Optimization of the Number of Passes in the Problem of Logical Image Filtering

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A method for optimization of the passes’ number is considered, which makes it possible to reduce the image processing time when implementing various operations, for example, logical filtering and/or depth mapping. A feature of this method is the use of two passes in the forward and reverse directions. The presented pseudocodes allow understanding the essence of the proposed passages. Evaluation of the method performance, confirmed by the results of simulation modeling, showed a noticeable decrease in the temporal characteristics of processing an image with a size of 3×3.

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Sobre autores

Maxim Bobyr

Southwest State University

Autor responsável pela correspondência
Email: maxbobyr@gmail.com

Doctor of Technical Sciences, Professor of the Department of Computer Science

Rússia, Kursk

Sergey Emelyanov

Southwest State University

Email: fregat_mn@rambler.ru

Doctor of Technical Sciences, Professor of the Department of Unique Buildings and Structures

Rússia, Kursk

Natalya Milostnaya

Southwest State University

Email: nat_mil@mail.ru

Candidate of Technical Sciences, Leading Researcher of the Department of Computer Science

Rússia, Kursk

Bibliografia

  1. Gurevich Yu.E. Robototehnicheskie ustrojstva [Robotic devices]. Staryj Oskol: Izdatel`stvo «Tonkie naukoemkie tehnologii» [Publishing House "Thin science-intensive technologies"], 2022. P. 328.
  2. Kolosov O.S., Esjutkin A.A., Prokof'ev N.A., Vershinin D.V., Balarev D.A. Avtomatizacija proizvodstva [Automation of Manufacturing]. Moskva: Yurayt [Moscow: Yurayt], 2018. P 291.
  3. Noskov V.P., Rubtsov V.I., Rubtsov I.V. Matematicheskie modeli dvizhenija i sistemy tehnicheskogo zrenija mo-bil'nyh robototehnicheskih kompleksov [Mathematical models of motion and vision systems of mobile robotic complexes]. Moskva: MGTU [Moscow: MGTU], 2015. 96p.
  4. Bobyr M., Arkhipov A., Emelyanov S., Milostnaya N. A method for creating a depth map based on a three-level fuzzy model // Engineering Applications of Artificial Intelligence. 2023. № 117. P. 105629.
  5. Yamashita H., Kobayashi E. Mechanism and design of a novel 8K ultra-high-definition video microscope for microsurgery. Heliyon. 2021. № 7(2). Р. 06244.
  6. Alam S. A. et al. Winograd convolution for deep neural networks: Efficient point selection //ACM Transactions on Embedded Computing Systems. 2022. Т.21. № 6. Р. 1-28.
  7. Arkhipov P.O., Trofimenkov A.K., Tsukanov M.V., Nosova N.Yu. Issledovanie metodov detektirovanija kljuchevyh tochek pri sozdanii panoramnyh izobrazhenij [Investigation of methods for detecting key points when creating panoramic images] // Sistemy i sredstva informatiki [Computer science systems and tools]. 2022. № 32(2). Р. 92-104.
  8. Maneckshaw B., Mahapatra G.S. Novel fuzzy matrix swap algorithm for fuzzy directed graph on image processing // Expert Systems with Applications. 2022. № 193. Р.116291.
  9. Zhang Z., Li Y., Yan X., Ouyang Z. A low-complexity AMP detection algorithm with deep neural network for massive MIMO systems // Digital Communications and Networks. 2022. November. Р. 11.
  10. Korchazhkina O.M. Optimizacija poiska pri reshenii perebornyh zadach v uglublennom kurse informatiki na urovne osnovnogo obshhego obrazovanija [Search optimization when solving iterative problems in an advanced computer science course at the level of basic general education] // Sistemy i sredstva informatiki [Computer science systems and tools]. 2022. № 32(4). Р. 145-156.
  11. Apanovich M.S., Lyapin A.P., Shadrin K.V. Primenenie metodov kompyuternoy algebry dlya vychisleniya resheniya zadachi Koshi dlya dvumernogo raznostnogo uravneniya v tochke [Application of computer algebra methods to calculate the solution of the Cauchy problem for a two-dimensional difference equation at a point] // Programmirovanie [Programming]. 2021. № 1. Р. 5-10.
  12. Robocraft // Electronic resource. URL: https://robocraft.ru/computervision/427 (accessed 25.01.2023).
  13. Habr // Electronic resource. URL: https://habr.com/ru/post/477718/ (accessed 25.01.2023)
  14. Bobyr M.V. Metod nelineynogo obucheniya neyronechetkoy sistemy vyvoda [The method of non-linear learning the neuro-fuzzy inference system] // Iskusstvenniy intellekt i prinyatie resheniy [Artificial intelligence and decision making]. 2018. № 1. P. 67-75.
  15. Nguyen T., Hefenbrock D., Oberg J., Kastner R., Baden S. A software-based dynamic-warp scheduling approach for load-balancing the Viola–Jones face detection algorithm on GPUs // Journal of Parallel and Distributed Computing. 2013. № 73(5). Р. 677–685.

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2. Fig. 1. Initial a) and preparatory b) arrays

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3. Fig. 2. Preparatory a) and final b) arrays

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4. Fig. 3. Programme interface for processing an image with a 3x3 window, where w = h = 10, n = 20000, n is the number of repetitions for calculating the total value, MapRand [y, x] = Random (0, 1)

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5. Fig. 4. Graphs of the time spent to perform all iterations during the experiment: a - the first experiment; b - the second experiment. Upper line - direct convolution method with a single filter

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6. Fig. 5. Programme interface for processing an image with a 3x3 window, with w = h = 10, n = 20000, MapRand [y, x] = Random (0, 255)

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7. Fig. 6. Graph of the time taken to perform all iterations in the third experiment. Upper line - direct convolution method with a single filter

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