Depth map reconstruction based on features formed by descriptor of stereo color pairs

Cover Page

Abstract


Novel local image descriptor that is tested in the computer vision problem is proposed. The designed descriptor is based on visual primitives and relations between them, namely, coplanarity, cocolority, distance and angle. The designed feature descriptor covers both geometric and appearance information. Proposed descriptor has demonstrated its ability to compute depth maps from image pairs where the performance evaluation via criterion Bad Matching Pixels has shown it superior quality in comparison with other descriptors from state-of-the-art methods.


About the authors

V. F. Kravchenko

Kotelnikov Institute of Radio Engineering and Electronics of the Russian Academy of Sciences;Scientific and Technological Centre of Unique Instrumentation of the Russian Academy of Sciences; Bauman Moscow State Technical University

Email: vponomar@ipn.mx

Russian Federation, 11-7, Mokhovaya street, Moscow, 125009; 15, Bytlerova street, Moscow, 117342; 5, 2-nd Baumanskaya, Moscow, 105005

V. I. Ponomaryov

Instituto Politécnico Nacional

Author for correspondence.
Email: vponomar@ipn.mx

Mexico, Nueva Industrial Vallejo, Ciudad de México, 07738

V. I. Pustovoit

Scientific and Technological Centre of Unique Instrumentation of the Russian Academy of Sciences

Email: vponomar@ipn.mx

Russian Federation, 15, Bytlerova street, Moscow, 117342

Academician of the Russian Academy of Sciences

D. Rosas-Miranda

Instituto Politécnico Nacional

Email: vponomar@ipn.mx

Mexico, Nueva Industrial Vallejo, Ciudad de México, 07738

References

  1. Ramos-Diaz E., Kravchenko V., Ponomaryov V. // EURASIP J. Adv. Signal Process. 2011. P. 1-10.
  2. Кравченко В. Ф., Пономарев В. И., Пустовойт В. И. // ДАН. 2014. Т. 459. № 1. С. 32-36.
  3. Кравченко В. Ф., Пономарев В. И., Пустовойт В. И. // ДАН. 2015. Т. 465. № 3. С. 293-297.
  4. Кравченко В. Ф., Пономарев В. И., Пустовойт В. И., Садовничий С. Н. // ДАН. 2017. Т. 475. № 5. С. 514-518.
  5. Huitron V., Ponomaryov V. // IEEE Lat. Amer. Trans. 2016. V. 14. № 6. P. 2968-2973.
  6. Gonzalez-Huilton V., Ponomaryov V., Ramos-Diaz E., Sadovnychiy S. // Signal, Image, Video Proc. 2018. V. 12. № 2. P. 231-238.
  7. Rosas D., Ponomaryov V., Reyes R. // Int. J. Comput. 2018. V. 17. № 3. P. 171-179.
  8. Kravchenko V. F., Perez-Meana H.M., Ponomaryov V. I. Adaptive Digital Processing of Multidimensional Signals with Applications. Moscow: Fizmatlit, 2009.
  9. Felsberg M., Sommer G. // IEEE Trans. Sig. Proc. 2001. V. 49. № 12. P. 3136-3144.
  10. Xuanzi Y. // Machine Vision and Appl. 2015. V. 26. № 7/8. P. 975-990.
  11. Wan Y., Miao Z., Tang Z., Wan L., Wang Z. // IEICE Trans. Inf. Sys. 2012. V. 95. № 7. P. 2021-2024.
  12. Tola E., Lepetit V., Fua P. // IEEE Trans. Pat. Anal. Mach. Intell. 2010. V. 32. № 5. P. 815-830.
  13. Pugeault N., Wörgötter F., Krüger N. // Int. J. Human. Robot. 2010. V. 7. P. 379-405.
  14. http://vision.middlebury.edu/stereo/data (November 2013).
  15. http://vision.middlebury.edu/stereo/. 2016.

Statistics

Views

Abstract - 90

PDF (Russian) - 74

PlumX


Copyright (c) 2019 Russian academy of sciences

This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies