THE POSSIBILITY OF PARALLELIZATION OF IMAGE PROCESSING USING OPENCV AND OPENMP

The relevance of the study is due to the need to solve problems related to image processing in various technical applications. Several approaches are considered: on the basis of the usual access to image pixels, when in fact all values of the array are bypassed in turn, access to pixels is carried out using arithmetic operations on pointers, pixels are located within a single continuous memory block in a sequential manner, and, on the basis of the proposed approach associated with parallelization of calculations, using multithreading. On the basis of empirical studies, the possibility of accelerating the calculations based on the proposed method several times was shown. A block binarization algorithm is considered when binary blocks form a complete binary image. Within this algorithm parallelization of calculations is carried out. When implementing the algorithm, the C++ language and OpenCV and OpenMP libraries were used. On the basis of empirical studies in tables and graphs, it is shown that due to parallelization, even at full load of the kernel, the image processing time was reduced by almost 2 times, which confirms the possibility of using the proposed algorithm.

1. Binarization of black and white images: state and prospects [Electronic resource] / A. Fedorov. Access Mode: http://itclaim.ru/Library/Books/ITS/wwwbook/ist4b/its4/fyodorov.htm

2. Yakovleva E. S., Makarov A. A. On the properties of the block algorithm binarization of digital images / E. S. Yakovleva, A. A Makarov // Computer tools in education. –– 2015. –– No. 4. –– S. 26–36

3. Dawson-Howe K. A Practical Introduction to Computer Vision with OpenCV. Wiley-IS&T Series in Imaging Science and Technology. –– 1 edition. –– Wiley, 2014.

4. Gonzales R., Woods R. Digital image processing. / R. Gonzales, R. Woods // M .: Technosphere, 2006 .-- 1072 p.

5. GRASS GIS [Electronic resource]: Home. Access mode: https://grass.osgeo.org/

6. A Digital Image Mapping System [Electronic resource]: A software and hardware solution for the various mapping tasks is being developed in a joint project of research institutes and Teragon Context AB. Access mode: http://www.isprs.org/proceedings/XXVII/congress/part2/380_XXVII-part2- sup.pdf

7. Juhasz Z. An analytical method for predicting the performance of parallel image processing operations // The Journal of supercomputing. – 1998. – Vol. 12. – P. 157–174.

8. On the properties of the block digital binarization algorithm [Electronic Resource] / Yakovleva E. S., Makarov A. A. Access mode: www.ipo.spb.ru/journal/content/1784/О%20properties%20block%20alg oritma% 20 binarization% 20 digital% 20 images ..pdf

9. Manisha Chate Object Detection and tracking in Video Sequences / Manisha Chate, S.Amudha,Vinaya Gohokar // ACEEE Int. J. on Signal & Image Processing, Vol. 03, No. 01, Jan 2012.

10. Anaswara S Mohan Video Image Processing for Moving Object Detection and Segmentation using Background Subtraction / Anaswara S Mohan, R.Resmi // IEEE International Conference on Computational Systems and Communications (ICCSC), Vol. 01, no. 01, pp.288-292, 17-18 Dec 2014.

11. . Weiming Hu. A Survey on Visual Surveillance of Object Motion and Behaviors / Weiming Hu, Tieniu Tan,Liang Wang, and Steve Maybank // IEEE Transactions on systems, man, and cyberneticsapplications and reviews, vol. 34, no. 3, pp. 334- 352, august 2004

12. Asim R. Aldhaheri. Detection and Classification of a Moving Object in a Video Stream / Asim R. Aldhaheri and Eran A. Edirisinghe // In Proc. of the Intl. Conf. on Advances in Computing and Information Technology-ACIT, 2014.

13. Singla M. Motion Detection Based on Frame Difference Method International / M. Singla // Journal of Information & Computation Technology. 2014. Vol. 4. No. 15. P. 1559–1565.

14. Zivkovic Z. Improved adaptive gaussian mixture model for background subtraction / Z. Zivkovic // IEEE Int. Conf. Pattern Recognition. 2004. Vol. 2. P. 28–31

15. Bouwmans T. Background Modeling using Mixture of Gaussians for Foreground Detection – A Survey / T. Bouwmans, F. El Baf, B.Vachon // Recent Patents on Computer Science. 2008. Vol. 1. P. 219– 237.

16. Quinn Michael J. Parallel Programming in C with MPI and OpenMP / J. Quinn Michael // McGraw-Hill Inc, 2004.

17. Learning OpenCV [Электронный ресурс]: Learning OpenCV by Gary Bradski and Adrian Kaehler. Режим доступа: https://www.bogotobogo.com/ cplusplus/files/OReilly%20Learning%20OpenCV.pdf

18. Howse J. OpenCV: Computer Vision Projects with Python / J. Howse, P.Joshi, M.Beyeler // United Kingdom, Packt Publishing, 2016. 570 p.

19. Laganiere R. OpenCV 3 Computer Vision Application Programming Cookbook / R. Laganiere // United Kingdom, Packt Publishing, 2017. 474 p.