THE VECTOR ROTOR OF SECOND ORDER AS MEANS OF IMPRUVEMENT OF TOOLS FOR AUTOMATION OF IMAGE PREPROCESSING

Аlexander Trunov

Анотація


The vector rotor of second order, which provide processing of image was introduced and considered. The feature of vector rotor of second order as vector product and step vector were investigated. It was received the algebraical expressions for determination of step vector as under direct action of rotor. The obtained results of modelling and investigation of values coefficients of compression and losses for different type of image, gradations and vector rotor fist and second order. It is shown the examples of applications of vector rotor to preprocessing of images in sensors for hyperspectral analysis

Ключові слова


automation; preprocessing; vector rotor second order; controlling rules; operation under rotor vector; expression of step vector; coefficient of compression and losses; hyperspectral sensor

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Petrov E. Koordynatsyonnoe upravlenye (menedzhment) protsessamy realyzatsyy reshenyi: Problems of Information Technology, Jornal, Vol. #02(016). 2014: 6-11.

Khodakov V, Sokolova N, Kyryichuk D. O razvyty osnov teoryy koordynatsyy slozhnykh system: Problems of Information Technology, Jornal, Vol. #02(016). 2014: 12-22.

Fisun M, Horshenova K. Modeliuvannia dynamichnykh protsesiv vitrovoi elektrychnoi stantsii u seredovyshchi gpss: Problems of Information Technology, Jornal, Vol. #01(017). 2015: 145-149.

Trunov A. Realization of Paradigm of Prescribed Control of Nonlinaer as the Maximization Adequacy Problem: Eastern-European Journal Enterprise Technologyies, №4(82). 2016: 50-58.

Khodakov V, Vezumskiy A. Kharakternye osobennosti odnogo klassa sotsial'noekonomicheskikh system: Problems of Information Technology, Jornal, №2(014). 2013: 10-14.

Trunov A. Recurrence approximation in problems of modeling and design: Monografy – Mykolayiv: Petro Mohyla BSSU. 2011: 272.

Kondratenko YP, Sidenko Ie. Decision-Making Based on Fuzzy Estimation of Quality Level for Cargo Delivery. In book: Recent Developments and New Directions in Soft Computing. Studies in Fuzziness and Soft Computing 317, Zadeh, L.A. et al. (Eds), Springer International Publishing Switzerland. 2014: 331-344, DOI: 10.1007/978-3-319-06323-2_21.

Kondratenko Yuriy P, Gerasin Oleksandr S, Topalov Andriy M. Modern Sensing Systems of Intelligent Robots Based on Multi-Component Slip Displacement Sensors, (IDAACS’2015), 2426 September, Warsaw, Poland, vol. 2. 2015: 902-907.

Trunov OM, Belikov OE. Modeling of interaction EMW with biologics objects in during phototherapy: Sience and methodology journal, – Т.107. Vol. 94. Ecology. – Mykolayiv: Publisher of МSHU named after. Petro Mohyla. 2009: 23-27.

Trunov AN. Peculiarities of the Interaction of Electromagnetic Waves with Bio Tissue and Tool for Early Diagnosis: Prevention and Treatment, in Proceedings are available in IEEE Xplore Digital Library, IEEE 36th International Conference on Electronics and Nanotechnology (ELNANO) (April), Kyiv, Ukraine. 2016: 169-174.

Beyerer Jürgen, Puente León, Fernando & Frese, Christian. Machine Vision - Automated Visual Inspection: Theory, Practice and Applications. Berlin:Springer. doi:10.1007/978-3-662-477946. ISBN 978-3-662-47793-9. Retrieved 10-11; 2016.

Graves, Mark & Bruce G. Batchelor Machine Vision for the Inspection of Natural Products. Springer. p. 5. ISBN 978-1-85233-525-0. Retrieved 2010-11-02. Springer Science & Business Media, 20 nov. 2003.

Holton W, Conard. "By Any Other Name".Vision Systems Design. 15 (10). ISSN 1089-3709 Retrieved 2013-03-05:http://www.vision-systems.com/articles/print/volume-15/issue10/Departments/Inside_Vision/by-any-other-name.html.

Owen-Hill Alex. "Robot Vision vs Computer Vision: What's the Difference?": http://www.roboticstomorrow.com/article/2016/07/robot-vision-vs-computer-vision-whats-thedifference/8484/

William H Smith. Digital array scanned interferometer US 4976542 A, published 11 December: 1990.

Michael W, Kudenov and Eustace L. “Dereniak compact snapshot real-time imaging spectrometer”, in Proceedings of SPIE, Vol. 8186 81860W-2, Downloaded from SPIE Digital Library on 06 February to 150.135.50.130. Terms of Use: http://spiedl.org/terms: 2012.

Chein-I Chang. Hyperspectral Imaging: Techniques for Spectral Detection and Classification. Springer Science & Business Media. ISBN 978-0-306-47483-5. 31 July; 2003.

Hans Grahn, Paul Geladi. Techniques and Applications of Hyperspectral Image Analysis. John Wiley & Sons. ISBN 978-0-470-01087-7.27 September; 2007.

Lu G; Fei B. "Medical Hyperspectral Imaging: a review" (Online full text article available). Journal of Biomedical Optics. 19 (1): 10901. Bibcode:2014JBO....19a0901L. doi:10.1117/1.JBO.19.1.010901. PMC 3895860. PMID 24441941, January; 2014

"Nikon MicroscopyU. – Confocal Microscopy – Spectral Imaging". http://www.bodkindesign.com/wp-content/uploads/2012/09/Hyperspectral-1011.pdf "OSA – Basic slit spectroscope reveals three-dimensional scenes through diagonal slices of hyperspectral cubes". Schurmer, J.H., Air Force Research Laboratories Technology Horizons, Dec.; 2003.

Ellis J. Searching for oil seeps and oil-impacted soil with hyperspectral imagery, Earth Observation Magazine, Jan.; 2001.

"SPIE – Journal of Biomedical Optics – Medical hyperspectral imaging: a review",http://biomedicaloptics.spiedigitallibrary.org/article.aspx?articleid=1816617#Hardwarea ndSystems.

Dacal-Nieto, Angel; et al. Common scab detection on potatoes using an infrared hyperspectral imaging system. 2011.- pp. 303–312. ISBN 978-3-642-24087-4.

Polycarpou M, Trunov A. “Automated Fault Diagnosis in Nonlinear Multivariable Systems Using a Learning Methodology”, IEEE Transactions on Neural Networks, vol. 11, no. 1, January. 2000: 91-101.

Polycarpou M, Trunov A. “Learning Approach to Nonlinear Fault Diagnosis: Detectability Analysis”, IEEE Transactions on Automatic Control, vol. 45, no. 4, April. 2000: 806-812.

Fiesler T, Duong, Trunov A. “Design of neural network-based microchip for color segmentation”, IEEE Transactions on Intelligent Optical Systems. Proceedings of SPIE 4055. 2000: 228-238.

Trunov A. Methodology of evaluation alternatives on the basis several etalons. Naukovi pratsi: Naukovo-metodychnyi zhurnal. – Mykolaiv: Vydavnytstvo ChDU im. Petra Mohyly, Vyp. 225. T. 237. Kompiuterni tekhnolohii. 2014: 99-104.

Vatolyn D. Metody szhatyya dannykh. M.: «Dyaloh-MYFY». 2003: 338-361.

Ryaben'kyy V. Umen'shenye ob’ema potoka vydeodannykh metodom vydelenyya dynamycheskoy sostavlyayushchey Vestnyk Khersonskoho natsyonal'noho tekhnycheskoho unyversyteta. # 2 (38). 2010: 314-318.

Ryaben'kyy V. Model' kompleksnoy ob’ektyvnoy otsenky kachestva vydeoyzobrazhenyya v Simulink. Problemy ynformatsyonnykh tekhnolohyy. # 2 (006). 2009: 116-121.

Trounov A N. Mathematical aspects of image recognition. Proc. Of International technology 90, Szezecin, Poland. 1990: 479-493.

Trunov OM, Volkova SO. Analiz metodiv i zasobiv pidvyshhennya yakosti ta nadijnosti system medychnoyi diahnostyky: Naukovyj zhurnal. – №2. Matematychni mashyny i systemy. – Kyyiv: Instytut problem matematychnyx mashyn i system NAN Ukrayiny. 2008:158-164.

Trunov O M. Teoretychni zasady proektuvannya intelektualizovanyx videosensoriv dlya ASUTP : Naukovo-metodychnyj zhurnal. –Mykolayiv: Vyd-vo MDHU im. Petra Mohyly,– Vyp.179. T.191. Komp’yuterni texnolohiyi. 2011: 124-128.

Trunov AN. Issledovanie invariantnyh svojstv video ob"ektov v ASUTP [Elektronnij resurs] :Biomedicinskaja inzhenerija i jelektronika. – # 1: 2012.

Trunov A. Recurrent approximation as the tool for expansion of functions and modes of operation of neural network, Eastern-European Journal Enterprise Technologyies, 5/4 (83). 2016: 41-48.


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