Skip to main content
SpringerLink
Log in

Foreground Object Image Masking via EPI and Edge Detection for Photogrammetry with Static Background

  • Conference paper
  • First Online:
Advances in Visual Computing (ISVC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11845))

Included in the following conference series:

  • 1482 Accesses

Abstract

In automated photogrammetry of a small object, rotating the object provides an easier setting and more stable camera positions than moving the camera around the object. However, the static features in the background can confuse the structure from motion, which leads to the failure of reconstruction. We are addressing the problem by proposing a masking algorithm based on light field epipolar-plane images (EPIs). Using a simple EPI analysis and edge detection technique, a single light field image is enough to create an initial mask, which acts as a region of interest for an edge image. Lastly, binary morphological techniques are applied to obtain the final mask image. The result shows promising performances of 93.84% recall and outperforms comparable algorithms in accuracy, precision, JI, and F1 scores with 98.39%, 97.75%, 91.86%, and 95.75%, respectively.

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 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
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. Sergeeva, A.D., Sablina, V.A.: Using structure from motion for monument 3D reconstruction from images with heterogeneous background. In: 7th Mediterranean Conference on Embedded Computing (MECO), pp. 1–4. IEEE (2018). https://doi.org/10.1109/meco.2018.8406058

  2. Sosa, G.D., et al.: 3D surface reconstruction of entomological specimens from uniform multi-view image datasets. In: 2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA), pp. 1–8. IEEE (2016). https://doi.org/10.1109/stsiva.2016.7743319

  3. Ströbel, B., Schmelzle, S., Blüthgen, N., Heethoff, M.: An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759, 1–27 (2018). https://doi.org/10.3897/zookeys.759.24584

  4. Nguyen, C.V., et al.: 3D scanning system for automatic high-resolution plant phenotyping. In: 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 1–8. IEEE (2016). https://doi.org/10.1109/dicta.2016.7796984

  5. Scharr, H., et al.: Fast high resolution volume carving for 3D plant shoot reconstruction. Front. Plant Sci. 8, 1680 (2017). https://doi.org/10.3389/fpls.2017.01680

  6. Levoy, M., Hanrahan, P.: Light field rendering. In: Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques, pp. 31–42. ACM (1996). https://doi.org/10.1145/237170.237199

  7. Zhu, H., Wang, Q., Yu, J.: Light field imaging: models, calibrations, reconstructions, and applications. Front. Inf. Technol. Electron. Eng. 18(9), 1236–1249 (2017). https://doi.org/10.1631/fitee.1601727

    Article  Google Scholar 

  8. Bolles, R.C., Baker, H.H., Marimont, D.H.: Epipolar-plane image analysis: an approach to determining structure from motion. Int. J. Comput. Vis. 1(1), 7–55 (1987). https://doi.org/10.1007/bf00128525

    Article  Google Scholar 

  9. Wanner, S., Goldluecke, B.: Globally consistent depth labeling of 4D light fields. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 41–48. IEEE (2012). https://doi.org/10.1109/cvpr.2012.6247656

  10. Goldluecke, B., Wanner, S.: The variational structure of disparity and regularization of 4D light fields. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1003–1010 (2013). https://doi.org/10.1109/cvpr.2013.134

  11. Zhang, Y., et al.: Light-field depth estimation via epipolar plane image analysis and locally linear embedding. IEEE Trans. Circuits Syst. Video Technol. 27(4), 739–747 (2016). https://doi.org/10.1109/tcsvt.2016.2555778

    Article  Google Scholar 

  12. Lee, J.Y., Park, R.-H.: Depth estimation from light field by accumulating binary maps based on foreground–background separation. IEEE J. Sel. Top. Sig. Process. 11(7), 955–964 (2017). https://doi.org/10.1109/jstsp.2017.2747154

    Article  Google Scholar 

  13. Monteiro, N.B., Barreto, J.P., Gaspar, J.: Dense lightfield disparity estimation using total variation regularization. In: Campilho, A., Karray, F. (eds.) ICIAR 2016. LNCS, vol. 9730, pp. 462–469. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-41501-7_52

    Chapter  Google Scholar 

  14. Lin, P.-H., Yeh, J.-S., Wu, F.-C., Chuang, Y.-Y.: Depth estimation for Lytro images by adaptive window matching on EPI. J. Imaging 3(2), 17 (2017). https://doi.org/10.3390/jimaging3020017

    Article  Google Scholar 

  15. Diebold, M., Goldluecke, B.: Epipolar plane image refocusing for improved depth estimation and occlusion handling. In: Vision, Modeling & Visualization (VMV), pp. 145–152. The Eurographics Association (2013). http://dx.doi.org/10.2312/PE.VMV.VMV13

  16. Suzuki, T., Takahashi, K., Fujii, T.: Disparity estimation from light fields using sheared EPI analysis. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 1444–1448. IEEE (2016). https://doi.org/10.1109/icip.2016.7532597

  17. Wanner, S., Fehr, J., Jähne, B.: Generating EPI representations of 4D light fields with a single lens focused plenoptic camera. In: Bebis, G., et al. (eds.) ISVC 2011. LNCS, vol. 6938, pp. 90–101. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-24028-7_9

    Chapter  Google Scholar 

  18. Tao, M.W., Hadap, S., Malik, J., Ramamoorthi, R.: Depth from combining defocus and correspondence using light-field cameras. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 673–680 (2013). https://doi.org/10.1109/iccv.2013.89

  19. Yücer, K., Sorkine-Hornung, A., Wang, O., Sorkine-Hornung, O.: Efficient 3D object segmentation from densely sampled light fields with applications to 3D reconstruction. ACM Trans. Graph. (TOG) 35(3), 22 (2016). https://doi.org/10.1145/2876504

    Article  Google Scholar 

  20. The Stanford 3D Scanning Repository. http://graphics.stanford.edu/data/3Dscanrep/

  21. Canny, J.: A computational approach to edge detection. In: Readings in Computer Vision, pp. 184–203. Elsevier, Amsterdam (1987)

    Google Scholar 

  22. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd Edn. Prentice-Hall Inc., Upper Saddle River (2006)

    Google Scholar 

  23. Csurka, G., Larlus, D., Perronnin, F., Meylan, F.: What is a good evaluation measure for semantic segmentation? In: British Machine Vision Conference (BMVC), pp. 32.31–32.11. Citeseer (2013). https://doi.org/10.5244/c.27.32

  24. Jeon, H.-G., et al.: Accurate depth map estimation from a lenslet light field camera. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1547–1555 (2015). https://doi.org/10.1109/cvpr.2015.7298762

  25. Zhang, Y., Yu, P., Yang, W., Ma, Y., Yu, J.: Ray space features for plenoptic structure-from-motion. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4631–4639 (2017). https://doi.org/10.1109/iccv.2017.496

Download references

Author information

Authors and Affiliations

  1. School of Engineering and Information Technology, The University of New South Wales Canberra, Campbell, ACT, 2612, Australia

    Chawin Sathirasethawong, Andrew Lambert & Murat Tahtali

  2. CSIRO Data61, Marsfield, NSW, 1710, Australia

    Changming Sun

  3. School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW, 2052, Australia

    Changming Sun

Authors
  1. Chawin Sathirasethawong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Changming Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andrew Lambert
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Murat Tahtali
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chawin Sathirasethawong .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. University of Nevada, Reno, NV, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Daniela Ushizima

  6. Latent AI, Palo Alto, CA, USA

    Sek Chai

  7. Texas A&M University, College Station, TX, USA

    Shinjiro Sueda

  8. Louisiana State University, Baton Rouge, LA, USA

    Xin Lin

  9. University of North Carolina at Charlotte, Charlotte, NC, USA

    Aidong Lu

  10. École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Daniel Thalmann

  11. Notre Dame University, Notre Dame, IN, USA

    Chaoli Wang

  12. Bosch Research North America, Palo Alto, CA, USA

    Panpan Xu

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sathirasethawong, C., Sun, C., Lambert, A., Tahtali, M. (2019). Foreground Object Image Masking via EPI and Edge Detection for Photogrammetry with Static Background. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2019. Lecture Notes in Computer Science(), vol 11845. Springer, Cham. https://doi.org/10.1007/978-3-030-33723-0_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-33723-0_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33722-3

  • Online ISBN: 978-3-030-33723-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation