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6. [http://wohlersassociates.com/2018report.htm '''Wohlers Report.'''] Annual worldwide progress report in 3D Printing, 2018. <ref>Wohlers Report. Annual worldwide progress report in 3D Printing, 2018.</ref>
6. [http://wohlersassociates.com/2018report.htm '''Wohlers Report.'''] Annual worldwide progress report in 3D Printing, 2018. <ref>Wohlers Report. Annual worldwide progress report in 3D Printing, 2018.</ref>


7. ''Ugandhar Delli, Shing Chang.'' [https://www.sciencedirect.com/science/article/pii/S2351978918307820 '''Automated processes monitoring in 3D printing using supervised machine learning.'''] Procedia Manufacturing 26 (2018) 865–870, doi.org/10.1016/j.promfg.2018.07.111 <ref>Ugandhar Delli, Shing Chang. Automated processes monitoring in 3D printing using supervised machine learning.Procedia Manufacturing 26 (2018) 865–870, doi.org/10.1016/j.promfg.2018.07.111</ref>
7. ''U. Delli, S. Chang.'' [https://www.sciencedirect.com/science/article/pii/S2351978918307820 '''Automated processes monitoring in 3D printing using supervised machine learning.'''] Procedia Manufacturing 26 (2018) 865–870, doi.org/10.1016/j.promfg.2018.07.111 <ref>U. Delli, S. Chang. Automated processes monitoring in 3D printing using supervised machine learning.Procedia Manufacturing 26 (2018) 865–870, doi.org/10.1016/j.promfg.2018.07.111</ref>


8. ''L. Scime, J. Beuth.'' [https://www.sciencedirect.com/science/article/pii/S221486041730180X '''Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm.'''] Additive Manufacturing 19 (2018) 114–126. doi.org/10.1016/j.addma.2017.11.009 <ref>L. Scime, J. Beuth. Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm. Additive Manufacturing 19 (2018) 114–126. doi.org/10.1016/j.addma.2017.11.009</ref>
8. ''L. Scime, J. Beuth.'' [https://www.sciencedirect.com/science/article/pii/S221486041730180X '''Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm.'''] Additive Manufacturing 19 (2018) 114–126. doi.org/10.1016/j.addma.2017.11.009 <ref>L. Scime, J. Beuth. Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm. Additive Manufacturing 19 (2018) 114–126. doi.org/10.1016/j.addma.2017.11.009</ref>
Line 83: Line 83:
17. ''J. Barandiaran, D. Borro.'' [https://ieeexplore.ieee.org/document/4414647 '''Edge-based markerless 3D tracking of rigid objects.'''] 17th International Conference on Artificial Reality and Telexistence (ICAT 2007). doi:10.1109/ICAT.2007.62 <ref>J. Barandiaran, D. Borro. Edge-based markerless 3D tracking of rigid objects. 17th International Conference on Artificial Reality and Telexistence (ICAT 2007). doi:10.1109/ICAT.2007.62</ref>
17. ''J. Barandiaran, D. Borro.'' [https://ieeexplore.ieee.org/document/4414647 '''Edge-based markerless 3D tracking of rigid objects.'''] 17th International Conference on Artificial Reality and Telexistence (ICAT 2007). doi:10.1109/ICAT.2007.62 <ref>J. Barandiaran, D. Borro. Edge-based markerless 3D tracking of rigid objects. 17th International Conference on Artificial Reality and Telexistence (ICAT 2007). doi:10.1109/ICAT.2007.62</ref>


18. ''Wuest, Harald, Florent Vial, and D. Strieker.'' [https://ieeexplore.ieee.org/abstract/document/1544665 '''Adaptive line tracking with multiple hypotheses for augmented reality.'''] Fourth IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR'05), 2005. doi:10.1109/ISMAR.2005.8 <ref>Wuest, Harald, Florent Vial, and D. Strieker. Adaptive line tracking with multiple hypotheses for augmented reality. Fourth IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR'05), 2005. doi:10.1109/ISMAR.2005.8</ref>
18. ''Gordon I., Lowe D.G.'' [https://link.springer.com/chapter/10.1007/11957959_4 '''What and Where: 3D Object Recognition with Accurate Pose.'''] In: Ponce J., Hebert M., Schmid C., Zisserman A. (eds) Toward Category-Level Object Recognition. Lecture Notes in Computer Science, vol 4170. Springer, 2006. doi:10.1007/11957959_4 <ref>Gordon I., Lowe D.G. What and Where: 3D Object Recognition with Accurate Pose. In: Ponce J., Hebert M., Schmid C., Zisserman A. (eds) Toward Category-Level Object Recognition. Lecture Notes in Computer Science, vol 4170. Springer, 2006. doi:10.1007/11957959_4</ref>


19. K. Grauman and T. Darrell, “The pyramid match kernel: Discriminative classification with sets of image features,” in ICCV, vol. 2, 2005, pp. 1458–1465 Vol. 2.
19. ''Wuest, Harald, Florent Vial, and D. Strieker.'' [https://ieeexplore.ieee.org/abstract/document/1544665 '''Adaptive line tracking with multiple hypotheses for augmented reality.'''] Fourth IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR'05), 2005. doi:10.1109/ISMAR.2005.8 <ref>Wuest, Harald, Florent Vial, and D. Strieker. Adaptive line tracking with multiple hypotheses for augmented reality. Fourth IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR'05), 2005. doi:10.1109/ISMAR.2005.8</ref>


15. I. Gordon and D. Lowe. Scene Modelling, Recognition and Tracking with Invariant Image Features, Conference: Mixed and Augmented Reality, 2004. ISMAR 2004.
20. ''K. Grauman and T. Darrell.'' [https://ieeexplore.ieee.org/document/1544890 '''The pyramid match kernel: Discriminative classification with sets of image features.'''] Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, 2005. doi:10.1109/ICCV.2005.239 <ref>K. Grauman and T. Darrell. The pyramid match kernel: Discriminative classification with sets of image features. Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, 2005. doi:10.1109/ICCV.2005.239</ref>
https://www.cs.ubc.ca/~lowe/papers/gordon04.pdf


25. D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” IJCV, vol. 60, no. 2, pp. 91–110, 2004
21. ''I. Gordon and D. Lowe.'' [https://www.cs.ubc.ca/~lowe/papers/gordon04.pdf '''Scene Modelling, Recognition and Tracking with Invariant Image Features.'''] Conference: Mixed and Augmented Reality, 2004. ISMAR 2004. <ref>I. Gordon and D. Lowe. Scene Modelling, Recognition and Tracking with Invariant Image Features. Conference: Mixed and Augmented Reality, 2004. ISMAR 2004.</ref>


6. ''R. Hartley, A. Zisserman.'' '''Multiple View Geometry in Computer Vision. Cambridge University Press,''' 2003. <ref>R. Hartley, A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, 2003.</ref>
22. ''D. G. Lowe.'' [https://link.springer.com/article/10.1023/B:VISI.0000029664.99615.94 '''Distinctive image features from scale-invariant keypoints.'''] International Journal of Computer Vision (2004) vol. 60, no. 2, pp. 91–110. doi:10.1023/B:VISI.0000029664.99615.94 <ref>D. G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision (2004) vol. 60, no. 2, pp. 91–110. doi:10.1023/B:VISI.0000029664.99615.94</ref>


14. T. Drummond, R. Cipolla. Real-Time Visual Tracking of Complex Structures,  IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, i. 7, July 2002.
23. ''R. Hartley, A. Zisserman.'' '''Multiple View Geometry in Computer Vision. Cambridge University Press,''' 2003. ISBN: 0521623049 <ref>R. Hartley, A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, 2003.ISBN: 0521623049</ref>
https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1017620
 
24. ''T. Drummond, R. Cipolla.'' [https://ieeexplore.ieee.org/abstract/document/1017620 '''Real-Time Visual Tracking of Complex Structures.'''] IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, i. 7, July 2002. doi:10.1109/TPAMI.2002.1017620 <ref>T. Drummond, R. Cipolla. Real-Time Visual Tracking of Complex Structures. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, i. 7, July 2002. doi:10.1109/TPAMI.2002.1017620</ref>


----
----
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===2000 - ...===
===2000 - ...===


23. Michael Isard and Andrew Blake. 1998. CONDENSATION - Conditional Density Propagation for Visual Tracking. In International Journal of Computer Vision. 5–28.
25. ''M. Isard and A. Blake.'' [https://link.springer.com/article/10.1023/A:1008078328650 '''CONDENSATION – Conditional Density Propagation for Visual Tracking.'''] In International Journal of Computer Vision. August 1998, Volume 29, Issue 1, pp 5–28. doi:10.1023/A:100807832 <ref>M. Isard and A. Blake. CONDENSATION – Conditional Density Propagation for Visual Tracking. In International Journal of Computer Vision. August 1998, Volume 29, Issue 1, pp 5–28. doi:10.1023/A:100807832</ref>
 
26. ''R. Storn, K. Price.'' [https://link.springer.com/article/10.1023/A:1008202821328 '''Differential Evolution – a simple and efficient heuristic for global optimization over continuous spaces.'''] Journal of Global Optimization, December 1997, Volume 11, Issue 4. pp 341–359. doi:10.1023/A:100820282. <ref>R. Storn, K. Price. Differential Evolution – a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, December 1997, Volume 11, Issue 4. pp 341–359. doi:10.1023/A:100820282.</ref>


24. R. Storn, K. Price, Differential Evolution – a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, December 1997, Volume 11, Issue 4, pp 341–359. http://www1.icsi.berkeley.edu/~storn/TR-95-012.pdf


12. M. Armstrong A. Zisserman. Robust Object Tracking, Asian Conference on Computer Vision, vol. 1, pp. 58-61, 1995. http://www.robots.ox.ac.uk:5000/~vgg/publications/1995/Armstrong95/armstrong95.pdf
27. ''M. Armstrong A. Zisserman.'' [http://www.robots.ox.ac.uk:5000/~vgg/publications/1995/Armstrong95/armstrong95.pdf '''Robust Object Tracking.'''] Proceedings of the 2nd Asian Conference on Computer Vision, vol. 1. pp. 58–62, 1995. <ref>M. Armstrong A. Zisserman. Robust Object Tracking. Proceedings of the 2nd Asian Conference on Computer Vision, 1995, vol. 1. pp. 58–62, 1995.</ref>


8. Chris Harris and Carl Stennet. 1990. RAPID – A video-Rate Object Tracker. British Machine Vision Conference.
28. ''C. Harris and C. Stennet.'' [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.364.2382&rep=rep1&type=pdf '''RAPID – A video-Rate Object Tracker.'''] British Machine Vision Conference, 1990. <ref>C. Harris and C. Stennet. RAPID – A video-Rate Object Tracker. British Machine Vision Conference, 1990.</ref>


28. J. Canny, “A computational approach to edge detection,” PAMI, pp. 679–698, 1986.
29. ''J. Canny.'' [https://ieeexplore.ieee.org/document/4767851 '''A computational approach to edge detection.'''] IEEE Transactions on Pattern Analysis and Machine Intelligence (Volume: PAMI-8, Issue: 6, Nov. 1986). doi:10.1109/TPAMI.1986.4767851 <ref>J. Canny. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence (Volume: PAMI-8, Issue: 6, Nov. 1986). doi:10.1109/TPAMI.1986.4767851</ref>


2. ''Pierre Alfred Leon Ciraud.'' '''[https://patents.google.com/patent/DE2263777A1/en A method and apparatus for manufacturing objects made of any arbitrary material meltable.]''' German patent application DE2263777A1. December 28, 1971. <ref>Pierre Alfred Leon Ciraud. A method and apparatus for manufacturing objects made of any arbitrary material meltable. German patent application DE2263777A1. December 28, 1971.</ref>
30. ''Pierre Alfred Leon Ciraud.'' '''[https://patents.google.com/patent/DE2263777A1/en A method and apparatus for manufacturing objects made of any arbitrary material meltable.]''' German patent application DE2263777A1. December 28, 1971. <ref>Pierre Alfred Leon Ciraud. A method and apparatus for manufacturing objects made of any arbitrary material meltable. German patent application DE2263777A1. December 28, 1971.</ref>


The first patent in the field of additive manufacturing.
The first patent in the field of additive manufacturing.
Line 118: Line 119:




21. M. Lowney, A.S.Raj. Model Based Tracking for Augmented Reality an Mobile Devices. Dept. of Electrical Engineering, Stanford university.
? 29. M. Lowney, A.S.Raj. Model Based Tracking for Augmented Reality an Mobile Devices. Dept. of Electrical Engineering, Stanford university.




==References==
==References==

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Literature Review

Aliaksei Petsiuk (talk) 14:32, 20 May 2019 (PDT)

MOST Papers

1. Nuchitprasitchai, S., Roggemann, M.C. & Pearce, J.M. Three Hundred and Sixty Degree Real-Time Monitoring of 3-D Printing Using Computer Analysis of Two Camera Views. J. Manuf. Mater. Process. 2017, 1(1), 2; doi:10.3390/jmmp1010002. [1]

Abstract Prosumer (producing consumer)-based desktop additive manufacturing has been enabled by the recent radical reduction in 3-D printer capital costs created by the open-source release of the self-replicating rapid prototype (RepRap). To continue this success, there have been some efforts to improve reliability, which are either too expensive or lacked automation. A promising method to improve reliability is to use computer vision, although the success rates are still too low for widespread use. To overcome these challenges an open source low-cost reliable real-time optimal monitoring platform for 3-D printing from double cameras is presented here. This error detection system is implemented with low-cost web cameras and covers 360 degrees around the printed object from three different perspectives. The algorithm is developed in Python and run on a Raspberry Pi3 mini-computer to reduce costs. For 3-D printing monitoring in three different perspectives, the systems are tested with four different 3-D object geometries for normal operation and failure modes. This system is tested with two different techniques in the image pre-processing step: SIFT and RANSAC rescale and rectification, and non-rescale and rectification. The error calculations were determined from the horizontal and vertical magnitude methods of 3-D reconstruction images. The non-rescale and rectification technique successfully detects the normal printing and failure state for all models with 100% accuracy, which is better than the single camera set up only. The computation time of the non-rescale and rectification technique is two times faster than the SIFT and RANSAC rescale and rectification technique.

Notes

  • ---
  • ---
  • ---

2019

2. SONY IMX322 Datasheet (accessed on 16 May 2019). [2]

3. Marlin Open-Source RepRap Firmware (accessed on 16 May 2019). [3]

4. OpenCV (Open Source Computer Vision Library) (accessed on 20 May 2019). [4]

5. ViSP (Visual Servoing Platform), a modular cross-platform library for visual servoing tasks (accessed on 20 May 2019). [5]

2018

6. Wohlers Report. Annual worldwide progress report in 3D Printing, 2018. [6]

7. U. Delli, S. Chang. Automated processes monitoring in 3D printing using supervised machine learning. Procedia Manufacturing 26 (2018) 865–870, doi.org/10.1016/j.promfg.2018.07.111 [7]

8. L. Scime, J. Beuth. Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm. Additive Manufacturing 19 (2018) 114–126. doi.org/10.1016/j.addma.2017.11.009 [8]


2017

9. B. Wang, F. Zhong, X. Qin. Pose Optimization in Edge Distance Field for Textureless 3D Object Tracking. CGI'17 Proceedings of the Computer Graphics International Conference, Article No. 32. doi:10.1145/3095140.3095172 [9]

10. K. Garanger, E. Feron, P-L. Garoche, J. Rimoli, J. Berrigan, M. Grover, K. Hobbs. Foundations of Intelligent Additive Manufacturing. Published in ArXiv, May 2017. [10]

2016

11. F. Thewihsen, S. Karevska, A. Czok, C. Pateman-Jones, D. Krauss. EY’s Global 3D printing Report. 2016. [11]

12. J. Fastowicz, K. Okarma. Texture based quality assessment of 3D prints for different lighting conditions. In Proceedings of the International Conference on Computer Vision and Graphics, ICCVG (2016), 17-28. In: Chmielewski L., Datta A., Kozera R., Wojciechowski K. (eds) Computer Vision and Graphics. ICCVG 2016. Lecture Notes in Computer Science, vol 9972. Springer, Cham. doi:10.1007/978-3-319-46418-3_2 [12]

2015

13. O. Ronneberger, P. Fischer, T. Brox. U-Net: Convolutional Networks for Biomedical Image Segmentation. MICCAI 2015: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 pp 234-241. In: Navab N., Hornegger J., Wells W., Frangi A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science, vol 9351. Springer, Cham. doi:10.1007/978-3-319-24574-4_28 [13]

2014

14. A. Crivellaro; V. Lepetit. Robust 3D Tracking with Descriptor Fields. 2014. 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR'14), 2014. doi:10.1109/CVPR.2014.436 [14]


2012

15. C. Choi, H. Christensen. 3D Textureless Object Detection and Tracking: An Edge-based Approach. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012. doi:10.1109/IROS.2012.6386065 [15]

16. M.Oikawa, M.Fujisawa. Local quadrics surface approximation for real-time tracking of textureless 3D rigid curved objects. 14th Symposium on Virtual and Augmented Reality, 2012. doi:10.1109/SVR.2012.3 [16]

2010-2000

17. J. Barandiaran, D. Borro. Edge-based markerless 3D tracking of rigid objects. 17th International Conference on Artificial Reality and Telexistence (ICAT 2007). doi:10.1109/ICAT.2007.62 [17]

18. Gordon I., Lowe D.G. What and Where: 3D Object Recognition with Accurate Pose. In: Ponce J., Hebert M., Schmid C., Zisserman A. (eds) Toward Category-Level Object Recognition. Lecture Notes in Computer Science, vol 4170. Springer, 2006. doi:10.1007/11957959_4 [18]

19. Wuest, Harald, Florent Vial, and D. Strieker. Adaptive line tracking with multiple hypotheses for augmented reality. Fourth IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR'05), 2005. doi:10.1109/ISMAR.2005.8 [19]

20. K. Grauman and T. Darrell. The pyramid match kernel: Discriminative classification with sets of image features. Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, 2005. doi:10.1109/ICCV.2005.239 [20]

21. I. Gordon and D. Lowe. Scene Modelling, Recognition and Tracking with Invariant Image Features. Conference: Mixed and Augmented Reality, 2004. ISMAR 2004. [21]

22. D. G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision (2004) vol. 60, no. 2, pp. 91–110. doi:10.1023/B:VISI.0000029664.99615.94 [22]

23. R. Hartley, A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, 2003. ISBN: 0521623049 [23]

24. T. Drummond, R. Cipolla. Real-Time Visual Tracking of Complex Structures. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, i. 7, July 2002. doi:10.1109/TPAMI.2002.1017620 [24]

2000 - ...

25. M. Isard and A. Blake. CONDENSATION – Conditional Density Propagation for Visual Tracking. In International Journal of Computer Vision. August 1998, Volume 29, Issue 1, pp 5–28. doi:10.1023/A:100807832 [25]

26. R. Storn, K. Price. Differential Evolution – a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, December 1997, Volume 11, Issue 4. pp 341–359. doi:10.1023/A:100820282. [26]


27. M. Armstrong A. Zisserman. Robust Object Tracking. Proceedings of the 2nd Asian Conference on Computer Vision, vol. 1. pp. 58–62, 1995. [27]

28. C. Harris and C. Stennet. RAPID – A video-Rate Object Tracker. British Machine Vision Conference, 1990. [28]

29. J. Canny. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence (Volume: PAMI-8, Issue: 6, Nov. 1986). doi:10.1109/TPAMI.1986.4767851 [29]

30. Pierre Alfred Leon Ciraud. A method and apparatus for manufacturing objects made of any arbitrary material meltable. German patent application DE2263777A1. December 28, 1971. [30]

The first patent in the field of additive manufacturing.


? 29. M. Lowney, A.S.Raj. Model Based Tracking for Augmented Reality an Mobile Devices. Dept. of Electrical Engineering, Stanford university.


References

  1. ↑ Nuchitprasitchai, S., Roggemann, M.C. & Pearce, J.M. Three Hundred and Sixty Degree Real-Time Monitoring of 3-D Printing Using Computer Analysis of Two Camera Views. J. Manuf. Mater. Process. 2017, 1(1), 2; doi:10.3390/jmmp1010002.
  2. ↑ SONY IMX322 Datasheet. SONY, 2019 (accessed on 16 May 2019).
  3. ↑ Marlin Open-Source RepRap Firmware (accessed on 16 May 2019).
  4. ↑ OpenCV (Open Source Computer Vision Library). Available online: https://opencv.org/ (accessed on 20 May 2019).
  5. ↑ ViSP (Visual Servoing Platform), a modular cross-platform library for visual servoing tasks. Available online: https://visp.inria.fr/ (accessed on 20 May 2019).
  6. ↑ Wohlers Report. Annual worldwide progress report in 3D Printing, 2018.
  7. ↑ U. Delli, S. Chang. Automated processes monitoring in 3D printing using supervised machine learning.Procedia Manufacturing 26 (2018) 865–870, doi.org/10.1016/j.promfg.2018.07.111
  8. ↑ L. Scime, J. Beuth. Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm. Additive Manufacturing 19 (2018) 114–126. doi.org/10.1016/j.addma.2017.11.009
  9. ↑ B. Wang, F. Zhong, X. Qin, Pose Optimization in Edge Distance Field for Textureless 3D Object Tracking, CGI'17 Proceedings of the Computer Graphics International Conference, Article No. 32. doi:10.1145/3095140.3095172
  10. ↑ K. Garanger, E. Feron, P-L. Garoche, J. Rimoli, J. Berrigan, M. Grover, K. Hobbs. Foundations of Intelligent Additive Manufacturing. Published in ArXiv, May 2017.
  11. ↑ F. Thewihsen, S. Karevska, A. Czok, C. Pateman-Jones, D. Krauss. EY’s Global 3D printing Report, 2016.
  12. ↑ J. Fastowicz, K. Okarma. Texture based quality assessment of 3D prints for different lighting conditions. In Proceedings of the International Conference on Computer Vision and Graphics, ICCVG (2016), 17-28. In: Chmielewski L., Datta A., Kozera R., Wojciechowski K. (eds) Computer Vision and Graphics. ICCVG 2016. Lecture Notes in Computer Science, vol 9972. Springer, Cham. doi:10.1007/978-3-319-46418-3_2
  13. ↑ O. Ronneberger, P. Fischer, T. Brox. U-Net: Convolutional Networks for Biomedical Image Segmentation. MICCAI 2015: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 pp 234-241. In: Navab N., Hornegger J., Wells W., Frangi A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science, vol 9351. Springer, Cham. doi:10.1007/978-3-319-24574-4_28
  14. ↑ A. Crivellaro; V. Lepetit. Robust 3D Tracking with Descriptor Fields. 2014. 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR'14), 2014. doi:10.1109/CVPR.2014.436
  15. ↑ C. Choi, H. Christensen. 3D Textureless Object Detection and Tracking: An Edge-based Approach. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012. doi:10.1109/IROS.2012.6386065
  16. ↑ M.Oikawa, M.Fujisawa. Local quadrics surface approximation for real-time tracking of textureless 3D rigid curved objects. 14th Symposium on Virtual and Augmented Reality, 2012. doi:10.1109/SVR.2012.3
  17. ↑ J. Barandiaran, D. Borro. Edge-based markerless 3D tracking of rigid objects. 17th International Conference on Artificial Reality and Telexistence (ICAT 2007). doi:10.1109/ICAT.2007.62
  18. ↑ Gordon I., Lowe D.G. What and Where: 3D Object Recognition with Accurate Pose. In: Ponce J., Hebert M., Schmid C., Zisserman A. (eds) Toward Category-Level Object Recognition. Lecture Notes in Computer Science, vol 4170. Springer, 2006. doi:10.1007/11957959_4
  19. ↑ Wuest, Harald, Florent Vial, and D. Strieker. Adaptive line tracking with multiple hypotheses for augmented reality. Fourth IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR'05), 2005. doi:10.1109/ISMAR.2005.8
  20. ↑ K. Grauman and T. Darrell. The pyramid match kernel: Discriminative classification with sets of image features. Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, 2005. doi:10.1109/ICCV.2005.239
  21. ↑ I. Gordon and D. Lowe. Scene Modelling, Recognition and Tracking with Invariant Image Features. Conference: Mixed and Augmented Reality, 2004. ISMAR 2004.
  22. ↑ D. G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision (2004) vol. 60, no. 2, pp. 91–110. doi:10.1023/B:VISI.0000029664.99615.94
  23. ↑ R. Hartley, A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, 2003.ISBN: 0521623049
  24. ↑ T. Drummond, R. Cipolla. Real-Time Visual Tracking of Complex Structures. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, i. 7, July 2002. doi:10.1109/TPAMI.2002.1017620
  25. ↑ M. Isard and A. Blake. CONDENSATION – Conditional Density Propagation for Visual Tracking. In International Journal of Computer Vision. August 1998, Volume 29, Issue 1, pp 5–28. doi:10.1023/A:100807832
  26. ↑ R. Storn, K. Price. Differential Evolution – a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, December 1997, Volume 11, Issue 4. pp 341–359. doi:10.1023/A:100820282.
  27. ↑ M. Armstrong A. Zisserman. Robust Object Tracking. Proceedings of the 2nd Asian Conference on Computer Vision, 1995, vol. 1. pp. 58–62, 1995.
  28. ↑ C. Harris and C. Stennet. RAPID – A video-Rate Object Tracker. British Machine Vision Conference, 1990.
  29. ↑ J. Canny. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence (Volume: PAMI-8, Issue: 6, Nov. 1986). doi:10.1109/TPAMI.1986.4767851
  30. ↑ Pierre Alfred Leon Ciraud. A method and apparatus for manufacturing objects made of any arbitrary material meltable. German patent application DE2263777A1. December 28, 1971.
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