Directions of Development of Intelligent Real Time Video Systems
Real time video systems play a significant role in many fields of science and technology. The range of their applications is constantly increasing together with requirements to them, especially it concerns to real time video systems with the feedbacks. Conventional fundamentals and principles of real-time video systems construction are extremely redundant and do not take into consideration the peculiarities of real time processing and tasks, therefore they do not meet the system requirements neither in technical plan nor in informational and methodical one. Therefore, the purpose of this research is to increase responsiveness, productivity and effectiveness of real time video systems with a feedback during the operation with the high-speed objects and dynamic processes. The human visual analyzer is considered as a prototype for the construction of intelligent real time video systems. Fundamental functions, structural and physical peculiarities of adaptation and processes taking place in a visual analyzer relating to the information processing, are considered. High selectivity of information perception and wide parallelism of information processing on the retinal neuron layers and on the higher brain levels are most important peculiarities of a visual analyzer for systems with the feedback. The paper considers two directions of development of intelligent real time video systems. First direction based on increasing intellectuality of video systems at the cost of development of new information and dynamic models for video information perception processes, principles of control and reading parameters of video information from the sensor, adapting them to the requirements of concrete task, and combining of input processes with data processing. Second direction is associated with the development of new architectures for parallel perception and level-based processing of information directly on a video sensor matrix. The principles of annular and linear structures on the neurons layers, of close-range interaction and specialization of layers, are used to simplify the neuron network.
2. Forsyth D., Ponce J. Computer vision. A modern approach. Williams. Moscow, Saint Petersburg, Russia, Kiev, Ukraine. 2004. 928 p. (in Russian)
3. Burt P. J. Smart Sensing within a Pyramid Vision Machine, IEEE, V.76, No. 8, 1988, pp. 175-185.
4. Prett W. Digital Image processing. Mir, Moscow, USSR, 1982. vol.2, 560 p. (in Russian)
5. Boyun V. Intelligent selective perception of visual information. Informational aspects. Artificial intellect. 2011. No. 3. pp.16-24. (in Ukrainian)
6. Schiffmann H.R. Sensation and perception. 5-th ed. Piter, SPb., Russia, 2003. 928 p. (in Russian)
7. Boyun V. A human visual analyzer as a prototype for construction of the set of dedicated systems of machine vision. Transactions of the International conference “Artificial intelligence. Intelligent systems. II-2010, 2010, vol. 1, pp. 21-26. (in Ukrainian)
8. Kirpichnikov A.P. Is the fundamental processor of our civilization a unique example of evolution in newest history? Digital signal processing. No. 3, 2009. (in Russian)
9. Kirpichnikov A.P. Is the fundamental processor of our civilization a unique example of evolution in newest history? Digital signal processing. No. 3, 2009. (in Russian)
10. Alexandrov V.V. An eye and visual perception. Optical journal, vol. 66, No. 9, 1999. pp. 54-62. (in Russian
11. Anderson D. Cognitive psychology. 5-th ed. Piter, SPb., Russia, 2002. 496 p. (in Russian)
12. Supin A.J. Neuron mechanisms of visual analysis. Nauka. Moscow, USSR. 1974. – 180 p. (in Russian)
13. Shevelev I.A. Neurons of visual cortex. Adaptability and dynamics of receptive fields. Nauka, Moscow, USSR, 1984. – 220 p. (in Russian)
14. Hubel D. H. Eye, Brain and Vision. NewYork: Scienceific American, 1988.
15. Marr D. A Computational Investigation into Human Representation and Processing of Visual Information. W.H.Freeman and Company. New York. 1987.
16. Marr D. A Computational Investigation into Human Representation and Processing of Visual Information. W.H.Freeman and Company. New York. 1987.
17. Boyun V. Intelligent Selective Perception of Visual Information in Vision Systems. Proceedings of the 6-th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Application. (IDAACS”2011). Prague, Czech Republic, 2011. vol.1. p. 412-416.
18. Le Meur O., Le Callet P., Barba D., Thoreau D. A Coherent Computational Approach to Model Bottom-Up Visual Attention. IEEE Trans. On Pattern Analysis and Machine Intelligence. vol. 28, no. 5, May 2006.
19. 19. Tagare H.D., Toyama K., Wang J.G. A Maximum-Likelihood Strategy for Directing Attention during Visual Search, IEEE Transactions on pattern analysis and machine intelligence, vol.23, no.5, May 2001, pp. 490-500.
20. Shelepin Y., Bondurko V., Danilova M., Fovea construction and pyramid structure model of vision system, J.Sensor systems. No.1, 1995, pp. 87-97, (in Russian).
21. Boyun V. The dynamic theory of information. Fundamentals and applications. Institute of Cybernetics of NASU, Kyiv, Ukraine, 2001, 326 p. (in Russian)
22. Glushkov V. About cybernetics as a science, Cybernetics, thinking, life. Moscow, USSR, 1964, pp. 53-62 (in Russian)
23. Boyun V. Intelligent Real Time Videosystems of New Generation. Proc. of the XI Intern. Sc. and Tech. Conf. CSIT 2016. Lviv Politech. Publish. House, Ukraine, 2016, pp. 123-125
24. Shan S., Levine M.D. Visual Information Processing in Primate Cone Pathways - Part 1: A Model. Part 11: Experiments. IEEE Trans. On Systems. Man, and Cybernetics – Part B: Cybernetics, vol.26, no.2, Apr. 1996.
25. Huang C.H., Lin C.T. Bio-Inspired Computer Fovea Model Based on Hexagonal-Type Cellular Neural Network. IEEE Trans. Circuits Syst. I. Reg.Papers, vol.54, no.1, pp.35-46, Jan. 2007.
26. Ishii I., Yamamoto K., Kubozono M. Higher Order Autocorrelation Vision Chip. IEEE Trans. On Electron Devices. vol.53, no.8, Aug. 2006.
27. Zenkin G.М., Petrov A.P. Functional organization of visual process and Gestalt principle. Intelligent processes and modeling of them. Nauka, Moscow, USSR, 1987. (in Russian)
28. Bayev K.V. Problem solving of pattern generators and new concept of brain functions. Neurophysiology, Kitv, Ukraine, vol. 44, No. 5, 2012. pp. 469-488. (in Russian)
29. Gladilin S.A., Lebedev D.G. Dynamic model of neural network, which propagates output signal of ganglion cell. Biophysics, vol. 53, issue 1, 2008. (in Russian)
30. Podvigin N.F., Makarov F.N., Shelepin Y.E. Elements of structural and functional organization of visual oculomotor system. Nauka, Leningrad branch. Leningrad, USSR. 1986. (in Russian)
31. Svet V.D., Hazen А.М. On the forming of an image in inverted eye retina. Biophysics, 2009, vol. 54, issue 2, pp.274-286. (in Russian)
32. Hajjar A., T.Chen. A VLSI Architecture for Real-TimeEdge Linking. IEEE Trans. On Pattern Analysis and Machine Intelligence. vol. 21, no. 1, Jan.1999.
33. Cembrano G.L., Rodriguez-Vazquez A., Galan R.C., Jimenez-Garrido F., Espejo S., Domeniguez-Castro R. A 1000 FPS at 128x128 Vision Processor With 8-Bit Digitized I/O. IEEE Journal of Solid-State Circuits. vol.39, no.7, July 2004.
34. Mandolesi P.S., Julian P., Andreon A.G., A Scalable and Programmable Simplicial CNN Digital Pixel Processor Architecture. IEEE Trans. Circuits Syst.- I: Reg.Papers, vol.51, no.5, May 2004.
35. Nakada A., Shibata T., Konda M., MarimotoT., Ohmi T. A Fully Parallel Vector-Quantization Processor for Real-Time Motion-Picture Compression. IEEE Journal of Solid-State Circuits. vol.34, no.6, Jan.1999.
36. Yamasaki H., Shibata T. A Real-Time Image-Feature-Extraction and Vector-Generation VLSI Employing Arrayed-Shift-Register Architecture. IEEE Journal of Solid-State Circuits, vol.42, no.9, Sept.2007.
37. Barlow G.B. Neuron logic of matched filters. Optical journal, vol. 66, No. 9, 1999. (in Russian)
38. Erienne-Cummings R., Kalayjian Z.K., Cai D., A Programmable Focal-Plane MIMD Image Processor Chip. IEEE Journal of Solid-State Circuits. vol.36, no.1, Jan.2001.
39. Boyun V.P. Device for determination and parameters of an object in an image. Ukraine Patent for an invention, No. 76597, BI No. 6, 10.01.13. (in Ukrainian)
40. Boyun V.P. Sensor device for determination of location and center of gravity of an object. Ukraine Patent for an invention, No. 106292, BI No. 15, 11.08.14. (in Ukrainian)
41. Boyun V.P. Sensor device for determination of location and moments of inertia of an object in an image. Ukraine Patent for an invention, No. 106301, BI No. 15, 11.08.14. (in Ukrainian)
42. Boyun V.P. Sensor matrix with image processing. Ukraine Patent for an invention, No. 109335, BI No. 6, 10.08.15. (in Ukrainian)
43. Boyun V. Intelligent video computer systems and devices, Innovation technologies, No.2-3, 2003, pp.124-131 (in Russian)
44. Boyun V.P. Intelligent computer systems of perception and processing of physical information. The bulletin of AS of Ukraine. Kyiv, Ukraine. Akademperiodyka. 2015. No. 5. pp. 82-84. (in Ukrainian)
45. Boyun V., Lushchyk U., Malinovskyy L., Novytskyy V. Hemodynamic Lab “MACROMICROPOTOK” for Integrated Examination and Efficient Correction of the Human Vascular System. Distributed