Volume 32 #1

Contents

1. Variability of different functional songs types from Aelia acuminata L. (Heteroptera, Pentatomidae)
2. Receptive field sizes of sustained ganglion cells in the retina of Carassius gibelio
3. Fusion of radar, visible and thermal imagery with account for differences in brightness and chromaticity perception
4. Modelling of the unmanned aerial vehicle navigation on the basis of two height-shifted onboard cameras
5. Visual localisation of aerial images on vector map using colour-texture segmentation
6. Comparison of the digitized pages of business documents by means of recognition
7. The algorithm of epipole position estimation under pure camera translation
8. Segmentation of vehicle registration plates based on dynamic time warping
9. Finding the correspondence between closed curves under projective distortions
10. Blur kernel estimation with algebraic tomography technique and intensity profiles of object boundaries
11. Weighted search for a projective optical flow resistant to specular reflections
12. Algebraic methods for tomography problem
13. Quantitative analysis of the point light source virtual image doubling in underwater stereo vision

The weighted search algorithm of projective transformation between images of documents based on the generalized version of the Lucas-Canada algorithm is proposed and investigated. The main goal in the method development was a maximization of the result stability to the presence of a specular reflections in the images. The algorithm is able to to take into account the unequal contribution of different pixels when optimizing the target functional. In this paper we used the dichromatic reflection model. We have studied the way algorithms accuracy changes with transformation from the original color space to the subspace invariant to the point of view. Experiments were conducted using formed synthetic and real datasets.

Key words: optical flow, color, document recognition, Lucas-Canada, flare, inear model of spectrumstimulus formation, dichromatic model

DOI: 10.7868/S0235009218010110

References:

• Arlasarov V.V., Zhukovsky A.E., Krivtsov V.E., Nikolaev D.P., Polevoy D.V. Analiz osobennostei ispol'zovaniya statsionarnykh i mobil'nykh malorazmernykh tsifrovykh video kamer dlya raspoznavaniya dokumentov [Analysis of features of the use of fixed and mobile small-sized digital videocamera for OCR]. Informatsionnye tekhnologii i vychislitel'nye sistemy. 2014. (3). P. 71–81 (in Russian).
• Bulatov K.B., Ilin D.A., Polevoy D.V., Chernyshova Y.S. Problemy raspoznavaniya mashinochitaemykh zon s ispol'zovaniem maloformatnykh tsifrovykh kamer mobil'nykh ustroistv [Problems of machine-readable zone recognition captured with digital mobile cameras]. Trudy Instituta Sistemnogo Analiza Rossiiskoi Akademii Nauk. 2015. V. 65 (3). P. 85–93 (in Russian).
• Bulatov K.B., Kirsanov V. Yu., Arlasarov V.V., Nikolaev D.P., Polevoy D.V. Metody integratsii rezul'tatov raspoznavaniya tekstovykh polei dokumentov v videopotoke mobil'nogo ustroistva [Methods for Integration the Results of the Documents Text Fields Recognition in the Videostream of a Mobile Device]. Vestnik Rossiiskogo fonda fundamental'nykh issledovanii. 2016. V 92 (4). P. 109–115 (in Russian).
• Nikolaev P.P. Nekotorye algoritmy uznavaniya okraski poverkhnostei [Some algorithms for recognizing the color of surfaces]. Modelirovanie obucheniya i povedeniya. Moscow. Nauka, 1975. P. 121–151 (in Russian).
• Polevoy D.V., Bulatov K.B., Skoryukina N.S., Chernov T.S., Arlazarov V.V., Sheshkus A.V. Klyuchevye aspekty raspoznavaniya dokumentov s ispol'zovaniem malorazmernykh tsifrovykh kamer [Key Aspects of Document Recognition Using Small Digital Cameras]. Vestnik Rossiiskogo fonda fundamental'nykh issledovanii. 2016. V. 92 (4). P. 97–108 (in Russian).
• Shepelev D.A., Tereshin A.A., Nikolayev P.P., Ershov E.I. O problemakh sopostavleniya pikselei stereopary s tochki zreniya lineinoi modeli formirovaniya tsvetnogo izobrazheniya [Stereo correspondence problems in terms of linear theory of spectral stimulus formation]. Sensornye sistemy [Sensory systems]. 2017. V 31 (2). P. 150–160 [in Russian]).
• Baker S., Matthews I. Lucas-Kanade 20 Years On: A Unifying Framework. Int. J. Comput. Vision. 2004. V. 56 (3). P. 221–255.
• Buades A., Lisani J.-L., Miladinovic M. Patch-Based Video Denoising With Optical Flow Estimation. IEEE Transactions on Image Processing. 2016. V. 25(6). P. 2573–2586.
• Burie J.-C., Chazalon J., Coustaty M., Eskenazi S., Luqman M.M., Mehri M., Nayef N., Ogier J.-M., Prum S., Rusinol M. ICDAR2015 competition on smartphone document capture and OCR (SmartDoc). 2015 13th International Conference on Document Analysis and Recognition (ICDAR). 2015. P. 1161–1165.
• Konovalenko I., Miller A., Miller B., Nikolaev D. UAV navigation on the basis of the feature points detection on underlying surface. Proceedings of the 29th European Conference on Modeling and Simulation (ECMS 2015). 2015. P. 499–505.
• Lin S., Li Yu., Kang S.B., Tong X., Shum H.-Ye. DiffuseSpecular Separation and Depth Recovery from Image Sequences. European conference on computer vision. 2002. P. 210–224.
• Popov A., Miller B., Miller A., Stepanyan K. Optical Flow as a Navigation Means for UAVs with Optoelectronic Cameras. In Proceedings of 56th Israel Annual Conference on Aerospace Sciences. 2016. P. 9–10.
• Shafer S.A. Using color to separate reflection components. Color Research Appl. 1985. V. 10 (4). P. 210–218.
• Valentín K., Wild P., Stolc S., Daubner F., Clabian M. Optical benchmarking of security document readers for automated border control. In SPIE Security+ Defence. 2016. P. 999502–999502.
• Yang Q., Wang S., Ahuja N., Yang R.A. Uniform Framework for Estimating Illumination Chromaticity, Correspondence, and Specular Reflection. IEEE Transactions on Image Processing. 2011. V. 20 (1). P. 53–63.
• Zhou W., Kambhamettu C. Binocular Stereo Dense Matching in the Presence of Specular Reflections. 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06). 2006. P. 2363–2370.
• Zickler T., Mallick S.P., Kriegman D.J., Belhumeur P.N. Color Subspaces as Photometric Invariants. International Journal of Computer Vision. 2008. V. 79 (1). P. 13–30.