Skip to main content
SpringerLink
Log in

Image Segmentation of Bricks in Masonry Wall Using a Fusion of Machine Learning Algorithms

  • Conference paper
  • First Online:
Pattern Recognition. ICPR International Workshops and Challenges (ICPR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12667))

Included in the following conference series:

Abstract

Autonomous mortar raking requires a computer vision system which is able to provide accurate segmentation masks of close-range images of brick walls. The goal is to detect and ultimately remove the mortar, leaving the bricks intact, thus automating this construction-related task. This paper proposes such a vision system based on the combination of machine learning algorithms. The proposed system fuses the individual segmentation outputs of eight classifiers by means of a weighted voting scheme and then performing a threshold operation to generate the final binary segmentation. A novel feature of this approach is the fusion of several segmentations using a low-cost commercial off-the-shelf hardware setup. The close-range brick wall segmentation capabilities of the system are demonstrated on a total of about 9 million data points.

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

Access this chapter

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

Institutional subscriptions

References

  1. Adán, A., Quintana, B., Prieto, S., Bosché, F.: An autonomous robotic platform for automatic extraction of detailed semantic models of buildings. Autom. Constr. 109, 102963 (2020)

    Article  Google Scholar 

  2. Adán, A., Quintana, B., Prieto, S.A., Bosché, F.: Scan-to-BIM for ‘secondary’ building components. Adv. Eng. Inform. 37, 119–138 (2018)

    Article  Google Scholar 

  3. Altman, N.S.: An introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat. 46(3), 175–185 (1992)

    MathSciNet  Google Scholar 

  4. Bovenzi, M.: Health effects of mechanical vibration. G Ital. Med. Lav. Ergon. 27(1), 58–64 (2005)

    Google Scholar 

  5. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/A:1010933404324

    Article  MATH  Google Scholar 

  6. Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A.: Classification and Regression Trees. CRC Press, Boca Raton (1984)

    MATH  Google Scholar 

  7. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). https://doi.org/10.1007/BF00994018

    Article  MATH  Google Scholar 

  8. Grana, C., Borghesani, D., Cucchiara, R.: Optimized block-based connected components labeling with decision trees. IEEE Trans. Image Process. 19(6), 1596–1609 (2010)

    Article  MathSciNet  Google Scholar 

  9. Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2003)

    MATH  Google Scholar 

  10. Hastie, T., Rosset, S., Zhu, J., Zou, H.: Multi-class AdaBoost. Stat. Interface 2(3), 349–360 (2009)

    Article  MathSciNet  Google Scholar 

  11. Ibrahim, Y., Nagy, B., Benedek, C.: CNN-based watershed marker extraction for brick segmentation in masonry walls. In: Karray, F., Campilho, A., Yu, A. (eds.) ICIAR 2019. LNCS, vol. 11662, pp. 332–344. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-27202-9_30

    Chapter  Google Scholar 

  12. Intel Corporation: Intel RealSense D400 Series Product Family Datasheet (2020). https://dev.intelrealsense.com/docs/intel-realsense-d400-series-product-family-datasheet

  13. Kapoor, M., Katsanos, E., Thöns, S., Nalpantidis, L., Winkler, J.: Structural integrity management with unmanned aerial vehicles: state-of-the-art review and outlook. In: Sixth International Symposium on Life-Cycle Civil Engineering, IALCCE 2018, Ghent, Belgium (2018)

    Google Scholar 

  14. Kim, D., Yin, K., Liu, M., Lee, S., Kamat, V.: Feasibility of a drone-based on-site proximity detection in an outdoor construction site. Comput. Civ. Eng. 2017, 392–400 (2017). https://doi.org/10.1061/9780784480847.049

    Article  Google Scholar 

  15. Lu, Y., Wu, Z., Chang, R., Li, Y.: Building information modeling (BIM) for green buildings: a critical review and future directions. Autom. Constr. 83, 134–148 (2017)

    Article  Google Scholar 

  16. Oses, N., Dornaika, F., Moujahid, A.: Image-based delineation and classification of built heritage masonry. Remote Sens. 6(3), 1863–1889 (2014)

    Article  Google Scholar 

  17. Powers, D.M.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. J. Mach. Learn. Technol. 2, 37–63 (2011)

    Google Scholar 

  18. RAW: About Robot At Work (2019). https://robotatwork.com/about-raw/

  19. Riveiro, B., Lourenço, P.B., Oliveira, D.V., González-Jorge, H., Arias, P.: Automatic morphologic analysis of quasi-periodic masonry walls from LiDAR. Comput.-Aided Civ. Infrastruct. Eng. 31(4), 305–319 (2016)

    Article  Google Scholar 

  20. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  21. Srivastava, S., Gupta, M.R., Frigyik, B.A.: Bayesian quadratic discriminant analysis. J. Mach. Learn. Res. 8(Jun), 1277–1305 (2007)

    MathSciNet  MATH  Google Scholar 

  22. Valero, E., Bosché, F., Forster, A.: Automatic segmentation of 3D point clouds of rubble masonry walls, and its application to building surveying, repair and maintenance. Autom. Constr. 96, 29–39 (2018)

    Article  Google Scholar 

  23. Valero, E., Forster, A., Bosché, F., Hyslop, E., Wilson, L., Turmel, A.: Automated defect detection and classification in ashlar masonry walls using machine learning. Autom. Constr. 106, 102846 (2019)

    Article  Google Scholar 

  24. Wang, X.Y., Wang, T., Bu, J.: Color image segmentation using pixel wise support vector machine classification. Pattern Recogn. 44(4), 777–787 (2011)

    Article  Google Scholar 

  25. Webster, A., Feiner, S., MacIntyre, B., Massie, W., Krueger, T.: Augmented reality in architectural construction, inspection and renovation. In: Proceedings of the ASCE Third Congress on Computing in Civil Engineering, vol. 1, p. 996 (1996)

    Google Scholar 

  26. Xu, S., Wang, J., Wang, X., Shou, W.: Computer vision techniques in construction, operation and maintenance phases of civil assets: a critical review. In: Proceedings of the International Symposium on Automation and Robotics in Construction, ISARC, vol. 36, pp. 672–679. IAARC Publications (2019)

    Google Scholar 

  27. Zaher, M., Greenwood, D., Marzouk, M.: Mobile augmented reality applications for construction projects. Constr. Innov. 18(2), 152–166 (2018)

    Article  Google Scholar 

  28. Zhang, H.: Exploring conditions for the optimality of naive Bayes. Int. J. Pattern Recogn. Artif. Intell. 19(02), 183–198 (2005)

    Article  Google Scholar 

Download references

Acknowledgment

The authors would like to thank the company Robot At Work for offering their collaboration to solve the mortar raking problem. Furthermore, we thank Rune Hansen, Finn Christensen, and Kasper Laursen from Robot At Work for their support and contribution to the project.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Technical University of Denmark, Elektrovej 326, 2800 Kgs., Lyngby, Denmark

    Roland Kajatin & Lazaros Nalpantidis

Authors
  1. Roland Kajatin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lazaros Nalpantidis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Roland Kajatin .

Editor information

Editors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy

    Alberto Del Bimbo

  2. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Rita Cucchiara

  3. Department of Computer Science, Boston University, Boston, MA, USA

    Stan Sclaroff

  4. Dipartimento di Matematica e Informatica, University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Cloud & AI, JD.COM, Beijing, China

    Tao Mei

  6. Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy

    Marco Bertini

  7. Computational Sciences Department, National Institute of Astrophysics, Optics and Electronics (INAOE), Tonantzintla, Puebla, Mexico

    Hugo Jair Escalante

  8. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Roberto Vezzani

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kajatin, R., Nalpantidis, L. (2021). Image Segmentation of Bricks in Masonry Wall Using a Fusion of Machine Learning Algorithms. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12667. Springer, Cham. https://doi.org/10.1007/978-3-030-68787-8_33

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-68787-8_33

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-68786-1

  • Online ISBN: 978-3-030-68787-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation