Fig. 1. Work flow of seamless image mosaicking
Fig. 2. RANSAC algorithm to improve matching between adjacent images
Fig. 3. Determination of overlap region with EOP information
Fig. 4. Determination of overlap region using image matching
Fig. 5. Canny edge detection
Fig. 6. Edges after anisotropic diffusion with different coefficient and iteration
Fig. 7. Edges in image pyramid after applying anisotropic diffusion
Fig. 8. Seamline search scheme
Fig. 9. Incorrectly determined seamlines
Fig. 10. Mosaicking of image block
Fig. 11. Components of an edge segment
Fig. 12. Classification of edges based on proposed method
Fig. 13. Proposed scheme for seamline determination
Fig. 14. Cumulative histogram matching
Fig. 15. Adjacent images before histogram matching
Fig. 16. Histogram matching results from different schemes
Fig. 17. Seamline feathering region
Fig. 18. Seamline feathering region in overlap area
Fig. 19. Demonstration of feathering by Laplacian pyramid blending algorithm
Fig. 20. Aerial image: Case A
Fig. 21. Aerial image: Case B
Fig. 22. Terrestrial image: Case C
Fig. 23. Terrestrial image: Case D
Fig. 24. Building and road layers from digital map in overlap region of Case A
Fig. 25. Seamline from image and mosaicked image of Case A
Fig. 26. Seamline from digital map and mosaicked image of Case A
Fig. 27. Seamline from image and mosaicked image of Case B
Fig. 28. Seamline from image and mosaicked image of Case C
Fig. 29. Seamline from images in strip 1 and mosaicked image of Case D
Fig. 30. Seamline from images in strip 2 and mosaicked image of Case D
Fig. 31. Seamline from image of strip 1 and 2, and mosaicked image of Case D
Fig. 32. Seamline comparison and difference of mosaicked images
Fig. 33. Comparison of seamlines
Fig. 34. Seamline characteristics
Fig. 35. Close-range terrestrial image mosaicking
Table 1. Test images
참고문헌
- Anitha, J. and Deepa, S. (2014), Tracking and recognition of objects using SURF descriptor and Harris corner detection, International Journal of Current Engineering and Technology, Vol. 4, No. 2, pp. 775-778.
- Awrangjeb, M. and Lu, G. (2008), An improved curvature scale-space corner detector and a robust corner matching technique for transformed image identification, IEEE Transactions on Image Processing, Vol. 17, No. 12, pp. 2425-2441. https://doi.org/10.1109/TIP.2008.2006441
- Bay, H., Tuytelaars, T., and Van Gool, L. (2006), SURF: Speeded up robust features, In: Leonardis, A, Bischof, H., and Pinz, A. (eds.), Computer Vision: Proceedings 9th European Conference on Computer Vision Part I, Lecture Notes in Computer Science (LNCS 3951), Springer-Verlag, Berlin, Heidelberg, pp. 404-417.
- Brown, M., Szeliski, R., and Winder, S. (2004), Multi-Image Matching Using Multi-scale Oriented Patches, Technical Report MSR-TR-2004-133, Microsoft Corporation, Redmond, WA, 46p.
- Brown, M. and Lowe, D. (2007), Automatic panoramic image stitching using invariant features, International Journal of Computer Vision, Vol. 74, No. 1, pp. 59-73. https://doi.org/10.1007/s11263-006-0002-3
- Burt, P. and Adelson E. (1983), The Laplacian pyramid as a compact image code, IEEE transactions on Communications, Vol. 31, No. 4, pp. 532-540. https://doi.org/10.1109/TCOM.1983.1095851
- Canny, J. (1986), A computational approach to edge detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 8, pp. 679-698.
- Chen, Q., Sun, M., Hu, X., and Zhang, Z. (2014), Automatic seamline network generation for urban orthophoto mosaicking with the use of a digital surface model, Remote Sensing, Vol. 6, pp. 12334-12359. https://doi.org/10.3390/rs61212334
- Choi, J., Jung, H., and Yun, S. (2015), An efficient mosaic algorithm considering seasonal variation: Application to KOMPSAT-2 satellite images, Sensors, Vol. 15, pp. 5650-5665.
- Chon, J., Kim, H., and Lin, C. (2010), Seam-line determination for image mosaicking: A technique minimizing the maximum local mismatch and the global cost, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 65, pp. 86-92. https://doi.org/10.1016/j.isprsjprs.2009.09.001
- Dijkstra, E. (1959), A note on two problems in connection with graphs, Numerische Mathematik, Vol. 1, No.1, pp. 269-271. https://doi.org/10.1007/BF01386390
- Fischler, M. and Bolles, R. (1981), Random sample consensus: A paradigm for model fitting with application to image analysis and automated cartography, Communications of the ACM, Vol. 24, pp. 381-395. https://doi.org/10.1145/358669.358692
- Gerig, G., Kubler, O., Kikinis, R., and Jolesz, F. (1992), Nonlinear anisotropic filtering of MRI data, IEEE Transactions on Medical Imaging, Vol. 11, No. 2, pp. 221-232. https://doi.org/10.1109/42.141646
- Horn, B. (1986), Robot Vision, MIT Press, Cambridge, MA, 509p.
- Jung, H., Lim, S., and Lee, D.C. (2008), Utilizing airborne LiDAR Data for building extraction and superstructure analysis for modeling, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 26, No. 3, pp. 227-239.
- Kim, D., Yoon, U., and Jo, G. (2016), Feature point filtering method based on CS-RANSAC for efficient planar homography estimating, Korea Information Processing Society Transactions on Software and Data Engineering, Vol. 5, No. 6 pp. 307-312.
- Kim, J. and Kim, D. (2014), Matching points filtering applied panorama image processing using SURF and RANSAC Algorithm, Journal of The Institute of Electronics and Information Engineers, Vol. 51, No. 4, pp. 820-834.
- Lee, D. (2018), Determination of High Level-of-Detail Model Key Points of Buildings Using Airborne LiDAR data, Master's thesis, Sejong University, Seoul, Korea, 138p.
- Lee, D. and Lee, D.C. (2016), Satellite image geocoding using image and map matching, Proceedings of Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography, 28-29 April, Suwon, Korea, unpagenated CD-ROM. (in Korean with English abstract)
- Li, L., Yao, J., Liu, Y., Yuan, W., Shi, S., and Yuan, S. (2017), Optimal seamline detection for orthoimage mosaicking by combining deep convolutional neural network and graph cuts, Remote Sensing, Vol. 9, No. 7, pp. 1-22.
- Litvinov, A. and Schechner, Y. (2005), Radiometric framework for image mosaicking, Journal of the Optical Society of America, Vol. 22, No. 5, pp. 839-848. https://doi.org/10.1364/JOSAA.22.000839
- Lowe, D. (2004), Distinctive image features from scale-invariant key points, Computer Vision and Image Understanding, Vol. 20, pp. 91-110.
- Mills, S. and McLeod, P. (2013), Global seamline networks for orthomosaic generation via local search, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 75, No. 1, pp. 101-111. https://doi.org/10.1016/j.isprsjprs.2012.11.003
- Oshel, E., Stevenson, P., and Heipke, C. (1995), Experiments with anisotropic diffusion for adaptive image smoothing, In: Heipke, C. (ed.), From Digital Images to Spatial Objects, Report No. 433, Dept. of Geodetic Science and Surveying, The Ohio State University, Columbus, OH, pp. 67-85.
- Pan, J., Zhou, Q., and Wang, M. (2014), Seamline determination based on segmentation for urban image mosaicking, IEEE Geoscience and Remote Sensing Letters, Vol. 11, No. 8, pp. 1335-1339. https://doi.org/10.1109/LGRS.2013.2293197
- Perona, P. and Malik, J. (1990), Scale-space and edge detection using anisotropic diffusion, IEEE Transactions on Pattern Analysis And Machine Intelligence, Vol. 12. No. 7, pp. 629-639. https://doi.org/10.1109/34.56205
- Rosten, E. and Drummond, T. (2006), Machine learning for high speed corner detection, In: Leonardis, A, Bischof, H., and Pinz, A. (eds.), Computer Vision: Proceedings 9th European Conference on Computer Vision Part I, Lecture Notes in Computer Science (LNCS 3951), Springer-Verlag, Berlin, Heidelberg, pp. 430-443.
- Saito, S., Arai, R., and Aoki, Y. (2015), Seamline determination based on semantic segmentation for aerial image mosaicking, IEEE Access, Vol. 3, pp. 2847-2856. https://doi.org/10.1109/ACCESS.2015.2508921
- Sapiro, G. (2006), Geometric Partial Differential Equations and Image Analysis, Cambridge University Press, New York, NY, 385p.
- Song, N. (2005), Methodology of Seamline Extraction for Automatic Image Mosaic Generation, Master's thesis, Inha University, Incheon, Korea, 63p.
- Woo, H,, Noh, M., Park, J., Cho, W., and Kim, B. (2009), Color assessment for mosaic imagery using HSI model, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 27, No. 4, pp. 429-435.