К.А. Кивва, И.В. Рудаков
42
ISSN 0236-3933. Вестник МГТУ им. Н.Э. Баумана. Сер. Приборостроение. 2017. № 2
2.
Silveira J., Ferreira M.J., Santos C., Martins T.
Computer vision techniques applied to the quality
control of ceramic plates // Proc. IEEE international Conference on Industrial Technology.
Gippsland: ICIT, 2009.
URL:
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.973.1308&rep=rep1&type=pdf(дата обращения: 15.01.2017).
3.
Kuzmanić I., Vujović I., Šoda J.
Damage detection in materials based on computer vision wavelet
algorithm, advanced structured materials. Heidelberg, Germany: Springer Cham Heidelberg, 2014.
P. 157–186.
4.
Obstacle
avoidance system for UAVs using computer vision / Blin Richards, John Dayton,
Miguel Enriquez, Mathew Gan, James Liu, Jordan Quintana // Cal Poly Pomona Student RSCA
Conference. Pomona: California State Polytechnic University, 2014.
5.
Guizzo E.
How Google's self-driving car works // IEEE Spectrum: веб-сайт.
URL:
http://spectrum.ieee.org/automaton/robotics/artificial-intelligence/how-google-self-driving-car-works (дата обращения: 09.01.2017).
6.
Lepetit V.
On computer vision for augmented reality // International Symposium on Ubiquitous
Virtual Reality. 2008. P. 13–16. DOI: 10.1109/ISUVR.2008.10
URL:
http://ieeexplore.ieee.org/document/45686357.
Forsyth D.A., Ponce J.
Computer vision: a modern approach. 2nd Edition. Upper Saddle River,
New Jersey: Pearson Education, Inc., 2011. 793 с.
8.
Кормен Т.Х., Лейзерсон Ч.И., Ривест Р.Л., Штайн К.
Алгоритмы: построение и анализ.
М.: Вильямс, 2015. 1328 с.
9.
Быкова В.В.
Математические методы анализа рекурсивных алгоритмов // Журнал СФУ.
Сер. Математика и физика. 2008. Т. 1. № 3. С. 236–246.
URL:
http://elib.sfukras.ru/bitstream/handle/2311/772%20%20%20%20%20%20.pdf;jsessionid=B504A2DC25BD7FACCC6562B8810DFCF0?sequence=1
10.
Murphy К., Torralba А., Eaton D., Freeman W.
Object detection and localization using local
and global features // Toward category-level object recognition. Heidelberg, Germany: Springer
Berlin Heidelberg, 2006. С. 382–400. DOI: 10.1007/11957959_20
URL:
http://link.springer.com/chapter/10.1007/11957959_2011.
Erhan D., Szegedy С., Toshev А., Anguelov D.
Scalable object detection using deep neural net-
works // IEEE Conference on Computer Vision and Pattern Recognition. 2014. P. 2155–2162.
DOI: 10.1109/CVPR.2014.276 URL:
http://ieeexplore.ieee.org/document/690967312.
Dalal N., Triggs B.
Histograms of oriented gradients for human detection // IEEE Computer
Society Conference on Computer Vision and Pattern Recognition (CVPR'05). 2005. Vol. 1.
P. 886–893. IEEE Computer Society Conference on Computer Vision and Pattern Recognition
(CVPR'05) DOI: 10.1109/CVPR.2005.177 URL:
http://ieeexplore.ieee.org/document/146736013.
Corvee E.
Body parts detection for people tracking using trees of histogram of oriented gradient
descriptors // 7th IEEE International Conference on Advanced Video and Signal Based Surveil-
lance. 2010. P. 469–475. DOI: 10.1109/AVSS.2010.51
URL:
http://ieeexplore.ieee.org/document/559709314.
Paul V., Jones M.
Robust real-time face detection // International Journal of Computer Vision.
2004. Vol. 57. No. 2. P. 137–154. DOI: 10.1023/B
:VISI.0000013087.49260.fbURL:
http://link.springer.com/article/10.1023/B%3AVISI.0000013087.49260.fb15.
Rokach L., Maimon O.
Classification trees, data mining and knowledge discovery handbook.
New York: Springer US, 2010. P. 149–151.