Abstract
The efficiency of object recognition algorithms, computer vision systems and image analysis directly depend on the quality of image preprocessing. Gradation correction is one of the stages of this preprocessing. The paper discusses three the most common models of gradation image correction, which are able to work on any brightness scale. Also in this paper, criteria for the quality of gradation correction are formulated. Experiments have been carried out that confirm the operability and computational efficiency of the considered models.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Gonzalez R, Woods R (2018) Digital image processing, 4th edn. Pearson, New York
Forsyth D, Ponce J (2015) Computer vision: a modern approach, 2nd edn. Pearson, London
Corke P (2017) Robotics, vision and control. 2nd edn. Springer
Ruban I, Smelyakov K, Martovytskyi V, Pribylnov D, Bolohova N (2018) Method of neural network recognition of ground-based air objects. In: IEEE 9th international conference on dependable systems, services and technologies (DESSERT). IEEE, pp 589–592
Ageyev D et al (2018) Provision security in SDN/NFV. In: 2018 14th international conference on advanced trends in radioelecrtronics, telecommunications and computer engineering (TCSET). IEEE, pp 506–509. https://doi.org/10.1109/tcset.2018.8336252
Ruban I, Churyumov G, Tokarev V, Tkachov V (2017) Provision of survivability of reconfigurable mobile system on exposure to high-power electromagnetic radiation. In: Selected papers of the XVII international scientific and practical conference on information technologies and security (ITS 2017). CEUR workshop processing, pp 105–111
Kirichenko L, Radivilova T, Bulakh V (2020) Binary classification of fractal time series by machine learning methods. In: Lytvynenko V, Babichev S, Wójcik W, Vynokurova O, Vyshemyrskaya S, Radetskaya S (eds) Lecture notes in computational intelligence and decision making. ISDMCI 2019. Advances in intelligent systems and computing, vol 1020. Springer, Cham
Smelyakov K, Yeremenko D, Sakhon A, Polezhai V, Chupryna (2018) A Braille character recognition based on neural networks. In: IEEE second international conference on data stream mining & processing (DSMP). IEEE, pp 509–513
Smelyakov K, Ruban I, Sandrkin D, Martovytskyi V, Romanenkov Y (2018) Search by image. New search engine service model. In: 5th international scientific-practical conference problems of infocommunications. Science and technology (PIC S&T), pp 181–186
Ageyev DV, Salah MT (2016) Parametric synthesis of overlay networks with self-similar traffic. Telecommunications and Radio Engineering (English translation of Elektrosvyaz and Radiotekhnika) 75(14):1231–1241
Ionescu R, Popescu M (2016) Knowledge transfer between computer vision and text mining. Springer
Sonka M, Hlavac V, Boyle R (2014) Image processing, analysis, and machine vision. 4th edn. Cengage Learning
Ageyev D et al (2019) Infocommunication networks design with self-similar traffic. In: 2019 IEEE 15th international conference on the experience of designing and application of CAD systems (CADSM). IEEE, pp 24–27. https://doi.org/10.1109/cadsm.2019.8779314
Filatov V, Semenets V (2018) Methods for synthesis of relational data model in information systems reengineering problems. In: Proceedings of the international scientific-practical conference problems of infocommunications. Science and technology (PIC S&T), pp 247–251
Mukhin V, Romanenkov Y, Bilokin J, Rohovyi A, Kharazii A, Kosenko V, Kosenko N, Su Ju (2017) The method of variant synthesis of information and communication network structures on the basis of the graph and set-theoretical models. Int J Intell Syst Appl (IJISA) 9(11):42–51
Bielievtsov S, Ruban I, Smelyakov K, Sumtsov D (2018) Network technology for transmission of visual information. In: Selected papers of the XVIII international scientific and practical conference on information technologies and security (ITS 2018). CEUR workshop processing. Kyiv, pp 160–175
Lemeshko O, Arous K, Tariki N (2015) Effective solution for scalability and productivity improvement in fault-tolerant routing. In: Proceedings of second international IEEE conference problems of infocommunications. Science and technology (PICS&T-2015). Kharkiv, pp 76–78
Kryvinska N (2008) An analytical approach for the modeling of real-time services over IP network. Math Comput Simul 79(4):980–990. https://doi.org/10.1016/j.matcom.2008.02.016
Aristotle Roufanis photography, https://aristotle.photography/. Last accessed 10 Dec 2019
Pxhere free images, https://pxhere.com/en/photo/239908. Last accessed 21 Dec 2019
Pxhere free images, https://pxhere.com/en/photo/726866. Last accessed 21 Dec 2019
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Smelyakov, K., Chupryna, A., Hvozdiev, M., Sandrkin, D., Ruban, I., Voloshchuk, O. (2021). Unified Models of Gradation Image Correction. In: Radivilova, T., Ageyev, D., Kryvinska, N. (eds) Data-Centric Business and Applications. Lecture Notes on Data Engineering and Communications Technologies, vol 48. Springer, Cham. https://doi.org/10.1007/978-3-030-43070-2_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-43070-2_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-43069-6
Online ISBN: 978-3-030-43070-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)