EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 401 (2023) E3S Web of Conf., 401 (2023) 04020 References
Open Access
Issue
E3S Web of Conf.
Volume 401, 2023
V International Scientific Conference “Construction Mechanics, Hydraulics and Water Resources Engineering” (CONMECHYDRO - 2023)
Article Number 04020
Number of page(s) 11
Section Mechanization, Electrification of Agriculture and Renewable Energy Sources
DOI https://doi.org/10.1051/e3sconf/202340104020
Published online 11 July 2023
  1. De Solan, B., Lesergent, A.D, Gouache, D. and Baret, F. Current use and potential of satellite imagery for crop production management. (2012). [Google Scholar]
  2. Hoffmann, Michael & Butenko, Yaryna & Traore, Seydou. (2018). Evaluation of Satellite Imagery to Increase Crop Yield in Irrigated Agriculture. Agris on-line Papers in Economics and Informatics. 10. 45-55. 10.7160/aol.2018.100304. [CrossRef] [Google Scholar]
  3. Zhou, Chao & Yang, Xinting & Zhang, Baihai & Lin, Kai & Xu, Daming & Guo, Qiang & Sun, Chuanheng. (2017). An adaptive image enhancement method for a recirculating aquaculture system. Scientific Reports. 7. 10.1038/s41598-017-06538-9. [Google Scholar]
  4. Jain Anil K. Fundamentals of digital image processing (Prentice Hall, Pearson Education, 1989). [Google Scholar]
  5. Rafael C.Gonzales, Richard E. Woods, Digital Image Processing (Second Edition, 2002). [Google Scholar]
  6. Gonzales R.C., Woods R.E. Digital image processing. - Boston, MA Addison-Wesley, 2001. - 823 p. [Google Scholar]
  7. W. X. Kang, Q. Q. Yang, and R. P. Liang, “The comparative researchon image segmentation algorithms,” in Proc. First InternationalWorkshop on Education Technology and Computer Science, 2009.ETCS’09. pp. 703-707, 2009. (PDF) A Survey: Image Segmentation Techniques. [Google Scholar]
  8. Muhammad Waseem Khan, “A Survey: Image Segmentation Techniques,” International Journal of Future Computer and Communication vol. 3, no. 2, pp. 89-93, 2014. [Google Scholar]
  9. D. Ping Tian et al., “A review on image feature extraction and representation tech-niques,” International Journal of Multimedia and Ubiquitous Engineering, vol. 8, no. 4, pp. 385–396, (2013). [Google Scholar]
  10. Fazilov, S., Mamatov, N., Samijonov, A., & Abdullaev, S. Reducing the dimensionality of feature space in pattern recognition tasks. Journal of Physics: Conference Series, 1441(1), 012139. (2020). [CrossRef] [Google Scholar]
  11. Mamatov, N., Samijonov, A., & Niyozmatova, N. Determination of non-informative features based on the analysis of their relationships. Journal of Physics: Conference Series, 1441(1), 012149. (2020). [CrossRef] [Google Scholar]
  12. Niyozmatova, N. A., Mamatov, N., Samijonov, A., Mamadalieva, N., & Abdullayeva, B. M. (2020). Unconditional discrete optimization of linear-fractional function “-1”-order. IOP Conference Series: Materials Science and Engineering, 862(4), 042028. [CrossRef] [Google Scholar]
  13. Samijonov, A., Mamatov, N., Niyozmatova, N. A., Yuldoshev, Y., & Asraev, M. (2020). Gradient method for determining non-informative features on the basis of a homogeneous criterion with a positive degree. IOP Conference Series: Materials Science and Engineering, 919(4). [Google Scholar]
  14. Niyozmatova, N. A., Mamatov, N., Samijonov, A., Rahmonov, E., & Juraev, S. Method for selecting informative and non-informative features. IOP Conference Series: Materials Science and Engineering, 919(4). (2020). [Google Scholar]
  15. Mamatov, N., Niyozmatova, N. A., Samijonov, A., Juraev, S., & Abdullayeva, B. The choice of informative features based on heterogeneous functionals. IOP Conference Series: Materials Science and Engineering, 919(4). (2020). [Google Scholar]
  16. Fazilov, S., & Mamatov, N. Formation an informative description of recognizable objects. Journal of Physics: Conference Series, 1210(1). (2019). [Google Scholar]
  17. Mamatov, N., Samijonov, A., & Yuldashev, Z. Selection of features based on relationships. Journal of Physics: Conference Series, 1260(10), 102008. (2019). [CrossRef] [Google Scholar]
  18. Shavkat, F., Narzillo, M., & Abdurashid, S. Selection of significant features of objects in the classification data processing. International Journal of Recent Technology and Engineering, 8(2 Special Issue 11), 3790–3794. (2019). [Google Scholar]
  19. Mamatov, N., Samijonov, A., Yuldashev, Z., & Niyozmatova, N. Discrete Optimization of Linear Fractional Functionals. 2019 15th International Asian School-Seminar Optimization Problems of Complex Systems, OPCS 2019, 96–99. (2019). [Google Scholar]
  20. Shavkat, F., Narzillo, M., & Nilufar, N. Developing methods and algorithms for forming of informative features’ space on the base K-types uniform criteria. International Journal of Recent Technology and Engineering, 8(2 Special Issue 11), 3784–3786. (2019). [Google Scholar]
  21. Mamatov, N.S., Samijonov, A.N., Yuldoshev, Y., Khusan, R. Selection the Informative Features on the Basis of Interrelationship of Features. In: Pawar, P., Ronge, B., Balasubramaniam, R., Vibhute, A., Apte, S. (eds) Techno-Societal 2018. Springer, Cham. (2020). [Google Scholar]
  22. Mr. Sachin Sonawane, “A Literature Review on Image Processing and Classification Techniques for Agriculture Produce and Modeling of Quality Assessment system for Soybean industry Sample”. International Journal of Innovative Research in Electronics and Communications (IJIREC), 6(2), pp.8-16. (2019). [Google Scholar]
  23. Starovoitov, Valery & Golub, Yuliya. Quality assessments for digital image analysis. Artificial intelligence. 376-386. (2008). [Google Scholar]
  24. Golub, Yuliya & Starovoitov, Valery. Investigation of local estimates of the contrast of digital images in the absence of a standard. System Analysis and Applied Informatics. (2019). [Google Scholar]
  25. Altukhov A.I., Shabakov E.I., Korshunov D.S. A method for increasing the contrast of images in conditions of shooting the earth from space. Scientific and technical bulletin of information technologies, mechanics and optics. (2018). [Google Scholar]
  26. Popov G.A., Korneev M. Method of adaptive regulation of the digital image contrast level when preparing it for recognition. Modern Science: Actual Problems of Theory and Practice (1), pp.48–53. (2018). [Google Scholar]
  27. Anandha Jothi, R., Palanisamy, V.: Performance enhancement of minutiae extraction using frequency and spatial domain filters. Int. J. Pure Appl. Math. 118(7), 647–654 (2018) [Google Scholar]
  28. Yu.I. Monich, V.V. Starovoitov. Quality assessments for the analysis of digital Images. State Scientific Institution “Joint Institute for Informatics Problems of the National Academy of Sciences of Belarus” (UIPI NAS of Belarus), Minsk, Belarus [Google Scholar]
  29. Wang, Xiuyuan & Yang, Chenghai & Zhang, Jian & Song, Huaibo. Image dehazing based on dark channel prior and brightness enhancement for agricultural monitoring. International Journal of Agricultural and Biological Engineering. 11. 170-176. (2018). [CrossRef] [Google Scholar]
  30. Seyed Mohammad Entezarmahdi and Mehran Yazdi,” Stationary Image Resolution Enhancement on the Basis of Contourlet and Wavelet Transforms by means of the Artificial Neural Network”, 2010 IEEE. [Google Scholar]
  31. Liu, Chengwei & Sui, Xiubao & Hongyu, Kuang & Gu, & Chen, Guanhua. Adaptive Contrast Enhancement for Infrared Images Based on the Neighborhood Conditional Histogram. Remote Sensing. 11. 1381. (2019). [CrossRef] [Google Scholar]
  32. Widyantara, I Made. Image Enhancement Using Morphological Contrast Enhancement for Video Based Image Analysis. (2016). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.