  26. Saha S, Konar A, Roy J (2015) Single person hand gesture recognition using support vector machine. Computational advancement in communication circuits and systems. Springer, New Delhi, pp 161–167

    Google Scholar 

  27. Barsoum E (2016) Articulated hand pose estimation review. arXiv:1604.06195

  28. Jonathan T, Bordeaux L, Cashman T, Corish B, Keskin C, Sharp T, Soto E, Sweeney D, Valentin J, Luff B et al (2016) Efficient and precise interactive hand tracking through joint, continuous optimization of pose and correspondences. ACM Trans Graph (TOG) 35(4):143

    Google Scholar 

  29. Argyros AA, Lourakis MIA (2006) Binocular hand tracking and reconstruction based on 2d shape matching. In: 18th International Conference on Pattern Recognition, ICPR 2006. IEEE, vol 1, pp 207–210

    Google Scholar 

  30. Oikonomidis I, Kyriazis N, Argyros AA (2010) Markerless and efficient 26-dof hand pose recovery. Asian Conference on Computer Vision. Springer, Berlin, pp 744–757

    Google Scholar 

  31. Darrell TJ, Essa IA, Pentland AP (1996) Task-specific gesture analysis in real-time using interpolated views. IEEE Trans Pattern Anal Mach Intell 18(12):1236–1242

    Article  Google Scholar 

  32. Sun X, Wei Y, Liang S, Tang X, Sun J (2015) Cascaded hand pose regression. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 824–832

    Google Scholar 

  33. Sharp T, Keskin C, Robertson D, Taylor J, Shotton J, Kim D, Rhemann C, Leichter I, Vinnikov A, Wei Y et al (2015) Accurate, robust, and flexible real-time hand tracking. In: Proceedings of the 33rd annual ACM conference on human factors in computing systems, pp 3633–3642. ACM, New York

    Google Scholar 

  34. Qian C, Sun X, Wei Y, Tang X, Sun J (2014) Realtime and robust hand tracking from depth. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1106–1113

    Google Scholar 

  35. Ji S, Wei X, Yang M, Kai Y (2013) 3d convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell 35(1):221–231

    Article  Google Scholar 

  36. Kopinski T, Sachara F, Gepperth A, Handmann U (2016) A deep learning approach for hand posture recognition from depth data. International conference on artificial neural networks. Springer, Berlin, pp 179–186

    Google Scholar 

  37. Fanelli G, Gall J, Van Gool L (2011) Real time head pose estimation with random regression forests. In: 2011 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 617–624

    Google Scholar 

  38. Kopinski T, Gepperth A, Handmann U (2015) A simple technique for improving multi-class classification with neural networks. In: Proceedings. Presses universitaires de Louvain, p 469

    Google Scholar 

  39. Sato Y, Saito M, Koike H (2001) Real-time input of 3d pose and gestures of a user’s hand and its applications for HCI. In Proceedings IEEE Virtual Reality. IEEE, pp 79–86

    Google Scholar 

  40. Keskin C, Kıraç F, Kara YE, Akarun L (2013) Real time hand pose estimation using depth sensors. Consumer depth cameras for computer vision. Springer, London, pp 119–137

    Google Scholar 

  41. Suarez J, Murphy RR (2012) Hand gesture recognition with depth images: a review. In: 2012 IEEE RO-MAN. IEEE, pp 411–417

    Google Scholar 

  42. Konda KR, Königs A, Schulz H, Schulz D (2012) Real time interaction with mobile robots using hand gestures. In: Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction. ACM, New York, pp 177–178

    Google Scholar 

  43. Oikonomidis I, Kyriazis N, Argyros AA (2011) Markerless and efficient 26-dof hand pose recovery. Computer Vision-ACCV 2010. Springer, Berlin, pp 744–757

    Chapter  Google Scholar 

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Authors and Affiliations

  1. Torrens University Australia, Fortitude Valley, Brisbane, QLD, Australia

    Shahrzad Saremi & Seyedali Mirjalili

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  1. Shahrzad Saremi
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  2. Seyedali Mirjalili
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Correspondence to Shahrzad Saremi .

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Saremi, S., Mirjalili, S. (2020). Introduction to Hand Posture Estimation. In: Optimisation Algorithms for Hand Posture Estimation. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-13-9757-8_1

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