Abstract
In order to solve the problem of uneven distribution of picture features and stitching of images, an improved SURF feature extraction method is proposed. Image feature extraction and image registration are the core of image stitching, which is directly related to stitching quality. In this paper, a comprehensive and in-depth study of feature-based image registration is carried out, and an improved algorithm is proposed. Firstly, the Heisen detection operator in the SURF algorithm is introduced to realize feature detection, and the features are extracted as much as possible. Secondly, the characteristics are described by BRIEF operator in the ORB algorithm to realize the invariance of the rotation change. Then, the European pull distance is used to complete the similarity calculation, and the KNN algorithm is used to realize the feature rough matching. Finally, the distance threshold is used to remove the matching pair with larger distance, and then the RANSAC algorithm is used to complete the purification. Experiments show that the proposed algorithm has good real-time performance, strong robustness and high accuracy.
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References
Adwan, S., Alsaleh, I., Majed, R.: A new approach for image stitching technique using dynamic time warping (DTW) algorithm towards scoliosis x-ray diagnosis. Measurement 84, 32–46 (2016)
Suk, J.H., Lyuh, C.G., Yoon, S., Roh, T.M.: Fixed homography–based real-time SW/HW image stitching engine for motor vehicles. ETRI J. 37(6), 1143–1153 (2015)
Li, G.F., Jiang, D., Zhou, Y.L., Jiang, G.Z., Kong, J.Y., Gunasekaran, M.: Human lesion detection method based on image information and brain signal. IEEE Access 7, 11533–11542 (2019)
An, J., Koo, H.I., Cho, N.I.: Unified framework for automatic image stitching and rectification. J. Electron. Imaging 24(3), 033007 (2015)
Bang, S., Kim, H., Kim, H.: Uav-based automatic generation of high-resolution panorama at a construction site with a focus on preprocessing for image stitching. Autom. Constr. 84, 70–80 (2017)
Holmes, G., Hale, M., Mcalindon, M.E., Anderson, S.: PTH-185 mapping the gastric mucosal surface: image mosaicking for capsule endoscopy. Gut 64(Suppl. 1), A490.1–A49491 (2015)
Sun, Y., et al.: Gesture recognition based on Kinect and sEMG signal fusion. Mob. Netw. Appl. 23(4), 797–805 (2018)
Sjodahl, M., Oreb, B.F.: Stitching interferometric measurement data for inspection of large optical components. Opt. Eng. 41(2), 403–408 (2015)
Johnson, B.G.: Recommendations for a system to photograph core segments and create stitched images of complete cores. J. Paleolimnol. 53(4), 437–444 (2015)
Cheng, W.T., Sun, Y., Li, G.F., Jiang, G.Z., Liu, H.H.: Jointly network: a network based on CNN and RBM for gesture recognition. Neural Comput. Appl. 31(Suppl. 1), 309–323 (2018)
Lee, C.O., Lee, J.H., Woo, H., Yun, S.: Block decomposition methods for total variation by primal—dual stitching. J. Sci. Comput. 68(1), 273–302 (2016)
Berriman, G.B., Good, J.C.: The application of the montage image mosaic engine to the visualization of astronomical images. Publ. Astron. Soc. Pac. 129(975), 058006 (2017)
Chen, D.S., et al.: An interactive image segmentation method in hand gesture recognition. Sensors 17(2), 253 (2017)
Frankl, A., Seghers, V., Stal, C., Maeyer, P.D., Petrie, G., Nyssen, J.: Using image-based modelling (Sfm–MVS) to produce a 1935 ortho-mosaic of the ethiopian highlands. Int. J. Digit. Earth 8(5), 421–430 (2015)
Vargiu, L., Rodrigueztomé, P., Sperber, G.O., Cadeddu, M., Grandi, N., Blikstad, V.: Classification and characterization of human endogenous retroviruses; mosaic forms are common. Retrovirology 13(1), 7 (2016)
Mort, R.L.: Quantitative analysis of patch patterns in mosaic tissues with clonaltools software. J. Anat. 215(6), 698–704 (2015)
Acknowledgements
This work was supported by Grants of National Natural Science Foundation of China (Grant Nos. 51575407, 51505349, 51575338, 51575412, 61733011), the Grants of National Defense Pre-Research Foundation of Wuhan University of Science and Technology (GF201705) and Open Fund of the Key Laboratory for Metallurgical Equipment and Control of Ministry of Education in Wuhan University of Science and Technology (2018B07).
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Qi, J. et al. (2019). Image Stitching Based on Improved SURF Algorithm. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science(), vol 11744. Springer, Cham. https://doi.org/10.1007/978-3-030-27541-9_42
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DOI: https://doi.org/10.1007/978-3-030-27541-9_42
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