Open Access
Issue
E3S Web Conf.
Volume 318, 2021
Second International Conference on Geotechnical Engineering – Iraq (ICGE 2021)
Article Number 04002
Number of page(s) 11
Section Remote Sensing and Environmental Engineering
DOI https://doi.org/10.1051/e3sconf/202131804002
Published online 08 November 2021
  1. Dlask, P. 2006. Using of Modified Dynamic Model (MDM) for strategy development verification. Czech Technical University in Prague, Faculty of Civil Engineering. [Google Scholar]
  2. Gruen, A., 2012. Development and status of image matching in photogrammetry. The Photogrammetric Record, 27(137), pp.36–57. [Google Scholar]
  3. Remondino, F., Spera, M.G., Nocerino, E., Menna, F., Nex, F. and Gonizzi-Barsanti, S., 2013, October. Dense image matching: comparisons and analyses. In 2013 Digital Heritage International Congress (DigitalHeritage) (Vol. 1, pp. 47–54). IEEE. [Google Scholar]
  4. Nguyen, D.D., El Ouardi, A., Aldea, E. and Bouaziz, S., 2016, December. HOOFR: An enhanced bio-inspired feature extractor. In 2016 23rd International Conference on Pattern Recognition (ICPR) (pp. 2977–2982). IEEE. [Google Scholar]
  5. Zhang, X. and Feng, Z., 2018, April. New development of the image matching algorithm. In Ninth International Conference on Graphic and Image Processing (ICGIP 2017) (Vol. 10615, p. 106151T). International Society for Optics and Photonics. [Google Scholar]
  6. Awad, A.I. and Hassaballah, M., 2016. Image feature detectors and descriptors. Studies in Computational Intelligence. Springer International Publishing, Cham [Google Scholar]
  7. Arya Raj, A.K. and Radhakrishnan, B., 2016. A Comparative Study on Target Detection in Military Field Using Various Digital Image Processing Techniques. International Journal of Computer Science and Network, 5(1), pp. 181–185, 2016. [Google Scholar]
  8. Rabatel, G. and Labbé, S., 2016. Registration of visible and near infrared unmanned aerial vehicle images based on Fourier-Mellin transform. Precision agriculture, 17(5), pp.564–587. [Google Scholar]
  9. Gao, D., Jin, L., Chen, B., Qiu, M., Li, P., Wei, Y., Hu, Y. and Wang, H., 2020, July. Fashionbert: Text and image matching with adaptive loss for cross-modal retrieval. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 2251–2260). [Google Scholar]
  10. Safaei, A., Tang, H.L. and Sanei, S., 2016. Real-time search-free multiple license plate recognition via likelihood estimation of saliency. Computers and Electrical Engineering, 56, pp.15–29. [Google Scholar]
  11. Réhault, J., Borrego-Varillas, R., Oriana, A., Manzoni, C., Hauri, C.P., Helbing, J. and Cerullo, G., 2017. Fourier transform spectroscopy in the vibrational fingerprint region with a birefringent interferometer. Optics express, 25(4), pp.4403–4413. [Google Scholar]
  12. Al Taee, E.J. and Abdulsamad, Z., 2018. A New Approach for Fingerprint Authentication in Biometric Systems Using BRISK Algorithm. International Journal on Advanced Science Engineering Information Technology, 8(5). [Google Scholar]
  13. Cament, L.A., Galdames, F.J., Bowyer, K.W. and Perez, C.A., 2015. Face recognition under pose variation with local Gabor features enhanced by active shape and statistical models. Pattern Recognition, 48(11), pp.3371–3384. [Google Scholar]
  14. Matuska, S., Hudec, R., Kamencay, P., Benco, M. and Radilova, M., 2016. A novel system for noninvasive method of animal tracking and classification in designated area using intelligent camera system. Radioengineering, 25(1), pp.161–168. [Google Scholar]
  15. Oleinik, A., 2016, July. A lightweight face tracking system for video surveillance. In International Conference on Image Analysis and Recognition (pp. 407–414). Springer, Cham. [Google Scholar]
  16. Keuper, M., Tang, S., Andres, B., Brox, T. and Schiele, B., 2018. Motion segmentation and multiple object tracking by correlation co-clustering. IEEE transactions on pattern analysis and machine intelligence, 42(1), pp.140–153. [Google Scholar]
  17. C. Wojek, B. Schiele, P. Perona, and P. Doll, “Pedestrian Detection: An Evaluation of the State of the Art,” pp. 1–20. [Google Scholar]
  18. Loncomilla, P., Ruiz-del-Solar, J. and Martinez, L., 2016. Object recognition using local invariant features for robotic applications: A survey. Pattern Recognition, 60, pp.499–514. [Google Scholar]
  19. Moreels, P. and Perona, P., 2007. Evaluation of features detectors and descriptors based on 3d objects. International journal of computer vision, 73(3), pp.263–284. [Google Scholar]
  20. Zhang, J., Liu, Z., Nezan, J.F. and Zhang, G., 2018. Correspondence matching among stereo images with object flow and minimum spanning tree aggregation. International Journal of Advanced Robotic Systems, 15(2), p.17298814–18760986 [Google Scholar]
  21. Wang, J., Li, Y., Zhang, Y., Wang, C., Xie, H., Chen, G. and Gao, X., 2011. Notice of Violation of IEEE Publication Principles: Bag-of-Features Based Medical Image Retrieval via Multiple Assignment and Visual Words Weighting. IEEE transactions on medical imaging, 30(11), pp.1996–2011. [Google Scholar]
  22. Huang, L., Zhang, Q. and Asundi, A., 2013. Camera calibration with active phase target: improvement on feature detection and optimization. Optics Letters, 38(9), pp.1446–1448. [Google Scholar]
  23. Jayanthi, N. and Indu, S., 2016. Comparison of image matching techniques. International journal of latest trends in engineering and technology, 7(3), pp.396–401. [Google Scholar]
  24. Parker, J.R., 2010. Algorithms for image processing and computer vision. John Wiley & Sons. [Google Scholar]
  25. Abdulmunem, M.E. and Hato, E., 2018. A Comparison of Corner Feature Detectors for Video Abrupt Shot Detection. Al-Nahrain Journal of Science, 21(3), pp.169–179. [Google Scholar]
  26. Jiao, W., Fang, Y. and He, G., 2008. An integrated feature based method for sub-pixel image matching. The International Archives of the Photogrammetry. [Google Scholar]
  27. Y. Washington, D. C. Crc, G. X. Ritter, J. N. Wilson, and G. X. Ritter, Boca Raton London New Computer Vision Algorithms in Image Algebra Library of Congress Cataloging-in Publication Data. 2001. [Google Scholar]
  28. Luo, C., Yang, W., Huang, P. and Zhou, J., 2019, June. Overview of image matching based on ORB algorithm. In Journal of Physics: Conference Series (Vol. 1237, No. 3, p. 032020). IOP Publishing. [Google Scholar]
  29. Hashemi, N.S., Aghdam, R.B., Ghiasi, A.S.B. and Fatemi, P., 2016. Template matching advances and applications in image analysis. arXiv preprint arXiv:1610.07231. [Google Scholar]
  30. Joglekar, J. and Gedam, S.S., 2012. Area based image matching methods-A survey. Int. J. Emerg. Technol. Adv. Eng, 2(1), pp.130–136. [Google Scholar]
  31. Liu, X., Yu, Q., Zhang, X., Shang, Y., Zhu, X. and Lei, Z., 2012. Multi-temporal and multi-sensor image matching based on local frequency information. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 39, pp.485–490. [Google Scholar]
  32. Li, J., Hu, Q. and Ai, M., 2018. RIFT: Multi-modal image matching based on radiation-invariant feature transform. arXiv preprint arXiv:1804.09493. [Google Scholar]
  33. Karami, E., Prasad, S. and Shehata, M., 2017. Image matching using SIFT, SURF, BRIEF and ORB: performance comparison for distorted images. arXiv preprint arXiv:1710.02726. [Google Scholar]
  34. Badsha, M.F., Islam, M.R. and Bulbul, M.F., 2018. Object detection by point feature matching using Matlab. Advances in Image and Video Processing, 6, pp.22–29. [Google Scholar]
  35. Suthaharan, S., Rossi, E.A., Snyder, V., Chhablani, J., Lejoyeux, R., Sahel, J.A. and Dansingani, K.K., 2020. Laplacian feature detection and feature alignment for multimodal ophthalmic image registration using phase correlation and Hessian affine feature space. Signal processing, 177, p.107733. [Google Scholar]
  36. Madbouly, A.M.M., Wafy, M. and Mostafa, M.S.M., 2015. Performance assessment of feature detector-descriptor combination. International Journal of Computer Science Issues (IJCSI), 12(5), p.87. [Google Scholar]
  37. Johansson, J., 2015. Interest point detectors and descriptors for IR images: an evaluation of common detectors and descriptors on IR images. M.Sc. Thesis, KTH, School of Computer Science and Communication (CSC). [Google Scholar]
  38. Liu, Y., Zhang, H., Guo, H. and Xiong, N.N., 2018. A fast-brisk feature detector with depth information. Sensors, 18(11), p.3908. [Google Scholar]
  39. Makarov, A., Bolsunovskaya, M. and Zhigunova, O., 2018. Comparative analysis of methods for keypoint detection in images with different illumination level. In MATEC Web of Conferences (Vol. 239, p. 01028). EDP Sciences. [Google Scholar]
  40. Information on https://www.mathworks.com/help/vision/ref/detectbriskfeatures.html. [Google Scholar]
  41. Ha, Y., Kweon, G. and Kim, Y., 2020. Monitoring Technique Using a Vision-based Single-Camera System for Reinforced Soil Retaining Wall. Journal of the Korean Society of Hazard Mitigation, 20(6), pp.209–219. [Google Scholar]
  42. Rosten, E. and Drummond, T., 2006, May. Machine learning for high-speed corner detection. In European conference on computer vision (pp. 430–443). Springer, Berlin, Heidelberg. [Google Scholar]
  43. Babri, U.M., Tanvir, M. and Khurshid, K., 2016. Feature based correspondence: a comparative study on image matching algorithms. Int. J. Adv. Comput. Sci. Appl, 7(3), pp.206–210. [Google Scholar]
  44. Information on https://www.mathworks.com/help/vision/ref/detectfastfeatures.html. [Google Scholar]
  45. Portilla, K., Santos, V., Trujillo, M.F. and Rosales, A., 2017, November. Non-invasive heart rate monitor applying independent component analysis in videos. In 2017 International Conference on Information Systems and Computer Science (INCISCOS) (pp. 121–127). IEEE. [Google Scholar]
  46. Han, Y., Chen, P. and Meng, T., 2015. July. Harris corner detection algorithm at sub-pixel level and its application. In 2015 International Conference on Computational Science and Engineering. Atlantis Press. [Google Scholar]
  47. Information on https://www.mathworks.com/help/vision/ref/detectharrisfeatures.html. [Google Scholar]
  48. Jalil, A. H., 2020. Generating a 3D-model in close-range photogrammetry using auto-matching technique. M.Sc. Thesis, University of Technology, Iraq. [Google Scholar]
  49. Gutjahr, G., Chandrashekar, P., Nair, M.G., Haridas, M. and Nedungadi, P., 2021. Comparison of Hidden Markov Models and the FAST Algorithm for Feature-Aware Knowledge Tracing. In Machine Learning for Predictive Analysis (pp. 269–276). Springer, Singapore. [Google Scholar]
  50. Information on https://support.apple.com/kb/SP744?locale=en_US. [Google Scholar]

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