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http://dx.doi.org/10.7780/kjrs.2022.38.6.1.7

Study of Feature Based Algorithm Performance Comparison for Image Matching between Virtual Texture Image and Real Image  

Lee, Yoo Jin (Image Engineering Research Center, 3DLabs Co. Ltd.)
Rhee, Sooahm (Image Engineering Research Center, 3DLabs Co. Ltd.)
Publication Information
Korean Journal of Remote Sensing / v.38, no.6_1, 2022 , pp. 1057-1068 More about this Journal
Abstract
This paper compares the combination performance of feature point-based matching algorithms as a study to confirm the matching possibility between image taken by a user and a virtual texture image with the goal of developing mobile-based real-time image positioning technology. The feature based matching algorithm includes process of extracting features, calculating descriptors, matching features from both images, and finally eliminating mismatched features. At this time, for matching algorithm combination, we combined the process of extracting features and the process of calculating descriptors in the same or different matching algorithm respectively. V-World 3D desktop was used for the virtual indoor texture image. Currently, V-World 3D desktop is reinforced with details such as vertical and horizontal protrusions and dents. In addition, levels with real image textures. Using this, we constructed dataset with virtual indoor texture data as a reference image, and real image shooting at the same location as a target image. After constructing dataset, matching success rate and matching processing time were measured, and based on this, matching algorithm combination was determined for matching real image with virtual image. In this study, based on the characteristics of each matching technique, the matching algorithm was combined and applied to the constructed dataset to confirm the applicability, and performance comparison was also performed when the rotation was additionally considered. As a result of study, it was confirmed that the combination of Scale Invariant Feature Transform (SIFT)'s feature and descriptor detection had the highest matching success rate, but matching processing time was longest. And in the case of Features from Accelerated Segment Test (FAST)'s feature detector and Oriented FAST and Rotated BRIEF (ORB)'s descriptor calculation, the matching success rate was similar to that of SIFT-SIFT combination, while matching processing time was short. Furthermore, in case of FAST-ORB, it was confirmed that the matching performance was superior even when 10° rotation was applied to the dataset. Therefore, it was confirmed that the matching algorithm of FAST-ORB combination could be suitable for matching between virtual texture image and real image.
Keywords
Performance evaluation; Feature matching; Virtual texture image; V-World;
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1 Bayraktar, E. and P. Boyraz, 2017. Analysis of feature detector and descriptor combinations with a localization experiment for various performance metrics, Turkish Journal of Electrical Engineering and Computer Sciences, 25(3): 2444-2454. https://doi.org/10.3906/elk-1602-225   DOI
2 Kim, J.H., J.-W. Ko, and J. Yoo, 2016. A panorama image generation method using FAST algorithm, Journal of the Korea Institute of Information and Communication Engineering, 20(3): 630-638. https://doi.org/10.6109/jkiice.2016.20.3.630   DOI
3 Luo, C., W. Yang, P. Huang, and J. Zhou, 2019. Overview of image matching based on ORB algorithm, Journal of Physics: Conference Series, 1237(3): 032020. https://doi.org/10.1088/1742-6596/1237/3/032020   DOI
4 Tomono, M., 2010. 3D localization based on visual odometry and landmark recognition using image edge points, Proc. of 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan, Oct. 18-22, pp. 5953-5959. https://doi.org/10.1109/IROS.2010.5649853   DOI
5 Kang, S.C., I.-T. Whoang, and K.-N. Choi, 2009. A Scheme for Matching Satellite Images Using SIFT, Journal of Internet Computing and Services, 10(4): 13-23.
6 Karami, E., S. Prasad, and M. Shehata, 2017. Image matching using SIFT, SURF, BRIEF and ORB: Performance comparison for distorted images, arXiv preprint arXiv:1710.02726. https://doi.org/10.48550/arXiv.1710.02726   DOI
7 Bay, H., T. Tuytelaars, and L.-V. Gool, 2006. SURF: Speeded up robust features, In: Leonardis, A., Bischof, H., Pinz, A. (eds), Computer Vision - ECCV 2006, Springer, Berlin, Heidelberg, Germany, vol. 3951, pp. 404-417. https://doi.org/10.1007/11744023_32   DOI
8 Bookstein, A., V.-A. Kulyukin, and T. Raita, 2002. Generalized hamming distance, Information Retrieval, 5(4): 353-375. https://doi.org/10.1023/A:1020499411651   DOI
9 Figat, J., T. Kornuta, and W. Kasprzak, 2014. Performance evaluation of binary descriptors of local features, In: Chmielewski, L.J., Kozera, R., Shin, B.S., Wojciechowski, K. (eds), ICCVG 2014: Computer Vision and Graphics, Springer, Cham, Switzerland, vol. 8671, pp. 187-194. https://doi.org/10.1007/978-3-319-11331-9_23   DOI
10 Jung, H.J. and J.-S. Yoo, 2015. Feature matching algorithm robust to viewpoint change, The Journal of Korean Institute of Communications and Information Sciences, 40(12): 2363-2371. https://doi.org/10.7840/kics.2015.40.12.2363   DOI
11 Kim, H.S., J.-H. Kim, D.-H. Kim, and I.-C. Kim, 2014. Design of a Real-Time Visual Loop Closure Detector using Key Frame Images, Proc. of the 2014 Fall Conference of the Korea Information Processing Society, Busan, Korea, Nov. 7-8, vol. 21, pp. 809-812. https://doi.org/10.3745/PKIPS.y2014m11a.809   DOI
12 Kim, J.H., K.-M. Koo, C.-K. Kim, and E.-Y. Cha, 2012. SURF algorithm to improve correspondence point using geometric features, Proc. of the Korean Society of Computer Information Conference, Busan, Korea, Jul. 12-14, pp. 43-46.
13 Lee, Y.H., J.-H. Park, and Y. Kim, 2013. Comparative Analysis of the Performance of SIFT and SURF, Journal of the Semiconductor & Display Technology, 12(3): 59-64 (in Korean with English abstract).
14 Pena, M. G., 2011. A comparative Study of three image matching algorithms: SIFT, SURF, and FAST, Utah State University, Logan, UT, USA.
15 Yildirim, I., F. Demirtas, B. Gulmez, U.-M. Leloglu, M. Yaman, and E.-T. Guneyi, 2019. Comparison of image matching algorithms on satellite images taken in different seasons, Proc. of Turkiye Ulusal Fotogrametri ve Uzaktan Algilama Birligi Teknik Sempozyumu, Aksaray, Turkiye, Apr. 25-27, pp. 323-330. https://hdl.handle.net/11511/77295
16 Jiang, X., J. Ma, G. Xiao, Z. Shao, and X. Guo, 2021. A review of multimodal image matching: Methods and applications, Information Fusion, 73: 22-71. https://doi.org/10.1016/j.inffus.2021.02.012   DOI
17 Rublee, E., V. Rabaud, K. Konolige, and G. Bradski, 2011. ORB: An efficient alternative to SIFT or SURF, Proc. of 2011 International Conference on Computer Vision, Barcelona, Spain, Nov. 6-13, pp. 2564-2571. https://doi.org/10.1109/ICCV.2011.6126544   DOI