Principles of Organization of the Human Eye Retina and Their Use in Computer Vision Systems
- 9 Downloads
This paper provides a summary of principles of organization of the human eye retina. The following principles are considered: locality during interacting neurons, ring organization of receptive fields with on- and off-centers (center-surround organization), specialization of neuron layers, organization of feedbacks, adaptation to lighting and contrast levels, and data volume reduction in a video stream. It is shown that the perfect organization of the human retina being used as a prototype allows to significantly improve technical characteristics of computer vision systems. The results of this research were used in creating a family of intelligent video cameras and a number of systems based on them and also in constructing specialized neural networks for primary information processing directly on a sensor matrix.
Keywordsretina rod cell cone cell horizontal cell bipolar cell amacrine cell ganglion cell on-center off-center neural network video sensor control of parameters of reading information intelligent video camera multilayer matrix structure
Unable to display preview. Download preview PDF.
- 1.Harvey Richard Schiffman, Sensation and Perception: An Integrated Approach [Russian translation], Piter, St. Petersburg (2003).Google Scholar
- 2.V. P. Boyun, “Human visual analyzer as a prototype for designing a family of problem-oriented machine vision systems,” in: Proc. Intern. Sci. and Techn. Conf. “Artificial Intelligence. Intelligent Systems (AI-2000),” Vol. 1, Donetsk (2010), pp. 21–26.Google Scholar
- 6.D. Anderson, Cognitive Psychology [Russian translation], Piter, St. Petersburg (2002).Google Scholar
- 8.Yu. E. Shelepin, V. M. Bondarko, and M. V. Danilova, “Foveola construction and visual system pyramidal organization model,” Sensory Systems, Vol. 9, No. 1, 87–97 (1995).Google Scholar
- 10.V. P. Boyun, “Intelligent selective perception of visual information. Informational aspects,” Artificial Intelligence, No. 3, 16–24 (2011).Google Scholar
- 11.V. Boyun, “Intelligent selective perception of visual information in vision systems,” in: Proc. 6th IEEE Intern. Conf. on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Application. (IDAACS’2011) (Prague, Czech Republic, 15–17 September 2011), Vol. 1 (2011), pp. 412–416.Google Scholar
- 12.V. P. Boyun, Dynamic Information Theory. Fundamentals and Applications [in Russian], V. M. Glushkov Institute of Cybernetics (2001).Google Scholar
- 14.O. G. Rudenko and E. V. Bodyansky, Artificial Neural Networks [in Russian], SMITH Company, Kharkiv (2005).Google Scholar
- 15.R. Gonzalez and R. Woods, Digital Image Processing [Russian translation], Technosphere, Moscow (2005).Google Scholar
- 16.V. Boyun, “Bioinspired approaches to the selection and processing of video information,” in: Proc. IEEE Second Intern. Conf. on Data StreamMining & Processing (DSMP) (2018), pp. 498–502. https://doi.org/10.1109/DSMP.2018.8478541.
- 17.V. P. Boyun, Device for Determining the Location and Parameters of Image Objects, UA Patent No. 76597, BI No. 6 (2013).Google Scholar
- 18.V. P. Boyun, Sensor Device for Determination of Location and Center of Gravity of an Object, UA Patent No. 106292, BI No.12 (2014).Google Scholar
- 19.V. P. Boyun, Sensor Device for Determining the Location and Moments of Inertia of an Object in an Image, UA Patent No. 106301, BI No. 15 (2014).Google Scholar
- 20.V. P. Boyun, Sensor Matrix with Image Processing, UA Patent No. 109335, BI No. 6, (2015).Google Scholar