Abstract
This paper evaluates the performance of SIFT and SURF for cross band matching of multispectral images. The evaluation is based on matching a reference spectral image with the images acquired at different spectral bands. The reference image possesses scale and (in-plane) rotational differences in addition to spectral variations. Additive white Gaussian noise is also added to compare performance degradation at different noise levels. We use the precision and repeatability criteria for performance evaluation. Experimental results demonstrate that SIFT performs better than SURF in multispectral environment.
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References
Cave multispectral image database, http://www.cs.columbia.edu/CAVE/databases/multispectral/
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded Up Robust Features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)
Denman, S., Lamb, T., Fookes, C., Chandran, V., Sridharan, S.: Multi-spectral fusion for surveillance system. Journal of Computers and Electrical Engineering 36(4), 643–663 (2010)
Diem, M., Lettner, M., Sablatnig, R.: Multi-spectral image acquisition and registration of ancient manuscripts. In: Proceeding of 31st AAPR/OAGM Workshop, vol. 224, pp. 129–136 (2007)
Fischler, M.A., Bolles, R.C.: Random sample concensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24(6), 381–395 (1981)
Guoa, L., Chehataa, N., Malletb, C., Boukira, S.: Relevance of airborne lidar and multispectral image data for urban scene classification using random forests. Journal of Photogrammetry and Remote Sensing 66(1), 56–66 (2011)
Juan, L., Gwon, O.: Comparison of sift, pcasift and surf. International Journal of Image Processing 3(4), 143–152 (2009)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal on Computer Vision 60(2), 91–110 (2004)
Mikolajczyk, K., Schmid, C.: Indexing based on scale invariant interest points. In: International Conference on Computer Vision, vol. 1, pp. 525–531 (2001)
Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Van Gool, L.: A comparison of affine region detectors. International Journal of Computer Vision 65, 43–72 (2005)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis & Machine Intelligence 27(10), 1615–1630 (2005)
Schmid, C., Mohr, R., Bauckhage, C.: Evaluation of interest point detectors. International Journal on Computer Vision 37(2), 151–172 (2000)
Stollnitz, E.J., DeRose, T.D., Salesin, D.H.: Wavelets for computer graphics:a primer, part i. IEEE ComputerGraphics and Applications 15(3), 76–84 (1995)
Switonski, A., Janik, L., Jedrasiak, K.: Individual features of the skin spectra. In: Proceedings of the World Congress on Engineering and Computer Science, vol. 1, pp. 147–151 (2011)
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Saleem, S., Bais, A., Sablatnig, R. (2012). A Performance Evaluation of SIFT and SURF for Multispectral Image Matching. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2012. Lecture Notes in Computer Science, vol 7324. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31295-3_20
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DOI: https://doi.org/10.1007/978-3-642-31295-3_20
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