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| A comparison of SIFT, PCA-SIFT and SURF
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Full
text: |
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Source |
International Journal of Image Processing (IJIP) |
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Table of Contents |
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Download
Complete Issue PDF(5.39MB) |
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Volume: 3 Issue: 4 |
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Pages: 187-245 |
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Publication
Date: August 2009 |
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ISSN
(Online): 1985-2304 |
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Pages |
143 - 152 |
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Author(s) |
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Published
Date |
21-10-2009 |
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Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
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ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
Colleague |
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KEYWORDS: SIFT, , PCA-SIFT,, SURF,, robust detectors |
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| This paper compares three robust feature detection methods, they are, Scale Invariant Feature Transform (SIFT), Principal Component Analysis (PCA) -SIFT and Speeded Up Robust Features (SURF). Lowe presented SIFT [1], which was successfully used in recognition, stitching and many other applications because of its robustness. Yan Ke [2] gave a change of SIFT by using PCA to normalize the gradient patch instead of histogram. H. Bay [3] presented a faster method for SURF, which used Fast-Hessian detector. The performance of the three methods is compared for scale changes, rotation , blur, illumination changes and affine transformations, all of which uses repeatability as an evaluation measurement. Additionally, RANSAC is used to reject the inconsistent matches [4]. SIFT presents its stability in most situation except rotation and illumination changes. SURF is the fastest one with good performance as the same as SIFT, PCA-SIFT shows its advantages in rotation, blur and illumination changes. |
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| 1 |
FOR JOURNALS: D. Lowe.”Distinctive Image Features from Scale-Invariant Keypoints”, IJCV, 60(2):91–110, 2004.
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FOR CONFERENCES: Y. Ke and R. Sukthankar.PCA-SIFT: “A More Distinctive Representation for Local Image Descriptors” ,Proc. Conf. Computer Vision and Pattern Recognition, pp. 511-517, 2004.
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FOR CONFERENCES: Bay,H,. Tuytelaars, T., &Van Gool, L.(2006). “SURF: Speeded Up Robust Features”, 9th European Conference on Computer Vision.
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FOR CONFERENCES: K. Mikolajczyk and C. Schmid. “Indexing Based on Scale Invariant Interest Points”. Proc. Eighth Int’l Conf. Computer Vision, pp. 525-531, 2001.
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FOR JOURNALS: K.Kanatani. “Geometric information criterion for model selection”, IJCV, 26(3):171-189,1998.
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FOR JOURNALS: K. Mikolajzyk and C. Schmid. “A Perforance Evaluation of Local Descriptors”, IEEE,Trans. Pattern Analysis and Machine Intelligence, vol.27, no.10, pp 1615-1630, October 2005.
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FOR JOURNALS: K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, and L.V. Gool.” A Comparison of Affine Region Detectors”, IJCV, 65(1/2):43-72, 2005.
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Eric Chu,Erin Hsu, Sandy Yu. “Image-Guided Tours: Fast-Approximated SIFT with U-SURF Features”, Stanford University.
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FOR CONFERENCES: Yang zhan-long and Guo bao-long. “Image Mosaic Based On SIFT”, International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp:1422-1425,2008.
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FOR CONFERENCES: M. Brown and D. Lowe.” Recognizing Panoramas”. Proc. Ninth Int’l Conf. Computer Vision, pp. 1218-1227, 2003.
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FOR CONFERENCES: Salgian, A.S. “Using Multiple Patches for 3D Object Recognition”, Computer Vision and Pattern Recognition, CVPR '07. pp:1-6, June 2007.
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FOR CONFERENCES: Y. Heo, K. Lee, and S. Lee. “Illumination and camera invariant stereo matching ”. In CVPR, pp:1–8, 2008.
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FOR SYMPOSIUM: Kus, M.C.; Gokmen, M.; Etaner-Uyar, S. ” Traffic sign recognition using Scale Invariant Feature Transform and color classification”. ISCIS '08. pp: 1-6, Oct. 2008.
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FOR TRANSACTIONS: Stokman, H; Gevers, T.” Selection and Fusion of Color Models for Image Feature Detection ”. Pattern Analysis and Machine Intelligence, IEEE Transactions on Volume 29, Issue 3, pp:371 – 381, March 2007.
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FOR CONFERENCES: Cheng-Yuan Tang; Yi-Leh Wu; Maw-Kae Hor; Wen-Hung Wang. “Modified sift descriptor for image matching under interference”. Machine Learning and Cybernetics, 2008 International Conference on Volume 6, pp:3294 – 3300, July 2008.
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| Luo : Colleagues
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