Журнал «Труды Института системного анализа Российской академии наук» - A.A. Ivanova, S.A. Gladilin, A.E. Zhukovsky, E.L. Pliskin Database for the administrative accounting of scientific publications

Просматривается номер 2018-S1

Data mining and image recognition

N.S. Skoryukina, A.N. Milovzorov, D.V. Polevoy, V.V. Arlazarov Paintings recognition in uncontrolled conditions using one-shot learning

A.E. Zhukovsky Methods for interframe integration of document detection results in a video stream of a mobile device

I.A. Kunina, E. I. Panfilova, M.A. Povolotskiy Zebra-crossing detection on road images using dynamic time warping

O.A. Slavin, V.L. Arlazarov Method for classifying recognized pages of administrative documents on the basis of text key points

O.O. Petrova, K.B. Bulatov Methods of machine-readable zone recognition results post-processing

A.E. Marchenko, E.I. Ershov, D.A. Shepelev, D.S. Sidorchuk, V.P. Bozhkova, D.P. Nikolaev Designing of language of description of observable properties of recognized objects in the absence of samples

Intellectual systems and technologies

E.E. Limonova, N.L. Rzhenev, A.V. Uskov, M.I. Neiman-zade Fast implementation of Hamming distance on VLIW-architectures on the example of Elbrus platform

V.V. Arlazarov, K.B. Bulatov, A.V. Uskov A model of object recognition system in video stream of a mobile device

A.A. Ivanova, S.A. Gladilin, A.E. Zhukovsky, E.L. Pliskin Database for the administrative accounting of scientific publications

A.S. Ingacheva, A.V. Sheshkus, T. S. Chernov, E.E. Limonova, V.V. Arlazarov X-ray computed tomography scanner – a new tool in recognition

N.O. Beshaposhnikov, A.G. Kushnirenko, A.A. Levin A method for auto-calibration of the educational robot control parameters using computer vision library OpenCV

Image and signal processing

A.E. Zhukovsky, E.E. Limonova, D.P. Nikolaev Exact implementation of common image processing algorithms using fully convolutional networks

V.E. Prun Reducing the influence of high-absorbing inclusions on CT reconstructions using algebraic reconstruction technique

B.I. Savelyev, I.B. Mamay, D.P. Nikolaev, V.L. Arlazarov, K.B. Bulatov, N.S. Skoryukina A method of projective transformations graph adjustment for panorama stitching problem for images of planar objects

D.V. Tropin, D.P. Nikolaev, D.G. Slugin The method of image alignment based on sharpness maximization

J.A. Shemiakina, A.E. Zhukovsky, I.A. Konovalenko, D.P. Nikolaev Algorithm for automatic framing of digital images under projective transformation

MACHINE LEARNING

A.V. Gayer, A.V. Sheshkus, Y.S. Chernyshova Augmentation on the fly for the neural networks learning

V.V. Arlazarov, D.P. Matalov, S.A. Usilin Localization of the seal on the identity document image using machine learning approach

A.E. Lynchenko, A.V.Sheshkus, V.L.Arlazarov Identity document classifiaction algorithm based on similarity metric robust to projective distortions

V.A. Malykh, V.A. Lyalin On Classification of Noisy Texts

Y.S. Chernyshova, M.A. Aliev, A.V. Sheshkus Optical font recognition of images captured with mobile devices and its application for detecting identity documents forgery

D.A. Ilin Fast words boundaries localization in text fields for low quality document images

D.E. Ivanov, D.V. Polevoy, D.L. Sholomov Selection of informative elements for the training of a lightweight convolutional neural network classifier in the conditions of a strong imbalance of the training sample


	A.A. Ivanova, S.A. Gladilin, A.E. Zhukovsky, E.L. Pliskin Database for the administrative accounting of scientific publications
Abstract. The article considers software requirements for administrative accounting of scientific publications. We design the database structure, taking into account various aspects of the publication activity of the scientific team. Keywords: bibliographic references, scientific publications, database, publication activity, scientometrics, administration of scientific research. PP. 83-89. DOI: 10.14357/20790279180509 References 1. Strakhov A.A., Anisimova Т.V. Automation of bibliographic description of sources and links in the MS Word 2010 document // Bulletin of the Moscow University of the Ministry of Internal Affairs of Russia. 2017. №5. 2. I.V. Artemova. Accounting for R & D by the performer in 2018. URL: https://www.referent.com/40/11763 (retrieved 10.07.2018). 3. Informational and analytical system “TRUE”. User guide. Subsystem “Research work”. URL: http://docs.istina.msu.ru/data_input/research.html (retrieved 10.07.2018). 4. Website SNOSKA.INFO for registration of bibliographic references. URL: http://www.snoskainfo.ru/ (retrieved 10.07.2018). 5. The ZoteroBib website for creating bibliographic lists. URL: https://zbib.org (retrieved 10.07.2018). 6. Gospodarik Yu.P. Accounting for individual achievements of students in research and development / / Higher education in Russia. 2013. №3. URL: https://cyberleninka.ru/article/n/uchet-individualnyh-dostizheniy-studentov-vnauchno- issledovatelskoy-deyatelnosti (retrieved 10.07.2018). 7. Nikolenko V.N., Vyalkov A.I., Martynchik S.A., Glukhova E.A. Approaches to the evaluation of effectiveness and ways to stimulate the publication activity in a major medical college // Higher education in Russia. 2014. №10. URL: https://cyberleninka.ru/article/n/podhody-kotsenke-effektivnosti-i-sposoby-stimulirovaniyap u b l i k a t s i o n n o y - a k t i v n o s t i - v - k r u p n o m - meditsinskom-vuze (retrieved 10.07.2018). 8. Van Eck, Nees Jan, and Ludo Waltman. “CitNetExplorer: A new software tool for analyzing and visualizing citation networks.” Journal of Informetrics 8.4 (2014): 802-823. 9. Pearce, Joshua M. “How to Perform a Literature Review with Free and Open Source Software.” Practical Assessment, Research & Evaluation 23.8 (2018): 2. URL: https://research.aalto.fi/files/21756617/ELEC_Pearce_How_to_perform_PaRE.pdf (retrieved 10.07.2018). 10. Mingers, John and Loet Leydesdorff. “A review of theory and practice in scientometrics.” European Journal of Operational Research 246.1 (2015): 1-19. URL: https://arxiv.org/ftp/ arxiv/papers/1501/1501.05462.pdf (retrieved 10.07.2018). 11. Ulanin S.E. Virtual research environment // Bulletin of the State University of Management. 2017. №2. URL: https://cyberleninka.ru/article/n/ virtualnaya-nauchno-issledovatelskaya-sreda (retrieved 11.07.2018). 12. K. Bulatov, V.V. Arlazarov, T. Chernov, O. Slavin and D. Nikolaev. “Smart IDReader: Document Recognition in Video Stream,” 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), Kyoto, 2017, pp. 39-44. doi: 10.1109/ICDAR.2017.347 13. A. Zhukovsky et al. “Segments Graph-Based Approach for Document Capture in a Smartphone Video Stream,” 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), Kyoto, 2017, pp. 337-342. doi: 10.1109/ICDAR.2017.63 14. T.S. Chernov, N.P. Razumnuy, A.S. Kozharinov, D.P. Nikolaev and V.V. Arlazarov. “Image quality assessment for video stream recognition systems,” Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106961U, pp. 1-8, 2018, DOI: 10.1117/12.2309628. 15. D. Ilin, E. Limonova, V. Arlazarov and D. Nikolaev. “Fast Integer Approximations In Convolutional Neural Networks Using Layer-By-Layer Training,” Proceedings SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 103410Q, pp. 1-5, 2017, DOI: 10.1117/12.2268722. 16. V.V. Arlazarov, O.A. Slavin, A.V. Uskov and I.M. Yanishevskiy. “Modelling the flow of character recognition results in video stream,” Bulletin of the South Ural State University. Ser. Mathematical Modelling, Programming & Computer Software, vol. 11, no 2, pp. 14-28, 2018. 17. D. Abulkhanov, I. Konovalenko, D. Nikolaev, A. Savchik, E. Shvets and D. Sidorchuk. “Neural Network-based Feature Point Descriptors for Registration of Optical and SAR Images,” Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106960L, pp. 1-8, 2018, DOI: 10.1117/12.2310085. 18. Ingacheva A., Nikolaev D., Khanipov T., Chukalina M. Algebraic reconstruction of the hardware function of the blurred image along the brightness profiles of object boundaries // Sensory systems. – 2018. – Vol. 32. – No. 1. – P. 67-72. – DOI: 10.7868 / S0235009218010109. 19. T.S. Chernov, S.I. Kolmakov and D.P. Nikolaev. “An algorithm for detection and phase estimation of protective elements periodic lattice on document image,” Pattern Recognition and Image Analysis, vol. 27, no 1, pp. 53-65, 2017, DOI: 10.1134/S1054661817010023.

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