Abstract
Image registration has widespread application in fields like medical imaging, satellite imagery and agriculture precision as it is essential for feature detection and extraction. The extent of this paper is focussed on analysis of intensity and feature-based registration algorithms over Blue and RedEdge multispectral images of wheat and cauliflower field under different altitudinal conditions i.e., drone imaging at 3 m for cauliflower and handheld imaging at 1 m for wheat crops. The overall comparison among feature and intensity-based algorithms is based on registration quality and time taken for feature matching. Intra-class comparison of feature-based registration is parameterized on type of transformation, number of features being detected, number of features matched, quality and feature matching time. Intra-class comparison of intensity-based registration algorithms is based on type of transformation, nature of alignment, quality and feature matching time. This study has considered SURF, MSER, KAZE, ORB for feature-based registration and Phase Correlation, Monomodal intensity and Multimodal intensity for intensity-based registration. Quantitatively, feature-based techniques were found superior to intensity-based techniques in terms of quality and computational time, where ORB and MSER scored highest. Among intensity-based methods, Monomodal intensity performed best in terms of registration quality. However, Phase Correlation marginally scored less in quality but fared well in terms of computational time.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Boda, S.: Feature-Based Image Registration (2009)
Alam, M.S., Morshidi, M.A., Gunawan, T.S., Olanrewaju, R.F.: A comparative analysis of feature extraction algorithms for augmented reality applications. In: 2021 IEEE 7th International Conference of Smart Instrumentation, Measurement and Application, ICSIMA 2021, pp. 59–63 (2021). https://doi.org/10.1109/ICSIMA50015.2021.9526295
Sedaghat, A., Mohammadi, N.: ISPRS journal of photogrammetry and remote sensing uniform competency-based local feature extraction for remote sensing images. ISPRS J. Photogramm. Remote Sens. 135, 142–157 (2018). https://doi.org/10.1016/j.isprsjprs.2017.11.019
Sharma, V., Mir, R.N.: A comprehensive and systematic look up into deep learning based object detection techniques: a review. Comput. Sci. Rev. 38, 100301 (2020). https://doi.org/10.1016/j.cosrev.2020.100301
Banerjee, A., Mistry, D.: Comparison of feature detection and matching approaches: SIFT and SURF. GRD Journals-Global Res. Dev. J. Eng. 2, 7–13 (2017)
Bay, H., Ess, A.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110, 346–359 (2008). https://doi.org/10.1016/j.cviu.2007.09.014
Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 14 (2007). https://doi.org/10.1016/j.cviu.2007.09.014
Ma, J., Jiang, X., Fan, A., Jiang, J., Yan, J.: Image matching from handcrafted to deep features: a survey. Int. J. Comput. Vision 129(1), 23–79 (2020). https://doi.org/10.1007/s11263-020-01359-2
Qin, C., Hu, Y., Yao, H., Duan, X., Gao, L.: Perceptual image hashing based on weber local binary pattern and color angle representation. IEEE Access. 7, 45460–45471 (2019). https://doi.org/10.1109/ACCESS.2019.2908029
Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. Image Vis. Comput. 22, 761–767 (2004). https://doi.org/10.1016/j.imavis.2004.02.006
Nist, D., Stew, H.: Scalable recognition with a vocabulary tree. In: IEEE computer society conference on computer vision and pattern recognition, p. 8 (2006). https://doi.org/10.1109/CVPR.2006.264
Korkmaz, Sevcan Aytac; Esmeray, F.: Classification with random forest based on local tangent space alignment and neighborhood preserving embedding for MSER features: MSER_DFT_LTSA-NPE_RF. Int. J. Mod. Res. Eng. Technol. 3, 7 (2018)
Dutta, K., Das, N., Kundu, M., Nasipuri, M.: Text localization in natural scene images using extreme learning machine. In: International conference on advanced computational and communication paradigms, ICACCP 2019, pp. 8–13. IEEE (2019). https://doi.org/10.1109/ICACCP.2019.8882986
Sun, Y., Li, H., Sun, L.: A novel wide-baseline stereo matching algorithm combining MSER and DAISY. In: International conference on computer science and application engineering, p. 5 (2018). https://doi.org/10.1145/3207677.3277960
Huang, Po-Hsun; Chen, Y., Fuh, C.: String finding based on application connected to server (2017). https://www.csie.ntu.edu.tw/~fuh/personal/StringFindingBasedonApplicationConnectedtoServer.pdf
Murugesan, M., Thilagamani, S.: Efficient anomaly detection in surveillance videos based on multi layer perception recurrent neural network. Microprocess. Microsyst. 79, 103303 (2020). https://doi.org/10.1016/j.micpro.2020.103303
Ahmed, S.B., Naz, S.; Razzak, M.I., Yusof, R.B.: A novel dataset for English–Arabic scene text recognition (EASTR)-42K and its evaluation using invariant feature extraction on detected extremal regions. IEEE Access. 7, 20 (2019). https://doi.org/10.1109/ACCESS.2019.2895876
Ahmed, S.B, Razzak, M.I., Yusof, R.: Cursive Script Text Recognition in Natural Scene Images. Springer (2020). https://doi.org/10.1007/978-981-15-1297-1
Gupta, N., Rohil, M.K.: Image feature detection using an improved implementation of maximally stable extremal regions for augmented reality applications. Int. J. Image Data Fusion. 9, 43–62 (2018). https://doi.org/10.1080/19479832.2017.1391337
Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: British machine vision conference, pp. 384–393 (2002)
Rublee, E., Garage, W., Park, M.: ORB : an efficient alternative to SIFT or SURF. In: International conference on computer vision, pp. 2564–2571. IEEE (2011)
Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 430–443. Springer, Heidelberg (2006). https://doi.org/10.1007/11744023_34
Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF : binary robust independent elementary features. In: European conference on computer vision, pp. 778–792 (2010)
Qiao, X., Ren, P., Dustdar, S., Chen, J.: A New Era for Web AR with Mobile Edge Computing. IEEE (2018). https://doi.org/10.1109/MIC.2018.043051464
Jing, J., Gao, T., Zhang, W., Gao, Y., Sun, C.: Image feature information extraction for interest point detection: a comprehensive review. Comput. Vis. Pattern Recogn. 1–34 (2021)
Bal, B., Erdem, T., Kul, S., Sayar, A.: Image-based locating and guiding for unmanned aerial vehicles using scale invariant feature transform, speeded-up robust features, and oriented fast and rotated brief algorithms. Concur. Comput. Pract. Exp. 24, 11 (2021). https://doi.org/10.1002/cpe.6766
Zhang, Z., Wang, L., Zheng, W., Yin, L., Hu, R., Yang, B.: Endoscope image mosaic based on pyramid ORB. Biomed. Signal Process. Control. 71, 103261 (2022). https://doi.org/10.1016/j.bspc.2021.103261
Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.): ECCV 2012. LNCS, vol. 7577. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33783-3
Alcantarilla, P.F., Bartoli, A.: Fast explicit diffusion for accelerated features in nonlinear scale spaces. In: British machine vision conference, p. 1 (2013)
Oppenheim, A. V., Lim, J.S.: Importance of phase in signals. In: Proceedings of the IEEE (1981). https://doi.org/10.1109/PROC.1981.12022
Ye, Y., Shan, J., Member, S., Bruzzone, L., Shen, L.: Robust Registration of multimodal remote sensing images based on structural similarity. IEEE Trans. Geosci. Remote Sens. 55, 2941–2958 (2017). https://doi.org/10.1109/TGRS.2017.2656380
Viola, P., Iii, W.M.W.: Alignment by maximization of mutual information. Int. J. Comput. Visionm. 24, 137–154 (1997). https://doi.org/10.1023/A:1007958904918
Gao, Z., Gu, B., Lin, J.: Monomodal image registration using mutual information based methods. Image Vis. Comput. 26, 164–173 (2008). https://doi.org/10.1016/j.imavis.2006.08.002
Hel-or, Y., Hel-or, H., David, E.: Fast template matching in non-linear tone-mapped images. IEEE Trans. Pattern Anal. Mach. Intell. 1355–1362 (2011). https://doi.org/10.1109/ICCV.2011.6126389
Acknowledgement
The completion of this analysis would not have been accomplished without the support of our team at Department of Agriculture Sciences, University of Napoli Federico II. We are grateful for all the data resources that they have been sharing with us for research purpose. It’s a heartfelt thanks from us for all the opportunities and resources that have been provided by them.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Rana, S., Gerbino, S., Mehrishi, P., Crimaldi, M. (2022). Comparative Analysis of Feature and Intensity Based Image Registration Algorithms in Variable Agricultural Scenarios. In: Chen, J.IZ., Tavares, J.M.R.S., Shi, F. (eds) Third International Conference on Image Processing and Capsule Networks. ICIPCN 2022. Lecture Notes in Networks and Systems, vol 514. Springer, Cham. https://doi.org/10.1007/978-3-031-12413-6_12
Download citation
DOI: https://doi.org/10.1007/978-3-031-12413-6_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-12412-9
Online ISBN: 978-3-031-12413-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)