SPEEDING UP SIFT, PCA-SIFT AND SURF USING IMAGE PYRAMID
Abstract
Scale Invariant Feature Transform (SIFT), Principal Component Analysis (PCA)–SIFT and Speeded Up Robust Features (SURF) are common robust feature detection methods used in photogrammetry and computer vision applications. The performance of these methods have been widely investigated and compared. In terms of processing time, results show that SURF is relatively the fastest due to utilizing integral image. However, these techniques are still slow and need to be improved for nearly real time applications, such as those based on vision navigation.
This paper works on speeding up SIFT, PCA-SIFT and SURF using image pyramid. The images are firstly resampled and matched to detect the interest points. Then, the approximate locations of the matched points are determined on the original images from similar triangles. These points are surrounded by small searching windows and matched again with the corresponding searching windows in the other image. As a result, instead of matching the whole two images, a number of tiny images are matched together. The results show that the idea is powerful for reducing the processing time of such techniques significantly. The performance of this idea is affected by the resampling level and method, the image size, and the selected number of matching points.
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References
2. Yang zhan-long and Guo bao-long, 2008. Image mosaic based on SIFT, International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp:1422-1425.
3. Mikolajczyk, K. and Schmid, C., 2001. Indexing based on scale invariant interest points. Proc. Eighth int’l conf. Computer vision, pp. 525-531.
4. Brown, M. and Lowe, D., 2008. Recognizing Panoramas. Proc. Ninth Int’l Conf. Computer Vision, pp. 1218-1227.
5. Kus, M.C., Gokmen, M., Etaner-Uyar, S., 2003. Traffic sign recognition using Scale Invariant Feature Transform and color classification. ISCIS '08. pp: 1-6, Oct.
6. D. Lowe, 2004. Distinctive Image Features from Scale-Invariant Keypoints”, IJCV, 60(2):91–110.
7. Juan, L. and Gwun, O., 2009. A comparison of SIFT, PCA-SIFT and SURF. International Journal of Image Processing (IJIP), 3, 143-152.
8. Peng, K., Chen, X., Zhou, D. and Liu, Y., 2009. 3D reconstruction based on SIFT and Harris feature points. Proceedings of the IEEE International Conference on Robotics and Biomimetics, 960-964.
9. Ke, Y. and Sukthankar, R., 2004. PCA-SIFT. A More Distinctive Representation for Local Image Descriptors, Proc. Conf. Computer Vision and Pattern Recognition, pp. 511-517.
10. Bay, H,. Tuytelaars, T., and Van Gool, L., 2006. SURF: Speeded Up Robust Features. 9th European Conference on Computer Vision.
11. Amami, M., 2015. Low cost vision based personal mobile mapping system. Doctoral dissertation, University of Nottingham, UK.
12. Panchal, P. M., Panchal, S. R., and Shah, S. K. 2013. A Comparison of SIFT and SURF. International Journal of Innovative Research In Comuter And Communication Engineering 1, no.2 323-327.
13. Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., and Gool, L.V., 2005. A Comparison of Affine Region Detectors, IJCV, 65(1/2):43-72.
14. McGlone, J. C., Mikhail, E. M., Bethel, J. and Roy, M., 2004. Manual of photogrammetry. 5th ed. Bethesda, Md.: American Society of Photogrammetry and Remote Sensing.
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