Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Ankle Active Rehabilitation Strategies Analysis Based on the Characteristics of Human and Robotic Integrated Biomechanics Simulation Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Object-Shape Recognition Based on Haptic Image

Authors : Yi Gong, Juan Wu, Miao Wu, Xiao Han

Published in: Intelligent Robotics and Applications

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

This paper introduces a concept of haptic image which contains sufficient haptic data acquired from haptic interaction. Deep mining and proper processing of haptic image may extend the applications to many fields. An approach of haptic shape recognition based on haptic image is presented. Firstly, a glove-like device mounted with pressure sensors and fiber sensors is utilized to acquire haptic image during the exploration of object shape. Secondly, pre-processing of haptic image is conducted including smoothing and standardization. Thirdly, haptic flow is extracted from haptic image as shape feature. Haptic flow proposed in this paper is the displacement of contact points between adjacent time intervals, which is inspired by optical flow. At last, a self-organizing map (SOM) is employed for the classification and recognition of the explored shapes. In the experiment, a recognition test of 4 different shapes, including cube, block, cylinder and sphere, is conducted and the mean recognition rate is approximately 90%.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Web-Based Human Robot Interaction via Live Video Streaming and Voice
next chapter Personal Desktop-Level Jet Fighter Simulator for Training or Entertainment
Literature
1.
Schill, J., Laaksonen, J., Przybylski, M., Kyrki, V., Asfour, T., Dillmann, R.: Learning continuous grasp stability for a humanoid robot hand based on tactile sensing. In: 4th IEEE RAS & EMBS International Conference, pp. 1901–1906. IEEE (2012)
2.
Shahabi, C., Kolahdouzan, M.R., Barish, G., Zimmermann, R., Yao, D., Fu, K., Zhang, L.: Alternative techniques for the efficient acquisition of haptic data. In: ACM SIGMETRICS Performance Evaluation Review, vol. 29, no. 1, pp. 334–335. ACM, Berlin (2001)
3.
Gao, Y., Hendricks, L.A., Kuchenbecker, K.J., Darrell, T.: Deep learning for tactile understanding from visual and haptic data. In: 2016 IEEE International Conference on Robotics and Automation, pp. 536–543. IEEE (2016)
4.
Nakano, T., Uozumi, S., Johansson, R., Ohnishi, K.: A quantization method for haptic data lossy compression. In: 2015 IEEE International Conference on Mechatronics, pp. 126–131. IEEE (2015)
5.
Kaneko, T., Ito, S., Sakaino, S., Tsuji, T.: Haptic data compression for rehabilitation databases. In: 13th International Workshop on Advanced Motion Control, pp. 657–662. IEEE (2015)
6.
7.
Nadjarbashi, O.F., Abdi, H., Nahavandi, S.: Applying inverse just-noticeable-differences of velocity to position data for haptic data reduction. In: 2015 IEEE International Conference on Systems, Man, and Cybernetics, pp. 440–445. IEEE (2015)
8.
Burka, A., Hu, S., Helgeson, S., Krishnan, S., Gao, Y., Hendricks, L.A., Kuchenbecker, K.: Proton: a visuo-haptic data acquisition system for robotic learning of surface properties. In: 2016 IEEE International Conference on Multisensor Fusion and Intergration for Intelligent Systems, pp. 58–65. IEEE (2016)
9.
Gemici, M.C., Saxena, A.: Learning haptic representation for manipulating deformable food objects. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 638–646. IEEE (2014)
10.
Park, N., Zhu, W., Jung, Y., McLaughlin, M., Jin, S.: Utility of haptic data in recognition of user state. In: Proceedings of HCI International, vol. 11. Las Vegas, Nevada (2005)
11.
Oz, C., Leu, M.: Recognition of finger spelling of American sign language with artificial neural network using position/orientation sensors and data glove. In: Advances in Neural Networks ISNN 2005, pp. 812–812. Springer, New York (2005)
12.
Ji, W., Zhao, D., Cheng, F., Xu, B., Zhang, Y., Wang, J.: Automatic recognition vision system guided for apple harvesting robot. Comput. Electr. Eng. 38(5), 1186–1195 (2015)CrossRef
13.
Yatani, K., Banovic, N., Truong, K.: SpaceSense: representing geographical information to visually impaired people using spatial tactile feedback. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 415–424. ACM, New York (2012)
14.
Hirano, Y., Kitahama, K.I., Yoshizawa, S.: Image-based object recognition and dexterous hand/arm motion planning using RRTs for grasping in cluttered scene. In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2041–2046. IEEE (2005)
15.
Choi, J., Seo, B.K., Lee, D., Park, H., Park, J.I.: RGB-D camera-based hand shape recognition for human-robot interaction. In: 2013 44th International Symposium on Robotics, pp. 1–2. IEEE (2013)
16.
Johnsson, M., Balkenius, C.: Experiments with proprioception in a self-organizing system for haptic perception. In: Towards Autonomous Robotic Systems, pp. 239–245 (2007)
17.
Dipietro, L., Sabatini, A.M., Dario, P.: A survey of glove-based systems and their applications. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 38(4), 461–482 (2008)
18.
Marion, G.C.: Wireless communication glove apparatus for motion tracking, gesture recognition, data transmission, and reception in extreme environments. In: Proceedings of the 2009 ACM symposium on Applied Computing, pp. 172–173. ACM, New York (2009)
19.
Okada, K., Kojima, M., Tokutsu, S., Maki, T., Mori, Y., Inaba, M.: Multi-cue 3D object recognition in knowledge-based vision-guided humanoid robot system. In: 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3217–3222. IEEE (2007)
20.
Yabuta, Y., Mizumoto, H., Arii, S.: Binocular robot vision system with shape recognition. In: ICCAS2007 International Conference on Control, Automation and Systems, pp. 2299–2302. IEEE (2007)
21.
Bhattacharjee, T., Rehg, J.M., Kemp, C.C.: Haptic classification and recognition of objects using a tactile sensing forearm. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4090–4097. IEEE (2012)
22.
Gorges, N., Navarro, S.E., Goger, D., Worn, H.: Haptic object recognition using passive joints and haptic key features. In: 2010 IEEE International Conference on Robotics and Automation, pp. 2349–2355. IEEE (2010)
23.
Luzhnica, G., Simon, J., Lex, E., Pammer, V.: A sliding window approach to natural hand gesture recognition using a custom data glove. In: 2016 IEEE Symposium on 3D User Interfaces, pp. 81–90. IEEE (2016)
24.
Craig, J.J.: Introduction to robotics: mechanics and control, vol. 3, pp. 48–70. Pearson Prentice Hall, Upper Saddle River (2005)
25.
Gibson, J.J.: The Perception of the Visual World. Houghton Mifflin, Oxford (1950)
Metadata
Title
Object-Shape Recognition Based on Haptic Image
Authors
Yi Gong
Juan Wu
Miao Wu
Xiao Han
Copyright Year
2017
Book
Intelligent Robotics and Applications

Print ISBN: 978-3-319-65288-7

Electronic ISBN: 978-3-319-65289-4

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-65289-4_39

Premium Partner

    Image Credits