• 1990 (Том 4)
  • 1989 (Том 3)
  • 1988 (Том 2)
  • 1987 (Том 1)

Том 35 №3

Содержание

  1. ОЦЕНКА ОСТРОТЫ ЗРЕНИЯ. 1. ПЕРВИЧНЫЕ ИЗМЕРЕНИЯ И РАЗЛИЧНЫЕ МЕРЫ
  2. КОМПЛЕКСНЫЙ АНАЛИЗ ФУНКЦИОНАЛЬНЫХ ПОКАЗАТЕЛЕЙ БИНОКУЛЯРНОЙ ЗРИТЕЛЬНОЙ СИСТЕМЫ У ШКОЛЬНИКОВ
  3. ОКУЛОМОТОРНАЯ АКТИВНОСТЬ ПРИ РЕШЕНИИ ЗРИТЕЛЬНЫХ КОГНИТИВНЫХ ЗАДАЧ В РАЗЛИЧНЫХ ВРЕМЕННЫХ УСЛОВИЯХ
  4. РАЗЛИЧЕНИЕ ЗВУКОВЫХ СИГНАЛОВ С ГРЕБЕНЧАТЫМИ СПЕКТРАМИ В ПРИСУТСТВИИ ДОПОЛНИТЕЛЬНЫХ СИГНАЛОВ
  5. РОЛЬ ПРОЕКТИВНЫХ ПРЕОБРАЗОВАНИЙ В НОРМАЛИЗАЦИИ ИЗОБРАЖЕНИЙ

РОЛЬ ПРОЕКТИВНЫХ ПРЕОБРАЗОВАНИЙ В НОРМАЛИЗАЦИИ ИЗОБРАЖЕНИЙ

© 2021 г. И. A. Koнoвaлeнкo1, П. П. Николаев1,2

1Институт проблем передачи информации им. А.А. Харкевича РАН, 127051 Москва, Большой Каретный переулок, д. 19, Россия
konovalenko@iitp.ru
2Московский физико-технический институт, 141701 г. Долгопрудный, Институтский переулок, д. 9, Россия

Поступила в редакцию 25.03.2021 г.

Анализ сделанного с произвольного ракурса изображения требует его предварительной нормализации – преобразования к такому виду, как если бы оно было получено с удобного для анализа ракурса. Данная работа представляет собой обзор современных методов, критериев точности и приложений нормализации разнообразных типов, а также описывает основные этапы становления этой проблематики. Впервые единообразно рассматриваются два важнейших частных случая нормализации, в литературе традиционно рассматриваемые независимо: первый включает вопросы только геометрического характера, второй рассматривает исключительно цветовые аспекты. Плодотворность этого объединяющего подхода выражается еще и в том, что процедура нормализации оказывается фундаментальным образом привлекающей двумерные и трехмерные проективные преобразования с общим аналитическим аппаратом, безотносительно к цветовому и геометрическому ее истолкованию для практических задач.

Ключевые слова: геометрическая и цветовая нормализация, проективное преобразование, матрица гомографии, среднеквадратичная и максимальная невязки координат, критерии точности нормализации, область интереса

DOI: 10.31857/S0235009221030021

Цитирование для раздела "Список литературы": Konovalenko I. A., Nikolaev P. P. The role of projective transformations in image normalization. Sensornye sistemy [Sensory systems]. 2021. V. 35(3). P. 236–259. doi: 10.31857/S0235009221030021
Цитирование для раздела "References": Konovalenko I. A., Nikolaev P. P. The role of projective transformations in image normalization. Sensornye sistemy [Sensory systems]. 2021. V. 35(3). P. 236–259 . doi: 10.31857/S0235009221030021

Список литературы:

  • Abramov M.P., Shipitko O.C., Grigoryev A.S., Ershov E.I. Poisk tochki skhoda dlya dinamicheskoi kalibrovki vneshnikh parametrov monokulyarnoi kamery pri uslovii pryamolineinogo dvizheniya [Vanishing point detection for monocular camera extrinsic calibration under translation movement] Sensornye sistemy [Sensory systems]. 2020. V. 34 (1). P. 32–43 (in Russian). https://doi.org/10.31857/S0235009220010023
  • Abramov M.P., Shipitko O.S., Lukoyanov A.S., Panfilova E.I., Kunina I.A., Grigoryev A.S. Sistema pozitsionirovaniya vnutri zdanii mobil’noi robototekhnicheskoi platformy na osnove detektsii kraev [Edge detection based mobile robot indoor localization] Sensornye sistemy [Sensory systems]. 2019. V. 33 (1). P. 30–43 (in Russian). https://doi.org/10.1134/S0235009219010025
  • Abulkhanov D.A., Sidorchuk D.S., Konovalenko I.A. Obuchenie neirosetevykh deskriptorov osobykh tochek dlya sopostavleniya radiolokatsionnykh i opticheskikh izobrazhenii [Neural network-based feature point descriptors for registration of optical and SAR images] Sensornye sistemy [Sensory Systems]. 2018. V. 32 (3). P. 222–229 (in Russian). https://doi.org/10.1134/S0235009218030034
  • Alter T.D. 3d pose from 3 corresponding points under weakperspective projection. Technical report. Massachusetts Inst Of Technology Artificial Intelligence Lab. 1992.
  • Aradhye H., Myers G.K. Method and apparatus for recognition of symbols in images of three-dimensional scenes. US Patent. No. 7.738.706. 2010.
  • Arvind C.S., Mishra R., Vishal K., Gundimeda V. Vision based speed breaker detection for autonomous vehicle. Tenth International Conference on Machine Vision (ICMV 2017). International Society for Optics and Photonics. 2018. V. 106960E. P. 1–9.
  • Awal A.M., Ghanmi N., Sicre R., Furon T. Complex document classification and localization application on identity document images. IAPR 2017-International Conference on Document Analysis and Recognition. 2017. P. 427–431. https://doi.org/10.1109/ICDAR.2017.77
  • Balitskiy A.M., Savchik A.V., Gafarov R.F., Konovalenko I.A. O proektivno invariantnykh tochkakh ovala s vydelennoy vneshney pryamoy [On projectively invariant points of an oval with a distinguished exterior line] Problemy peredachi informatsii [Problems of Information Transmission]. 2017. V. 53 (3). P. 84–89 (in Russian).
  • Baltzopoulos V. A video fluoroscopy method for optical distortion correction and measurement of knee-joint kinematics. Clinical Biomechanics. 1995. V. 10 (2). P. 85–92.
  • Bezmaternykh P.V., Ilin D.A., Nikolaev D.P. U-Net-bin: hacking the document image binarization contest. Computer Optics. 2019. V. 43 (5). P. 825–832. https://doi.org/10.18287/2412-6179-2019-43-5-825-832
  • Bezmaternykh P.V., Nikolaev D.P. A document skew detection method using fast Hough transform. Proc. SPIE 11433. Twelfth International Conference on Machine Vision (ICMV 2019). 2020. V. 11433. P. 114330J. https://doi.org/10.1117/12.2559069
  • Bezmaternykh P.V., Nikolaev D.P., Arlazarov V.L. Textual Blocks Rectification Method Based on Fast Hough Transform Analysis in Identity Documents Recognition. Tenth International Conference on Machine Vision (ICMV 2017). International Society for Optics and Photonics. 2018. V. 10696. P. 1069606. https://doi.org/10.1117/12.2310162
  • Bezmaternykh P.V., Vylegzhanin D.V., Gladilin S.A., Nikolaev D.P. Generativnoe raspoznavanie dvumernykh shtrikhkodov. Iskusstvennyi intellekt i prinyatie reshenii [Scientific and Technical Information Processing]. 2010. V. 2010 (4). P. 63–69 (in Russian).
  • Bolotova Yu.A., Spitsyn V.G., Osina P.M. Obzor algoritmov detektirovaniya tekstovykh oblastei na izobrazheniyakh i videozapisyakh [Review Of Algorithms For Text Detection In Images And Videos] Komp’yuternaya optika [Computer Optics]. 2017. V. 41 (3). P. 441–452 (in Russian).
  • Bulatov K., Matalov D., Arlazarov V.V. MIDV-2019: Challenges of the Modern Mobile-Based Document OCR. Proc. SPIE 11433. Twelfth International Conference on Machine Vision (ICMV 2019). 2020. V. 11433. P. 114332N. https://doi.org/10.1117/12.2558438
  • Calderon F., Romero L. An accurate image registration method using a projective transformation model. Eighth Mexican International Conference on Current Trends in Computer Science (ENC2007). IEEE. 2007 P. 58–64.
  • Calore E., Pedersini F., Frosio I. Accelerometer based horizon and keystone perspective correction. Instrumentation and Measurement Technology Conference (I2MTC). 2012 IEEE International. IEEE. 2012. P. 205–209.
  • Chekhlov D.O., Ablameiko S.V. Normalizatsiya izobrazhenii otnositel’no perspektivnogo preobrazovaniya na osnove geometricheskikh parametrov [Normalization of images relating to perspective transformation based on geometric options] Informatika [Informatics]. 2004. V. (3). P. 67–76 (in Russian).
  • Chen H., Sukthankar R., Wallace G., Li K. Scalablealignment of large-format multi-projector displays using camera homography trees. Proc. Conf. Visualization’02. IEEE Computer Society. 2002. P. 339–346.
  • Chernov T.S., Ilin D.A., Bezmaternykh P.V., Faradzhev I.A., Karpenko S.M. Research of Segmentation Methods for Images of Document Textual Blocks Based on the Structural Analysis and Machine Learning. Vestnik RFFI. 2016. I. 4. P. 55–71. (in Russian). https://doi.org/10.22204/2410-4639-2016-092-04-55-71
  • Chochia P.A. Vosstanovlenie amplitudnykh kharakteristik monokhromnykh i mul’tispektral’nykh izobrazhenii, ispol’zuya funktsiyu gradientov [Recovering of the Amplitude Characteristics of Monochrome and Multispectral Images Using the Function of Gradients] Informatsionnye protsessy. 2016. V. 16 (2). P. 112–120. (in Russian).
  • Chukalina M., Ingacheva A., Buzmakov A., Polyakov I., Gladkov A., Yakimchuk I., Nikolaev D. Automatic beam hardening correction for CT reconstruction. Proc. ECMS 2017, European Council for Modeling and Simulation 2017. 2017. P. 270–275. https://doi.org/10.7148/2017-0270
  • Clark A.J., Green R.D., Grant R.N. Perspective correction for improved visual registration using natural features. Image and Vision Computing New Zealand. 2008. IVCNZ 2008. 23rd International Conference. 2008. P. 1–6.
  • Dance C.R. Perspective estimation for document images. Document Recognition and Retrieval IX. International Society for Optics and Photonics. 2001. V. 4670. P. 244–255.
  • Das P., Baslamisli A.S., Liu Y., Karaoglu S., Gevers T. Color constancy by GANs: an experimental survey. 2018. arXiv preprint arXiv:1812.03085.
  • Dubuisson M.P., Jain A.K. A modified hausdorff distance for object matching. Proc. of 12th international conference on patternrecognition. IEEE. 1994. V. 1. P. 566–568.
  • Efimov A.I., Novikov A.I. Algoritm poetapnogo utochneniya proektivnogo preobrazovaniya dlya sovmeshcheniya izobrazhenii [An algorithm for multistage projective transformation adjustment for image superimposition] Komp’yuternaya Optika [Computer Optics]. 2016. V. 40 (2). P. 258–265 (in Russian). https://doi.org/10.18287/2412-6179-2016-40-2-258-265
  • Ershov E., Savchik A., Semenkov I., Banic N., Belokopytov A., Senshina D., Koscevic K., Subasi M., Loncaric S. The Cube++ Illumination Estimation Dataset. IEEE Access. 2020. V. 8. P. 227511–227527. https://doi.org/10.1109/ACCESS.2020.3045066
  • Ershov E.I., Korchagin S.A., Kokhan V.V., Bezmaternykh P.V. A generalization of Otsu method for linear separation of two unbalanced classes in document image binarization. Computer Optics. 2021. V. 45 (1). P. 66–76. https://doi.org/10.18287/2412-6179-CO-752
  • Faugeras O.D. What can be seen in three dimensions with anuncalibrated stereo rig? European conference on computer vision. Springer. 1992. P. 563–578.
  • Finlayson G.D., Funt B.V., Barnard K. Color constancy under varying illumination. Proc. of IEEE International Conference on Computer Vision. 1995. P. 720–725.
  • Finlayson G.D., Gong H., Fisher R.B. Color homography color correction. Color and Imaging Conference. 2016. V. 2016 (1). P. 310–314.
  • Finlayson G.D., Schiele B., Crowley J.L. Comprehensive colour image normalization. European conference on computer vision. 1998. P. 475–490.
  • Forsyth D.A., Ponce J. Computer vision: a modern approach. Prentice Hall Professional Technical Reference, 2002. 720 p.
  • Gayer A.V., Sheshkus A.V., Nikolaev D.P., Arlazarov V.V. Improvement of U-Net Architecture for Image Binarization with Activation Functions Replacement. Thirteenth International Conference on Machine Vision (ICMV 2020). 2021. V. 11605. P. 116050Y. https://doi.org/10.1117/12.2587027
  • Gijsenij A., Gevers T., Van De Weijer J. Computational color constancy: Survey and experiments. IEEE Transactions on Image Processing. 2011. V. 20 (9). P. 2475–2489.
  • Gladkov A.P., Kuznetsova E.G., Gladilin S.A., Gracheva M.A. Adaptivnaya stabilizatsiya yarkosti izobrazheniya v tekhnicheskoi sisteme raspoznavaniya krupnykh dvizhushchikhsya ob'ektov [Adaptive image brightness stabilization for the industrial system of large moving object recognition] Sensornye sistemy [Sensory systems]. 2017. V. 31 (3). P. 247–260 (in Russian). https://doi.org/10.31857/S0235009220010047
  • Gong H., Finlayson G.D., Fisher R.B., Fang F. 3D color homography model for photo-realistic color transfer re-coding. The Visual Computer. 2019. V. 35 (3). P. 323–333.
  • Goshin Y.V., Kotov A.P., Fursov V.A. Dvukhetapnoe formirovanie prostranstvennogo preobrazovaniya dlya sovmeshcheniya izobrazhenii [Two-stage formation of a spatial transformation for image matching] Komp’yuternaya optika [Computer Optics]. 2014. V. 38 (4). P. 886–891 (in Russian).
  • Goshtasby A.A. 2-D and 3-D Image Registration: for Medical, Remote Sensing, and Industrial Applications. John Wiley & Sons, 2005. 280 p.
  • Gruen A. Adaptive least squares correlation: a powerful image matching technique. South African Journal of Photogrammetry. RemoteSensing and Cartography. 1985. V. 14 (3). P. 175–187.
  • Haker S., Zhu L., Tannenbaum A., Angenent S. Optimal mass transport for registration and warping. International Journal of computer vision. 2004. V. 60 (3). P. 225–240.
  • Har-Peled S. New similarity measures between polylines with applications to morphing and polygon sweeping. Discrete & Computational Geometry. 2002. V. 28 (4). P. 535–569.
  • Hartley R., Zisserman A. Multiple view geometry in computer vision. Cambridge, England, Cambridge university press. 2003. 655 p.
  • Healey G. Using color for geometry-insensitive segmentation. JOSAA. 1989. V. 6 (6). P. 920–937.
  • Heckbert P.S. Fundamentals of texture mapping and image warping. University of California. Berkeley. 1989. V. 2 (3). P. 1–86.
  • Hsu S.C., Sawhney H.S. Influence of global constraints and lens distortion on pose and appearance recovery from a purely rotating camera. Applications of Computer Vision. 1998. WACV’98. Proc. of the Fourth IEEE Workshop on. IEEE. 1998. P. 154–159.
  • Huang J.B., Singh A., Ahuja N. Single image super-resolution from transformed self-exemplars. Proc. of the IEEE Conference on Computer Vision and Pattern Recognition. 2015. P. 5197–5206.
  • Huttenlocher D.P., Klanderman G.A., Rucklidge W.J. Comparing images using the hausdorff distance. IEEE Transactions on pattern analysis and machine intelligence. 1993. V. 15 (9). P. 850–863.
  • Ilyuhin S.A., Chernov T.S., Polevoy D.V., Fedorenko F.A. A method for spatially weighted image brightness normalization for face verification. Proc. SPIE 11041. Eleventh International Conference on Machine Vision (ICMV 2018). 2019a. V. 11041. P. 1104118. https://doi.org/10.1117/12.2522922
  • Ilyukhin S.A., Chernov T.S., Polevoy D.V. Povyshenie tochnosti neirosetevykh metodov verifikatsii lits za schet prostranstvenno-vzveshennoi normalizatsii yarkosti izobrazheniya [Improving the Accuracy of Neural Network Methods of Verification of Persons by SpatialWeighted Normalization of Brightness Image] Informatsionnye tekhnologii i vychislitel’nye sistemy [Journal Of Information Technologies And Computing Systems]. 2019b. V. 2019 (4). P. 12–20 (in Russian). https://doi.org/10.14357/20718632190402
  • Ingacheva A.S., Chukalina M.V. Polychromatic CT Data Improvement with One-Parameter Power Correction. Mathematical Problems in Engineering. 2019. V. 2019. P. 1405365. https://doi.org/10.1155/2019/1405365
  • Iwamura M., Niwa R., Kise K., Uchida S., Omachi S. Rectifying perspective distortion into affine distortion using variants and invariants. Proc. of the Second International Workshop on Camera-Based Document Analysis and Recognition. 2007. P. 138–145.
  • Iyatomi H., Celebi M.E., Schaefer G., Tanaka M. Automated color normalization for dermoscopy images. 2010 IEEE International Conference on Image Processing. IEEE. 2010. P. 4357–4360.
  • Jaccard P. Distribution de la flore alpine dans le bassin des Dranses et dans quelques régions voisines. Bull Soc Vaudoise Sci Nat. 1901. V. 37. P. 241–272.
  • Jesorsky O., Kirchberg K.J., Frischholz R.W. Robust face detection using the hausdorff distance. International Conference on Audio-and Video-Based Biometric Person Authentication. Springer. 2001. P. 90–95.
  • Kadir T., Zisserman A., Brady M. An affine invariantsalient region detector. European conference on computer vision. Springer. 2004. P. 228–241.
  • Karaimer H.C., Brown M.S. A software platform for manipulating the camera imaging pipeline. European Conference on Computer Vision. 2016. P. 429–444. https://doi.org/10.1007/978-3-319-46448-0_26
  • Karaimer H.C., Brown M.S. Improving color reproduction accuracy on cameras. Proc. of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. P. 6440–6449.
  • Karnaukhov V.N., Kober V.I. A locally adaptive algorithm for shadow correction in color images. Applications of Digital Image Processing XL, International Society for Optics and Photonics, Nov. 2017. 2017. V. 10396. P. 10396–23. https://doi.org/10.1117/12.2272692
  • Karpenko S., Konovalenko I., Miller A., Miller B., Nikolaev D. Uav control on the basis of 3d landmark bearing-only observations. Sensors. 2015. V. 15 (12). P. 29802–29820. https://doi.org/10.3390/s151229768
  • Katamanov S.N. Avtomaticheskaya privyazka izobrazhenii geostatsionarnogo sputnika mtsat-1r. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa [Current Problems In Remote Sensing Of The Earth From Space]. 2007. V. 1 (4). P. 63–68 (in Russian).
  • Kholopov I.S. Algoritm korrektsii proektivnykh iskazhenii pri malovysotnoi s'emke [Projective distortion correction algorithm at low altitude photographing] Komp’yuternaya optika [Computer Optics]. 2017. V. 41 (2). P. 284–290 (in Russian).
  • Kim K., Lim T., Kim C., Park S., Park C., Keum C. Highprecision color uniformity based on 4D transformation for micro-LED. Proc. SPIE 11302, Light-Emitting Devices, Materials, and Applications XXIV. 2020. V. 11302. P. 113021U. https://doi.org/10.1117/12.2542728
  • Kober V.I., Karnaukhov V.N. Adaptivnaya korrektsiya neravnomernogo osveshcheniya na tsifrovykh mul’tispektral’nykh izobrazheniyakh [Adaptive correction of nonuniform illumination of multispectral digital images] Informatsionnye protsessy. 2016a. V. 19 (2). P. 152–161 (in Russian).
  • Kober V.I., Karnaukhov V.N. Vosstanovlenie mul’tispektral’nykh izobrazhenii, iskazhennykh prostranstvennoneodnorodnym dvizheniem kamery [Restoration of multispectral images degraded by non-uniform camera motion] Informatsionnye protsessy. 2015. V. 15 (2). P. 269–277 (in Russian).
  • Kober V.I., Karnaukhov V.N. Adaptive correction of nonuniform illumination of multispectral digital images. JCTE. 2016b. V. 61 (12). P. 1419–1425. https://doi.org/10.1134/S1064226916120123
  • Konovalenko I.A. Srednekvadratichnaya nevyazka koordinat kak kriterii tochnosti normalizatsii izobrazhenii pri opticheskom raspoznavanii dokumentov [RMS coordinate discrepancy as accuracy criterion of images normalization at optical document recognition] Informatsionnye protsessy. 2020a. V. 20 (3). P. 215–230 (in Russian).
  • Konovalenko I.A., Shemiakina J.A. Error values analysis for inaccurate projective transformation of a quadrangle. Journal of Physics: Conference Series. 2018. V. 1096 (1). P. 012038. https://doi.org/10.1088/1742-6596/1096/1/012038
  • Konovalenko I.A., Kokhan V.V., Nikolaev D.P. Optimal’naya affinnaya approksimatsiya proektivnogo preobrazovaniya izobrazhenii [Optimal affine approximation of image projective transformation] Sensornye sistemy [Sensory systems]. 2019. V. 33 (1). P. 7–14 (in Russian). https://doi.org/10.1134/S0235009219010062
  • Konovalenko I.A., Kokhan V.V., Nikolaev D.P. Optimal affine image normalization approach for optical character recognition. Computer Optics. 2021. V. 45 (1). P. 90–100. https://doi.org/10.18287/2412-6179-CO-759
  • Konovalenko I.A., Polevoy D.V., Nikolaev D.P. Maksimal’naya nevyazka napravlenii kak kriterii tochnosti proektivnoi normalizatsii izobrazheniya pri opticheskom raspoznavanii teksta [Maximal directions discrepancy as accuracy criterion of images projective normalization for optical text recognition] Sensornye sistemy [Sensory systems]. 2020b. V. 34 (2). P. 131–146 (in Russian). https://doi.org/10.31857/S0235009220020079
  • Konovalenko I.A., Shemyakina Y.A., Faradzhev I.A. Otsenka tochki skhoda otrezkov metodom maksimal’nogo pravdopodobiya [Calculation of a vanishing point by the Maximum likelihood estimation method] Vestnik YuUrGU MMP [Bulletin of the South Ural State University, Series: Mathematical Modelling, Programming and Computer Software]. 2020c. V. 13 (1). P. 107–117 (in Russian). https://doi.org/10.14529/mmp200108
  • Konovalenko I.A., Kokhan V.V., Nikolaev D.P. Maximal coordinate discrepancy as accuracy criterion of image projective normalization for optical recognition of documents. Bulletin of the South Ural State University, Series: Mathematical Modelling, Programming and Computer Software. 2020d. V. 13 (3). P. 43–58. https://doi.org/10.14529/mmp200304
  • Kordecki A. Practical testing of irradiance-independent camera color calibration. Proc. SPIE 11041. Eleventh International Conference on Machine Vision (ICMV 2018). 2019. V. 11041. P. 340–345.
  • Kozlov E.P., Egoshkin N.A., Eremeev V.V. Normalizatsiya kosmicheskikh izobrazhenii Zemli na osnove ikh sopostavleniya s elektronnymi kartami. Tsifrovaya obrabotka signalov. [Digital Signal Processing]. 2009. V. (3). P. 21–26 (in Russian).
  • Kunina I.A., Aliev M.A., Arlazarov N.V., Polevoy D.V. A method of fluorescent fibers detection on identity documents under ultraviolet light. Proc. SPIE 11433. Twelfth International Conference on Machine Vision (ICMV 2019). 2020. V. 11433. P. 1–8. https://doi.org/10.1117/12.2558080
  • Kunina I.A., Gladilin S.A., Nikolaev D.P. Blind radial distortion compensation in a single image using fast Hough transform. Computer Optics. 2016. V. 40 (3). P. 395–403. https://doi.org/10.18287/2412-6179-2016-40-3-395-403
  • Kutulakos K.N., Vallino J. Affine object representations for calibration-free augmented reality. Virtual Reality Annual International Symposium. Proc. of the IEEE 1996. IEEE. 1996. P. 25–36.
  • Legge G.E., Pelli D.G., Rubin G.S., Schleske M.M. Psychophysics of reading–I. Normal vision. Vision research. 1985. V. 25 (2). P. 239–252.
  • Limonova E., Bezmaternykh P.V., Nikolaev D., Arlazarov V. Slant Rectification in Russian Passport OCR System Using Fast Hough Transform. Ninth International Conference on Machine Vision (ICMV 2016). 2017. V. 10341. P. 103410P. https://doi.org/10.1117/12.2268725
  • Limonova E., Nikolaev D., Arlazarov V.V. Bipolar Morphological U-Net for Document Binarization. Thirteenth International Conference on Machine Vision (ICMV 2020). 2021. V. 11605. P. 116050P. https://doi.org/10.1117/12.2587174
  • Lorenz H., Döllner J. Real-time piecewise perspective projections. GRAPP. 2009. P. 147–155.
  • Lu S., Chen B.M., Ko C.C. Perspective rectification of document images using fuzzy set and morphological operations. Image and Vision Computing. 2005. V. 23 (5). P. 541–553.
  • Lyubchenko V.A., Putyatin E.P. Matematicheskie modeli razlozheniya proektivnykh preobrazovanii v zadachakh normalizatsii. Radioelektronika i informatika. 2002. V. 2 (19). P. 57–59 (in Russian).
  • MacAdam D.L. Projective Transformations of I. C. I. Color Specification. J. Opt. Soc. Am. 1937. V. 27 (8). P. 294–299. https://doi.org/10.1364/JOSA.27.000294
  • Merino-Gracia C., Mirmehdi M., Sigut J., González-Mora J.L. Fast perspective recovery of text in natural scenes. Image and Vision Computing. 2013. V. 31 (10). P. 714–724.
  • Mikolajczyk K., Schmid C. An affine invariant interest point detector. European conference on computer vision. Springer. 2002. P. 128–142.
  • Mikolajczyk K., Schmid C. Scale & affine invariant interest point detectors. International journal of computer vision. 2004. V. 60 (1). P. 63–86.
  • Morel J.M., Yu G. ASIFT: A new framework for fully affine invariant image comparison. SIAM journal on imaging sciences. 2009. V. 2 (2). P. 438–469.
  • Murygin K.V. Normalizatsiya izobrazheniya avtomobil’nogo nomera i segmentatsiya simvolov dlya posleduyushchego raspoznavaniya [Normalization of the Image of a Car Plate and Segmentation of Symbols for the Subsequent Recognition] Shtuchny jintelekt [Artificial Intelligence]. 2010. V. (3). P. 364–369 (in Russian).
  • Nikolaev D.P., Gladkov A., Chernov T., Bulatov K. Diamond Recognition Algorithm using Two-Channel X-ray Radiographic Separator. Seventh International Conference on Machine Vision (ICMV 2014). International Society for Optics and Photonics. 2015. V. 9445. P. 944507. https://doi.org/10.1117/12.2181204
  • Nikolaev D.P., Grigoryev A.S., Gladkov A.P. Programma avtomaticheskogo soglasovaniya chuvstvitel’nostei kamer stereopary. Patent RF. No. RU 2016617966. 2016.
  • Nikolaev P.P. Metod proektivno invariantnogo opisaniya ovalov s osevoi libo tsentral’noi simmetriei. Informatsionnye tekhnologii i vychislitel’nye sistemy [Journal Of Information Technologies And Computing Systems]. 2014. V. (2). P. 46–59 (in Russian).
  • Nikolaev P.P. Proektivno invariantnoe raspoznavanie sostavnykh ovalov. Informatsionnye tekhnologii i vychislitel’nye sistemy [Journal Of Information Technologies And Computing Systems]. 2010. V. (4). P. 3–15 (in Russian).
  • Nikolaev P.P. Raspoznavanie proektivno preobrazovannykh ploskikh figur. X. Metody poiska okteta invariantnykh tochek kontura ovala - itog vklyucheniya razvitoy teorii v skhemy ego opisaniya [Recognition of projectively transformed planar figures. X. Methods for finding an octet of invariant points of an oval contour – the result of introducing a developed theory into the schemes of oval description] Sensornye sistemy [Sensory systems]. 2017. V. 31 (3). P. 202–226 (in Russian).
  • Nikolaev P.P., Savchik A.V., Konovalenko I.A. Proektivno invariantnoe predstavlenie kompozitsii dvukh ovalov [A Projectively Invariant Representation of a Composition of Two Ovals] Informatsionnye protsessy. 2018. V. 18 (4). P. 304–321 (in Russian).
  • Nikolayev P.P. Proektivno invariantnoe opisanie ovalov s simmetriyami trekh rodov [A Projective Invariant Description of Ovals with Three Possible Symmetry Genera] Vestnik RFFI [Vestnik RFFI]. 2016. V. (4). P. 38–54 (in Russian). https://doi.org/10.22204/2410-4639-2016-092-04-38-54
  • Nikolaidis A. Affine transformation invariant image watermarking using moment normalization and radial symmetry transform. 18th IEEE International Conference on Image Processing. IEEE. 2011. P. 2729–2732.
  • Ohta T.I., Maenobu K., Sakai T. Obtaining surface orientation from texels under perspective projection. IJCAI. 1981. P. 746–751.
  • Orrite C., Herrero J.E. Shape matching of partially occluded curves invariant under projective transformation. Computer Vision and Image Understanding. 2004. V. 93 (1). P. 34–64.
  • Panfilova E.I., Shipitko O.S., Kunina I.A. Fast Hough Transform-Based Road Markings Detection For Autonomous Vehicle. Thirteenth International Conference on Machine Vision (ICMV 2020). 2021 V. 11605. P. 116052B. https://doi.org/10.1117/12.2587615
  • Pavić D., Schönefeld V., Kobbelt L. Interactive image completion with perspective correction. The Visual Computer. 2006. V. 22 (9–11). P. 671–681.
  • Polevoy D.V., Panfilova E.I., Ershov E.I., Nikolaev D.P. Color correction of the document owner’s photograph image during recognition on mobile device. Thirteenth International Conference on Machine Vision (ICMV 2020). 2021. V. 11605. P. 1160510. https://doi.org/10.1117/12.2587627
  • Povolotskiy M.A., Kuznetsova E.G., Utkin N.V., Nikolaev D.P. Segmentatsiya registratsionnykh nomerov avtomobilei s primeneniem algoritma dinamicheskoi transformatsii vremennoi osi [Segmentation of vehicle registration plates based on dynamic time warping] Sensornye sistemy [Sensory systems]. 2018. V. 32 (1). P. 50–59 (in Russian). https://doi.org/10.7868/S0235009218010080
  • Povolotskiy M.A., Tropin D.V. Dynamic Programming Approach to Template-based OCR. Proc. SPIE 11041. Eleventh International Conference on Machine Vision (ICMV 2018). 2019. V. 11041. P. 110411T. https://doi.org/10.1117/12.2522974
  • Povolotskiy M.A., Tropin D.V., Chernov T.S., Savel’ev B.I. Metod segmentatsii strukturirovannykh tekstovykh ob'ektov na izobrazhenii s pomoshch’yu dinamicheskogo programmirovaniya [Dynamic programming approach to textual structured objects segmentation in images] Informatsionnye tekhnologii i vychislitel’nye sistemy [Journal Of Information Technologies And Computing Systems]. 2019. V. 69 (3). P. 66–78 (in Russian). https://doi.org/10.14357/20718632190306
  • Pritula N., Nikolaev D.P., Sheshkus A., Pritula M., Nikolaev P.P. Comparison of two algorithms modifications of projective-invariant recognition of the plane boundaries with the one concavity. Seventh International Conference on Machine Vision (ICMV 2014). International Society for Optics and Photonics. ICMV 2014. 2015. V. 944508. P. 1–5. https://doi.org/10.1117/12.2181215
  • Prun V.E., Polevoy D.V., Postnikov V.V. Forward Rectification – Spatial Image Normalization for a Video from a Forward Facing Vehicle Camera. Ninth International Conference on Machine Vision (ICMV 2016). 2017. V. 10341. P. 103410W. https://doi.org/10.1117/12.2268605
  • Putyatin E.P., Prokopenko D.O., Pechenaya E.M. Voprosy normalizatsii izobrazhenii pri proektivnykh preobrazovaniyakh. Radioelektronika i informatika [Radioelectronics & Informatics]. 1998. V. 2 (3). P. 82–86 (in Russian).
  • Rezatofighi H., Tsoi N., Gwak J., Sadeghian A., Reid I., Savarese S. Generalized intersection over union: A metric and a loss for bounding box regression. Proc. of the IEEE Conference on Computer Vision and Pattern Recognition. 2019. P. 658–666.
  • Rodríguez-Piñeiro J., Comesaña-Alfaro P., PérezGonzález F., Malvido-García A. A new method for perspective correction of document images. Document Recognition and Retrieval XVIII. International Society for Optics and Photonics. 2011. V. 787410. P. 1–12.
  • Safari R., Narasimhamurthi N., Shridhar M., Ahmadi M. Document registration using projective geometry. IEEE transactions on image processing. 1997. V. 6 (9). P. 1337–1341.
  • Savchik A., Ershov E., Karpenko S. Color Cerberus. ISPA 2019. 2019. P. 355–359. https://doi.org/10.1109/ISPA.2019.8868425
  • Savchik A.V., Nikolaev P.P. Metod proektivnogo sopostavleniya dlya ovalov s dvumya otmechennymi tochkami. Informatsionnye tekhnologii i vychislitel’nye sistemy [Journal Of Information Technologies And Computing Systems]. 2018. V. (1). P. 60–67 (in Russian).
  • Savchik A.V., Nikolaev P.P. Teorema o peresechenii T-i Hpolyar [The Theorem of T- and H- Polars Intersections Count]. Informatsionnye protsessy. 2016. V. 16 (4). P. 430–443 (in Russian).
  • Sawhney H.S., Kumar R. True multi-image align mentand its application to mosaicing and lens distortion correction. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1999. V. 21 (3). P. 235–243.
  • Schmitzer B., Schnörr C. Globally optimal joint image segmentation and shape matching based on Wasserstein modes. Journal of Mathematical Imaging and Vision. 2015. V. 52 (3). P. 436–458.
  • Shemiakina J., Konovalenko I., Tropin D., Faradjev I. Fast projective image rectification for planar objects with Manhattan structure. Proc. SPIE 11433. Twelfth International Conference on Machine Vision (ICMV 2019). 2020. V. 11433. P. 114331N. https://doi.org/10.1117/12.2559630
  • Shemiakina J.A., Zhukovsky A.E., Faradjev I.A. Issledovanie algoritmov vychisleniya proektivnogo preobrazovaniya v zadache navedeniya na planarnyi ob'ekt po osobym tochkam [The research of the algorithms of a projective transformation calculation in the problem of planar object targeting by feature points] Iskusstvennyi intellekt i prinyatie reshenii [Artificial Intelligence And Decision Making]. 2017. V. 2017 (1). P. 43–49 (in Russian).
  • Shemiakina Yu.A. Ispol’zovanie tochek i pryamykh dlya vychisleniya proektivnogo preobrazovaniya po dvum izobrazheniyam ploskogo ob'ekta [The usage of points and lines for the calculation of projective transformation by two images of one plane object] Informatsionnye tekhnologii i vychislitel’nye sistemy [Journal Of Information Technologies And Computing Systems]. 2017. V. 2017 (3). P. 79–91 (in Russian).
  • Shepelev D.A., Bozhkova V.P., Ershov E.I., Nikolaev D.P. Modelirovanie drobovogo shuma tsvetnykh podvodnykh izobrazhenii [Simulating shot noise of color underwater images] Komp’yuternaya optika [Computer Optics]. 2020. V. 44 (4). P. 671–679 (in Russian). https://doi.org/10.18287/2412-6179-CO-754
  • Sheshkus A., Ingacheva A., Arlazarov V., Nikolaev D. HoughNet: neural network architecture for vanishing points detection. ICDAR 2019, IEEE. 2020. V. 8978201. P. 844–849. https://doi.org/10.1109/ICDAR.2019.00140
  • Shipitko O., Grigoryev A. Ground Vehicle Localization With Particle Filter Based On Simulated Road Marking Image. ECMS 2018. 2018. P. 341–347. https://doi.org/10.7148/2018-0341
  • Shipitko O., Kibalov V., Abramov M. Linear Features Observation Model for Autonomous Vehicle Localization. ICARCV 2020, Institute of Electrical and Electronics Engineers Inc. 2021. V. 9305434. P. 1360–1365. https://doi.org/10.1109/ICARCV50220.2020.9305434
  • Shipitko O.S., Abramov M.P., Lukoyanov A.S., Panfilova E.I., Kunina I.A., Grigoryev A.S. Edge detection based mobile robot indoor localization system. Proc. SPIE 11041. Eleventh International Conference on Machine Vision (ICMV 2018). 2019. V. 11041. P. 110412V. https://doi.org/10.1117/12.2522788
  • Sim D.G., Kwon O.K., Park R.H. Object matching algorithms using robust hausdorff distance measures. IEEE Transactions on image processing. 1999. V. 8 (3). P. 425–429.
  • Sinclair D., Blake A. Isoperimetric normalization of planar curves. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1994. V. 16 (8). P. 769–777.
  • Singh S.K., Naidu S.D, Srinivasan T.P., Krishnaa B.G, Srivastava P.K. Rational polynomial modelling for cartosat-1 data. The International Archives of the Photogrammetry. Remote Sensing and Spatial Information Sciences. 2008. V. 37. P. 885–888.
  • Skoryukina N., Arlazarov V.V., Nikolaev D.P. Fast method of ID documents location and type identification for mobile and server application. ICDAR 2019. 2020. P. 850–857. https://doi.org/10.1109/ICDAR.2019.00141
  • Skoryukina N., Chernov T., Bulatov K., Nikolaev D.P., Arlazarov V. Snapscreen: Tv-stream frame search with projectively distorted and noisy query. Ninth International Conference on Machine Vision (ICMV 2016). 2017. V. 103410Y. P. 1–5. Bellingham. https://doi.org/10.1117/12.2268735
  • Skoryukina N., Shemiakina J., Arlazarov V. L., Faradjev I. Document localization algorithms based on feature points and straight lines. Tenth International Conference on Machine Vision (ICMV 2017). International Society for Optics and Photonics. 2018. V. 106961H. P. 1–8. https://doi.org/10.1117/12.2311478
  • Smagina A., Bozhkova V.P., Gladilin S., Nikolaev D. Linear colour segmentation revisited. Proc. SPIE 11041. Eleventh International Conference on Machine Vision (ICMV 2018). 2019. V. 11041. P. 110410F. https://doi.org/10.1117/12.2523007
  • Smagina A., Ershov E., Grigoryev A. Multiple Light Source Dataset for Colour Research. Proc. SPIE 11433. Twelfth International Conference on Machine Vision (ICMV 2019). 2020. V. 11433. P. 114332C. https://doi.org/10.1117/12.2559491
  • Smith T., Guild J. The C.I.E. colorimetric standards and their use. Transactions of the Optical Society. 1931. V. 33 (3). P. 73–134. https://doi.org/10.1088/1475-4878/33/3/301
  • Stein G.P. Lens distortion calibration using point correspondences. Computer Vision and Pattern Recognition, 1997. Proc. of the IEEE Computer Society Conference. 1997. P. 602–608.
  • Su Z., Zeng W., Wang Y., Lu Z.L., Gu X. Shape classification using Wasserstein distance for brain morphometry analysis. International Conference on Information Processing in Medical Imaging. Springer. 2015. P. 411–423.
  • Szeliski R. Video mosaics for virtual environments. IEEE computer Graphics and Applications. 1996. P. 16 (2). P. 22–30.
  • Takezawa Y., Hasegawa M., Tabbone S. Camera-captured document image perspective distortion correction using vanishing point detection based on Radon transform. Pattern Recognition (ICPR). 2016 23rd International Conference on. IEEE. 2016. P. 3968–3974.
  • Titov V., Shepelev D., Nikolaev D. Opredelenie parametrov pogloscheniya i rasseyaniya na osnove bystrogo preobrazovaniya Khafa. Sbornik trudov 43-i mezhdistsiplinarnoi shkoly-konferentsii IPPI RAN “Informatsionnye tekhnologii i sistemy 2019' (ITiS 2019). [Proc. of the ITaS 2019]. 2020. P. 495–500 (in Russian).
  • Tong L., Zhang, Y. Correction of perspective text image based on gradient method. Information Networking and Automation (ICINA). International Conference on. IEEE. 2010. V. 2. P. 312–316.
  • Triputen’ V.V., Gorokhovatskii V.A. Algoritm parallel’noi normalizatsii affinnykh preobrazovanii dlya tsvetnykh izobrazhenii. Radioelektronika i informatika [Radioelectronics & Informatics]. 1997. V. (1). P. 97–98. (in Russian).
  • Trusov A., Limonova E. The analysis of projective transformation algorithms for image recognition on mobile devices. Proc. SPIE 11433. Twelfth International Conference on Machine Vision (ICMV 2019). Wolfgang Osten and Dmitry P. Nikolaev editors. 2020. V. 11433. P. 250–257.
  • Tsviatkou V.Yu. Geometricheskie modeli mnogorakursnykh izobrazhenii i proektivnaya kompensatsiya dvizheniya kamery [Geometric models of multi-angle images and projective compensation of camera motion] Doklady Belorusskogo gosudarstvennogo universiteta informatiki i radioelektroniki [Doklady BGUIR]. 2014. V. 86 (8). P. 41–47 (in Russian).
  • Vanichev A.Yu. Normalizatsiya siluetov ob'ektov v sistemakh tekhnicheskogo zreniya. Programmnye produkty i sistemy. [Software & Systems]. 2007. V. (3). P. 86–88 (in Russian).
  • Wallace G., Chen H., Li K. Color gamut matching for tiled display walls. EGVE '03: Proc. of the workshop on Virtual environments 2003. 2003. P. 293–302. https://doi.org/10.1145/769953.769988
  • Wolberg G. Digital Image Warping. IEEE Computer Society Press, LosAlamitos, CA, 1990. 318 p.
  • Xie Y., Tang G., Hoff W. Geometry-based populated chessboard recognition. Tenth International Conference on Machine Vision (ICMV 2017). International Society for Optics and Photonics. 2018. V. 1069603. P. 1–5.
  • Zeynalov R., Velizhev A., Konushin A. Vosstanovlenie formy stranitsy teksta dlya korrektsii geometricheskikh iskazhenii [Document images geometrical distortions correction using text lines shape extraction]. Proc. of the 19 International Conference GraphiCon-2009. 2009. P. 125–128 (in Russian).
  • Zhang W., Li X., Ma X. Perspective correction method for Chinese document images. Intelligent Information Technology Application Workshops. 2008. IITAW’08. International Symposium on. IEEE. 2008. P. 467–470.
  • Zhang Z., He L.W. Whiteboard scanning and image enhancement. Digital signal processing. 2007. V. 17 (2). P. 414–432.
  • Zhukovskiy A.E., Nikolaev D.P., Arlazarov V.V., Postnikov V.V., Polevoy D.V., Skoryukina N.S., Chernov T.S., Shemyakina Yu.A., Mukovozov A.A., Konovalenko I.A., Povolotskiy M.A. Segments graph-based approach for document capture in a smartphone video stream. ICDAR2017. IEEE Computer Society. 2018. V. 1. P. 337–342. https://doi.org/10.1109/ICDAR.2017.63
  • Zwicker M., Rasanen J., Botsch M., Dachsbacher C., Pauly M. Perspective accurate splatting. Proc. of the Graphics interface 2004. Canadian Human-Computer Communications Society. 2004. P. 247–254.