Abstract
A significant expansion of the scope of computer vision, in particular in real-time systems, places very high demands on them in terms of productivity and efficiency of information processing, and in feedback systems, it also requires information lag in it. Such requirements are not ensured by traditional approaches. The way out of the situation may be to use as a prototype the principles of organization of the human visual system, which has a very high selectivity of perception of video information. The paper presents a generalized dynamic model of the organization of these principles. It is proposed to use them to organize the search for an object in a coarse image of a scene, to track an object and, if necessary, to carry out its classification or recognition at a more detailed level.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Anderson, D.: Cognitive Psychology, 5th edn. Piter, St. Petersburg, Russia (2002). (Russian translation)
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Comput. Vis. Image Underst. 110(3), 346–359 (2008). https://doi.org/10.1016/j.cviu.2007.09.014
Benoit, A., Caplier, A., Durette, B., Herault, J.: Using human visual system modeling for bio-inspired low level image processing. Comput. Vis. Image Underst. 114(7), 758–773 (2010). https://doi.org/10.1016/j.cviu.2010.01.011
Boyun, V.: Intelligent selective perception of visual information in vision systems. In: Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Application, IDAACS 2011, Czech Republic, Prague, vol. 1, pp. 412–416 (2011)
Boyun, V.: Directions of development of intelligent real time video systems. Appl. Theor. Comput. Technol. [S.l.] 2(3), 48–66 (2017)
Boyun, V.P., Voznenko, L.O., Malkush, I.F.: Principles of organization of the human eye retina and their use in computer vision systems. Cybern. Syst. Anal. 55(5), 701–713 (2019). https://doi.org/10.1007/s10559-019-00181-0
Boyun, V.: The dynamic theory of information. Fundamentals and applications. Institute of Cybernetics of NASU, Kyiv, Ukraine (2001)
Boyun, V.: A human visual analyzer as a prototype for construction of the set of dedicated systems of machine vision. In: Proceedings of the International Science and Technology Conference on "Artificial Intelligence", Intelligent Systems II-2010, vol. 1, pp. 21–26 (2010)
Boyun, V.: Intelligent selective perception of visual information: informational aspects. Artif. Intell. 3, 16–24 (2011). (in Ukrainian)
Boyun, V.: Device for determining the location and parameters of image objects, UA patent no. 76597, BI no. 6 (2013)
Boyun, V.: Sensor device for determination of location and center of gravity of an object, UA patent no. 106292, BI no. 12 (2014)
Boyun, V.: Sensor device for determining the location and moments of inertia of an object in an image, UA patent no. 106301, BI no. 15 (2014)
Boyun, V.: Sensor matrix with image processing, UA patent no. 109335, BI no. 6 (2015)
Burt, P.: Smart sensing within a pyramid vision machine. Proc. IEEE 76(8), 175–185 (1988). https://doi.org/10.1109/5.5971
Gollisch, T., Meister, M.: Eye smarter than scientists believed: neural computations in circuits of the retina. Neuron 65(2), 150–164 (2009). https://doi.org/10.1016/j.neuron.2009.12.009
Digital Image Processing. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-6113-4_9
Hubel, D.H.: Eye, Brain and Vision. Scienceific American, New York (1988)
Kolb, H.: How the retina works: much of the construction of an image takes place in the retina itself through the use of specialized neural circuits. Am. Sci. 91(1), 28–35 (2003). https://doi.org/10.1511/2003.1.28
Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: NIPS Proceedings. Advances in Neural Information Processing Systems, vol. 25 (2012). http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-network
Marr, D.: Computational Investigation into Human Representation and Processing of Visual Information. W.H. Freeman and Company, New York (1987)
Podvigin, N., Makarov, F., Shelepin, Y.: Elements of Structural and Functional Organization of Visual Oculomotor System. Nauka, Leningrad, USSR (1986). (in Russian)
Schiffmann, H.: Sensation and Perception: An Integrated Approach. Piter, St. Peterburg (2003). (Russian translation)
Shah, S., Levine, M.: Visual information processing in primate cone pathway - part i: a model, part ii: experiments. IEEE Trans. Syst. Man Cybern. Syst. Part b Cybern. 26(2), 259–289 (1996). https://doi.org/10.1109/3477.485837
Shelepin, Y., Bondarko, V., Danilova, M.: Foveola construction and visual system pyramidal organization model. Sens. Syst. 9(1), 87–97 (1995). (in Russian)
Shevelev, I.: Neurons of Visual Cortex. Adaptability and Dynamics of Receptive Fields. Nauka, Moscow (1984). (in Russian)
Siagian, C., Itti, L.: Rapid biologically-inspired scene classification using features shared with visual attention. IEEE Trans. Pattern Anal. Mach. Intell. 29(2), 300–312 (2007)
Supin, A.: Neuron Mechanisms of Visual Analysis. Nauka, Moscow, USSR (1974). (in Russian)
Tagare, H., Toyama, K., Wang, J.: A maximum-likelihood strategy for directing attention during visual search. IEEE Trans. Pattern Anal. Mach. Intell. 23, 490–500 (2001)
Yamasaki, H., Shibata, T.: A real-time image-feature-extraction and vector-generation VLSI employing arrayed-shift-register architecture. IEEE J. Solid-State Circ. 42(9), 2046–2053 (2007)
Acknowledgments
This work was carried out in the framework of fundamental competitive topics (VFK 200.15 and VFK 200.19), which were funded by the Presidium of the National Academy of Sciences (NAS) of Ukraine.
I express my sincere gratitude to the employees of the Department of Intelligent Real-Time Video Systems of the Institute of Cybernetics named after V.M. Glushkov NAS of Ukraine, which took part in the creation of tools, modeling and verification of theoretical provisions.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Boyun, V. (2020). The Principles of Organizing the Search for an Object in an Image, Tracking an Object and the Selection of Informative Features Based on the Visual Perception of a Person. In: Babichev, S., Peleshko, D., Vynokurova, O. (eds) Data Stream Mining & Processing. DSMP 2020. Communications in Computer and Information Science, vol 1158. Springer, Cham. https://doi.org/10.1007/978-3-030-61656-4_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-61656-4_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-61655-7
Online ISBN: 978-3-030-61656-4
eBook Packages: Computer ScienceComputer Science (R0)