Distributed Aerial Image Stitching on Multiple Processors using Message Passing Interface

Alif Ramadhan - Politeknik Elektronika Negeri Surabaya, Jl. Raya ITS, Surabaya, 60111, Indonesia
Fira Aulia - Politeknik Elektronika Negeri Surabaya, Jl. Raya ITS, Surabaya, 60111, Indonesia
Ni Made Dewi - Politeknik Elektronika Negeri Surabaya, Jl. Raya ITS, Surabaya, 60111, Indonesia
Idris Winarno - Politeknik Elektronika Negeri Surabaya, Jl. Raya ITS, Surabaya, 60111, Indonesia
Sritrusta Sukaridhoto - Politeknik Elektronika Negeri Surabaya, Jl. Raya ITS, Surabaya, 60111, Indonesia


Citation Format:



DOI: http://dx.doi.org/10.62527/joiv.8.1.1890

Abstract


This study investigates the potential of using Message Passing Interface (MPI) parallelization to enhance the speed of the image stitching process. The image stitching process involves combining multiple images to create a seamless panoramic view. This research explores the potential benefits of segmenting photos into distributed tasks among several identical processor nodes to expedite the stitching process. However, it is crucial to consider that increasing the number of nodes may introduce a trade-off between the speed and quality of the stitching process. The initial experiments were conducted without MPI, resulting in a stitching time of 1506.63 seconds. Subsequently, the researchers employed MPI parallelization on two computer nodes, which reduced the stitching time to 624 seconds. Further improvement was observed when four computer nodes were used, resulting in a stitching time of 346.8 seconds. These findings highlight the potential benefits of MPI parallelization for image stitching tasks. The reduced stitching time achieved through parallelization demonstrates the ability to accelerate the overall stitching process. However, it is essential to carefully consider the trade-off between speed and quality when determining the optimal number of nodes to employ. By effectively distributing the workload across multiple nodes, researchers and practitioners can take advantage of the parallel processing capabilities offered by MPI to expedite image stitching tasks. Future studies could explore additional optimization techniques and evaluate the impact on speed and quality to achieve an optimal balance in real-world applications.


Keywords


MPI parallelization; Image stitching; Distributed tasks; Parallel processing; Optimization techniques;

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References


E. Adel, M. Elmogy, and H. Elbakry, “Image stitching based on feature extraction techniques: a survey,” International Journal of Computer Applications, vol. 99, no. 6, pp. 1–8, 2014.

W. Lyu, Z. Zhou, L. Chen, and Y. Zhou, “A survey on image and video stitching,” Virtual Reality & Intelligent Hardware, vol. 1, no. 1, Art. no. 1, Feb. 2019, doi: 10.3724/SP.J.2096-5796.2018.0008.

B. Ma et al., “A fast algorithm for material image sequential stitching,” Computational Materials Science, vol. 158, pp. 1–13, Feb. 2019, doi: 10.1016/j.commatsci.2018.10.044.

K. Li and G. Ding, “A Novel Automatic Image Stitching Algorithm for Ceramic Microscopic Images,” in 2018 International Conference on Audio, Language and Image Processing (ICALIP), Shanghai: IEEE, Jul. 2018, pp. 17–21. doi: 10.1109/ICALIP.2018.8455766.

X. Li et al., “Full length image stitching algorithm for spinal deformity surgery,” Procedia Computer Science, vol. 209, pp. 93–102, 2022, doi: 10.1016/j.procs.2022.10.103.

R. Xie et al., “Automatic multi-image stitching for concrete bridge inspection by combining point and line features,” Automation in Construction, vol. 90, pp. 265–280, Jun. 2018, doi: 10.1016/j.autcon.2018.02.021.

G. Rosa et al., “Hyperspectral Images Acquisition: an Efficient Capture and Processing Stitching Procedure for Medical Environments,” in 2020 XXXV Conference on Design of Circuits and Integrated Systems (DCIS), Segovia, Spain: IEEE, Nov. 2020, pp. 1–6. doi: 10.1109/DCIS51330.2020.9268658.

S. Wang, C. Liu, and Y. Zhang, “Fully convolution network architecture for steel-beam crack detection in fast-stitching images,” Mechanical Systems and Signal Processing, vol. 165, p. 108377, Feb. 2022, doi: 10.1016/j.ymssp.2021.108377.

Y. Liu, M. He, Y. Wang, Y. Sun, and X. Gao, “Farmland Aerial Images Fast-Stitching Method and Application Based on Improved SIFT Algorithm,” IEEE Access, vol. 10, pp. 95411–95424, 2022, doi: 10.1109/ACCESS.2022.3204657.

T. Hovhannisyan, P. Efendyan, and M. Vardanyan, “Creation of a digital model of fields with application of DJI phantom 3 drone and the opportunities of its utilization in agriculture,” Annals of Agrarian Science, vol. 16, no. 2, Art. no. 2, Jun. 2018, doi: 10.1016/j.aasci.2018.03.006.

T. Zhang and M. Zhu, “GPS-assisted Aerial Image Stitching Based on optimization Algorithm,” in 2019 Chinese Control Conference (CCC), Guangzhou, China: IEEE, Jul. 2019, pp. 3485–3490. doi: 10.23919/ChiCC.2019.8866089.

O. Chum and J. Matas, “Optimal Randomized RANSAC,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 8, Art. no. 8, Aug. 2008, doi: 10.1109/TPAMI.2007.70787.

G. Yang, X. Chang, and Z. Jiang, “A Fast Aerial Images Mosaic Method Based on ORB Feature and Homography Matrix,” in 2019 International Conference on Computer, Information and Telecommunication Systems (CITS), Beijing, China: IEEE, Aug. 2019, pp. 1–5. doi: 10.1109/CITS.2019.8862133.

H. Zhao, Y. Du, H. Wang, and Y. Yue, “UAV aerial image mosaic algorithm based on FAST-Tomasi feature and Delaunay triangulation,” in 2020 IEEE International Conference on Mechatronics and Automation (ICMA), Beijing, China: IEEE, Oct. 2020, pp. 1088–1093. doi: 10.1109/ICMA49215.2020.9233570.

N. T. Pham, S. Park, and C.-S. Park, “Fast and Efficient Method for Large-Scale Aerial Image Stitching,” IEEE Access, vol. 9, pp. 127852–127865, 2021, doi: 10.1109/ACCESS.2021.3111203.

J. L. Hennessy and D. A. Patterson, “Fundamentals of quantitative design and analysis,” Computer Architecture: A Quantitative Approach, pp. 1–10, 2012.

W. D. Hillis, “What is massively parallel computing, and why is it important?,” Daedalus, vol. 121, no. 1, Art. no. 1, 1992.

P. Czarnul, J. Proficz, and K. Drypczewski, “Survey of methodologies, approaches, and challenges in parallel programming using high-performance computing systems,” Scientific Programming, vol. 2020, pp. 1–19, 2020.

J. J. Dongarra, S. W. Otto, M. Snir, and D. Walker, “An introduction to the MPI standard,” Communications of the ACM, vol. 18, 1995.

S. A. Dheyab, M. N. Abdullah, and B. F. Abed, “A novel approach for big data processing using message passing interface based on memory mapping,” Journal of Big Data, vol. 6, no. 1, Art. no. 1, 2019.

Z. Jiang et al., “Message passing optimization in robot operating system,” International Journal of Parallel Programming, vol. 48, pp. 119–136, 2020.

T. Ragunthar, P. Ashok, N. Gopinath, and M. Subashini, “A strong reinforcement parallel implementation of k-means algorithm using message passing interface,” Materials Today: Proceedings, vol. 46, pp. 3799–3802, 2021.

E. D. Fajrianti, A. A. Pratama, J. A. Nasyir, A. Rasyid, I. Winarno, and S. Sukaridhoto, “High-Performance Computing on Agriculture: Analysis of Corn Leaf Disease,” JOIV: International Journal on Informatics Visualization, vol. 6, no. 2, Art. no. 2, 2022.

C. A. Swann, “Software for parallel computing: the LAM implementation of MPI,” J. Appl. Econ., vol. 16, no. 2, Art. no. 2, Mar. 2001, doi: 10.1002/jae.595.

J. M. Squyres and A. Lumsdaine, “A Component Architecture for LAM/MPI,” in Recent Advances in Parallel Virtual Machine and Message Passing Interface, J. Dongarra, D. Laforenza, and S. Orlando, Eds., in Lecture Notes in Computer Science, vol. 2840. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003, pp. 379–387. doi: 10.1007/978-3-540-39924-7_52.

S. M. Pizer et al., “Adaptive histogram equalization and its variations,” Computer vision, graphics, and image processing, vol. 39, no. 3, Art. no. 3, 1987.

T. Arici, S. Dikbas, and Y. Altunbasak, “A histogram modification framework and its application for image contrast enhancement,” IEEE Transactions on image processing, vol. 18, no. 9, Art. no. 9, 2009.

B. S. Rao, “Dynamic histogram equalization for contrast enhancement for digital images,” Applied Soft Computing, vol. 89, p. 106114, 2020.

D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International journal of computer vision, vol. 60, pp. 91–110, 2004.

T. Lindeberg, “Scale Invariant Feature Transform,” Scholarpedia, vol. 7, no. 5, p. 10491, May 2012, doi: 10.4249/scholarpedia.10491.

L. Dalcin and Y.-L. L. Fang, “mpi4py: Status update after 12 years of development,” Computing in Science & Engineering, vol. 23, no. 4, Art. no. 4, 2021.

J. Satriawan, “3D Object Mapping using Drone Based on Autonomous Waypoint Navigation,” Unpublished Paper, 2023.