Skip to main content
SpringerLink
Log in
Book cover

International Conference on Smart Multimedia

ICSM 2019: Smart Multimedia pp 13–27Cite as

Textureless Object Recognition Using an RGB-D Sensor

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12015))

Abstract

Object recognition is a significant task in an industrial assembly line, where a robotic arm should pick a small, textureless, and mostly homogeneous object to place it in its designated location. Despite all the recent advancements in object recognition, the problem still remains challenging for textureless industrial parts with similar shapes. In this paper, we propose an effective and real-time system using a single RGB-D camera to recognize the industrial objects placed at arbitrary viewing direction around the vertical axis. First, we segment the region of interest using an improved watershed segmentation approach. Then, we extract low-level geometrical features. Finally, we train five models and compare their accuracy based on different rotation strategies. Our experimental results highlight the efficiency as well as real-time suitability of our approach.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Abbasi, S., Mokhtarian, F., Kittler, J.: Enhancing css-based shape retrieval for objects with shallow concavities. Image Vis. Comput. 18(3), 199–211 (2000)

    Article  Google Scholar 

  2. Balakrishnama, S., Ganapathiraju, A.: Linear discriminant analysis-a brief tutorial. Inst. Signal Inf. Process. 18, 1–8 (1998)

    Google Scholar 

  3. Beucher, S., Meyer, F.: The morphological approach to segmentation: the watershed transformation. Math. Morphol. Image Process. 34, 433–433 (1992). Optical Engineering-New York-Marcel Dekker Incorporated-

    Google Scholar 

  4. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  Google Scholar 

  5. Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: binary robust independent elementary features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 778–792. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15561-1_56

    Chapter  Google Scholar 

  6. Celebi, M.E., Aslandogan, Y.A.: A comparative study of three moment-based shape descriptors. In: International Conference on Information Technology: Coding and Computing (ITCC’05)-Volume II, vol. 1, pp. 788–793. IEEE (2005)

    Google Scholar 

  7. Cevikalp, H., Triggs, B.: Efficient object detection using cascades of nearest convex model classifiers. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3138–3145. IEEE (2012)

    Google Scholar 

  8. Chen, Y.W., Chen, Y.Q.: Invariant description and retrieval of planar shapes using radon composite features. IEEE Trans. Signal Process. 56(10), 4762–4771 (2008)

    Article  MathSciNet  Google Scholar 

  9. Chuang, G.H., Kuo, C.C.: Wavelet descriptor of planar curves: theory and applications. IEEE Trans. Image Process. 5(1), 56–70 (1996)

    Article  Google Scholar 

  10. Davies, E.: Machine Vision: Theory, Algorithms, Practicalities. Academic Press, Cambridge (1997)

    Google Scholar 

  11. Ding, W., Wang, X., Liu, H., Hu, B.: An empirical study of shape recognition in ensemble learning context. In: 2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR), pp. 256–261. IEEE (2018)

    Google Scholar 

  12. Feng, Y., An, X., Liu, X.: The application of scale invariant feature transform fused with shape model in the human face recognition. In: 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), pp. 1716–1720. IEEE (2016)

    Google Scholar 

  13. Flusser, J.: Invariant shape description and measure of object similarity. In: 1992 International Conference on Image Processing and its Applications, pp. 139–142. IET (1992)

    Google Scholar 

  14. Freeman, H.: On the encoding of arbitrary geometric configurations. IRE Trans. Electron. Comput. 2, 260–268 (1961)

    Article  MathSciNet  Google Scholar 

  15. Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Mach. Learn. 29(2–3), 131–163 (1997)

    Article  Google Scholar 

  16. Girshick, R.: Fast r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)

    Google Scholar 

  17. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)

    Google Scholar 

  18. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Region-based convolutional networks for accurate object detection and segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 38(1), 142–158 (2015)

    Article  Google Scholar 

  19. Guru, D., Nagendraswamy, H.: Symbolic representation of two-dimensional shapes. Pattern Recogn. Lett. 28(1), 144–155 (2007)

    Article  Google Scholar 

  20. Hartigan, J.A., Wong, M.A.: Algorithm as 136: a k-means clustering algorithm. J. Roy. Stat. Soc. Ser. C (Appl. Stat.) 28(1), 100–108 (1979)

    MATH  Google Scholar 

  21. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)

    Google Scholar 

  22. Hearst, M.A., Dumais, S.T., Osuna, E., Platt, J., Scholkopf, B.: Support vector machines. IEEE Intell. Syst. Appl. 13(4), 18–28 (1998)

    Article  Google Scholar 

  23. Hinterstoisser, S., Holzer, S., Cagniart, C., Ilic, S., Konolige, K., Navab, N., Lepetit, V.: Multimodal templates for real-time detection of texture-less objects in heavily cluttered scenes. In: 2011 International Conference on Computer Vision, pp. 858–865. IEEE (2011)

    Google Scholar 

  24. Hu, M.K.: Visual pattern recognition by moment invariants. IRE Trans. Inf. Theory 8(2), 179–187 (1962)

    Article  Google Scholar 

  25. Huang, C., Huang, J.: A fast hog descriptor using lookup table and integral image (2017). arXiv preprint arXiv:1703.06256

  26. Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall, Upper Saddle River (1988)

    MATH  Google Scholar 

  27. Kohonen, T.: Learning vector quantization. In: Self-Organizing Maps, pp. 175–189. Springer (1995). https://doi.org/10.1007/978-3-642-97610-0_6

  28. Kortli, Y., Jridi, M., Al Falou, A., Atri, M.: A comparative study of CFs, LBP, HOG, SIFT, SURF, and BRIEF for security and face recognition (2018)

    Google Scholar 

  29. Kpalma, K., Ronsin, J.: Multiscale contour description for pattern recognition. Pattern Recogn. Lett. 27(13), 1545–1559 (2006)

    Article  Google Scholar 

  30. Kurnianggoro, L., Jo, K.H., et al.: A survey of 2d shape representation: methods, evaluations, and future research directions. Neurocomputing 300, 1–16 (2018)

    Article  Google Scholar 

  31. Lee, S.M., Abbott, A.L., Clark, N.A., Araman, P.A.: A shape representation for planar curves by shape signature harmonic embedding. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), vol. 2, pp. 1940–1947. IEEE (2006)

    Google Scholar 

  32. Liang, M., Hu, X.: Recurrent convolutional neural network for object recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3367–3375 (2015)

    Google Scholar 

  33. Mountrakis, G., Im, J., Ogole, C.: Support vector machines in remote sensing: a review. ISPRS J. Photogram. Rem. Sens. 66(3), 247–259 (2011)

    Article  Google Scholar 

  34. Pham, T.T., Do, T.T., Sünderhauf, N., Reid, I.: Scenecut: joint geometric and object segmentation for indoor scenes. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 1–9. IEEE (2018)

    Google Scholar 

  35. Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)

    Google Scholar 

  36. Rahim, M.A., Azam, M.S., Hossain, N., Islam, M.R.: Face recognition using local binary patterns (lbp). Glob. J. Comput. Sci. Technol. (2013)

    Google Scholar 

  37. Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263–7271 (2017)

    Google Scholar 

  38. Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 6, 1137–1149 (2017)

    Article  Google Scholar 

  39. Ren, Z., Gao, S., Chia, L.T., Tsang, I.W.H.: Region-based saliency detection and its application in object recognition. IEEE Trans. Circ. Syst. Video Technol. 24(5), 769–779 (2013)

    Article  Google Scholar 

  40. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.R.: Orb: an efficient alternative to sift or surf. In: ICCV, vol. 11, p. 2. Citeseer (2011)

    Google Scholar 

  41. Smith, B.L., Williams, B.M., Oswald, R.K.: Comparison of parametric and nonparametric models for traffic flow forecasting. Transp. Res. Part C Emerg. Technol. 10(4), 303–321 (2002)

    Article  Google Scholar 

  42. Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision. Cengage Learning, Boston (2014)

    Google Scholar 

  43. Verma, R., Kaur, R.: Enhanced character recognition using surf feature and neural network technique. IJCSIT Int. J. Comput. Sci. Inf. Technol. 5(4), 5565–5570 (2014)

    Google Scholar 

  44. Yang, M.: Extraction d’attributs et mesures de similarité basées sur la forme. Ph.D. thesis, INSA de Rennes (2008)

    Google Scholar 

  45. Yang, M., Kpalma, K., Ronsin, J.: Scale-controlled area difference shape descriptor. In: Document Recognition and Retrieval XIV, vol. 6500, p. 650003. International Society for Optics and Photonics (2007)

    Google Scholar 

  46. Zhang, D., Lu, G.: A comparative study of curvature scale space and fourier descriptors for shape-based image retrieval. J. Vis. Commun. Image Representation 14(1), 39–57 (2003)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Multimedia Research Centre, University of Alberta, Edmonton, AB, Canada

    Gabriel Lugo, Nasim Hajari & Irene Cheng

  2. Rational Robotics, Edmonton, AB, Canada

    Ashley Reddy

Authors
  1. Gabriel Lugo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nasim Hajari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ashley Reddy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Irene Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gabriel Lugo .

Editor information

Editors and Affiliations

  1. Arizona State University, Mesa, AZ, USA

    Troy McDaniel

  2. University of Florence, Florence, Italy

    Stefano Berretti

  3. Nokia Technologies, Tampere, Finland

    Igor D. D. Curcio

  4. University of Alberta, Edmonton, AB, Canada

    Anup Basu

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lugo, G., Hajari, N., Reddy, A., Cheng, I. (2020). Textureless Object Recognition Using an RGB-D Sensor. In: McDaniel, T., Berretti, S., Curcio, I., Basu, A. (eds) Smart Multimedia. ICSM 2019. Lecture Notes in Computer Science(), vol 12015. Springer, Cham. https://doi.org/10.1007/978-3-030-54407-2_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-54407-2_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-54406-5

  • Online ISBN: 978-3-030-54407-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation