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Radial Fourier Analysis (RFA) Descriptor with Fourier-based Keypoint Orientation
Stephen Ching-Feng Lin, Chin Yeow Wong, Guanna Jiang, Md Arifur Rahman, Ngai Ming Kwok
Pages - 397 - 411     |    Revised - 07-10-2014     |    Published - 10-11-2014
Volume - 8   Issue - 6    |    Publication Date - November / December 2014  Table of Contents
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KEYWORDS
Local Keypoint Descriptor, Keypoint Orientation, Fourier Transform, Keypoint Matching, SIFT Descriptor.
ABSTRACT
Local keypoint detection and description have been widely employed in a large number of computer vision applications, such as image registration, object recognition and robot localisation. Since currently available local keypoint descriptors are based on the uses of statistical analysis in spatial domain, a local keypoint descriptor, namely Radial Fourier Analysis (RFA) keypoint descriptor, is developed with the use of spectral analysis in frequency domain. This descriptor converts image gradients around SIFT keypoints to frequency domain in order to extract the principle components of the gradients and derive distinctive descriptions for representing the keypoints. Additionally, a keypoint orientation estimate is also introduced to improve the rotational invariance of the descriptor rather than simply adopting SIFT keypoint orientations. The introduced orientation estimate employs the starting point normalisation of Fourier coefficients, which are frequency responses, to deduce rotating angles that ensure keypoint correspondences are aligned at the same orientation. Through experiments and comparisons, RFA descriptor demonstrates its outstanding and robust performances against various image distortions. Particularly, the descriptor has extremely reliable performances in dealing with the images, which are degraded by blurring, JPG compression and illumination changes. All these indicate that spectral analysis has strong potential for local keypoint description.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
1 J. Li, N. Allinson, D. Tao, and X. Li, “Multitraining support vector machine for image retrieval,” IEEE Transactions on Image Processing , vol. 15, no. 11, pp. 3597–3601, 2006.
2 J. Bauer, H. Bischof, A. Klaus, and K. Karner, “Robust and fully automated image registration using invariant features,” in Proceedings of International Society for the Photogrammetry, Remote Sensing and Spatial Information Sciences , 2004, pp. 1682–1777.
3 D. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision , vol. 60, no. 2, pp. 91–110, 2004.
4 G. Dorko and C. Schmid, “Selection of scale-invariant parts for object class recognition,” in Proceedings of International Conference on Computer Vision , 2003, pp. 634–639.
5 S. Lazebnik, C. Schmid, and J. Ponce, “A sparse texture representation using affine- invariant regions,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition , vol. 2, 2003, pp. 319–324.
6 M. Yang, L. Zhang, S. K. Shiu, and D. Zhang, “Monogenic binary coding: An efficient local feature extraction approach to face recognition,” IEEE Transactions on Information Forensics and Security , vol. 7, no. 6, pp. 1738–1751, 2012.
7 Y. Fan and M. H. Meng, “3d reconstruction of the wce images by affine sift method,” in Proceedings of World Congress on Intelligent Control and Automation , 2011, pp. 943–947.
8 S. Muramatsu, D. Chugo, S. Jia, and K. Takase, “Localization for indoor service robot by using local-features of image,” in Proceedings of ICCAS-SICE , 2009, pp. 3251–3254.
9 A. Sedaghat, M. Mokhtarzade, and H. Ebadi, “Uniform robust scaleinvariant feature matching for optical remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing , vol. 49, no. 11, pp. 4516–4527, 2011.
10 X. M. Mu, J. P. Liu, W. H. Gui, Z. H. Tang, C. H. Yang, and J. Q. Li, “Machine vision based flotation froth mobility analysis,” in Proceedings of Chinese Control Conference , 2010, pp. 3012–3017.
11 K. Mikolajczyk and C. Schmid, “Scale & affine invariant interest point detectors,” International Journal of Computer Vision , vol. 60, no. 1, pp. 63–86, 2004.
12 E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, “Orb: An efficient alternative to sift or surf,” in Proceedings of IEEE International Conference on Computer Vision , 2011, pp. 2564– 2571.
13 S. Taylor and T. Drummond, “Binary histogrammed intensity patches for efficient and robust matching,” International Journal of Computer Vision , vol. 94, no. 2, pp. 241–265, 2011.
14 K. Mikolajczyk and C. Schmid, “A performance evaluation of local descriptors,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 27, no. 10, pp. 1615–1630, 2005.
15 Y. Ke and R. Sukthankar, “Pca-sift: a more distinctive representation for local image descriptors,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition , vol. 2, 2004, pp. 506–513.
16 H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “Speeded-up robust features (SURF),” Computer Vision and Image Understanding , vol. 110, no. 3, pp. 346–359, 2008.
17 S. Leutenegger, M. Chli, and R. Siegwart, “Brisk: Binary robust invariant scalable keypoints,” in Proceedings of IEEE International Conference on Computer Vision , 2011, pp. 2548–2555.
18 J. Li and N. M. Allinson, “A comprehensive review of current local features for computer vision,” Neurocomputing , vol. 71, no. 10, pp. 1771–1787, 2008.
19 L. Juan and O. Gwun, “A comparison of sift, pca-sift and surf,” International Journal of Image Processing , vol. 3, no. 4, pp. 143–152, 2009.
20 J. L. Crowley and A. C. Parker, “A representation for shape based on peaks and ridges in the difference of low-pass transform,” IEEE Transactions on Pattern Analysis and Machine Intelligence , no. 2, pp. 156–170, 1984.
21 A. Folkers and H. Samet, “Content-based image retrieval using fourier descriptors on a logo database,” in Proceedings of International Conference on Pattern Recognition , vol. 3, 2002, pp. 521–524.
22 S. Lin, C. Wong, T. Ren, and N. Kwok, “The impact of information volume on sift descriptor,” in Proceedings of International Conference on Wavelet Analysis and Pattern Recognition , 2013, pp. 287–293.
23 A. K. Jain, Fundamentals of digital image processing . Prentice-Hall, Inc., 1989.
24 A. Strehl, J. Ghosh, and R. Mooney, “Impact of similarity measures on web-page clustering,” in Proceedings of Workshop on Artificial Intelligence for Web Search , 2000, pp. 58–64.
25 A. Huang, “Similarity measures for text document clustering,” in Proceedings of New Zealand Computer Science Research Student Conference , 2008, pp. 49–56.
26 O. Chum, J. Matas, and S. Obdrzalek, “Enhancing ransac by generalized model optimization,” in Proceedings of Asian Conference on Computer Vision , vol. 2, 2004, pp. 812–817.
27 M. Cho and H. Park, “A robust keypoints matching strategy for sift: An application to face recognition,” in Neural Information Processing , ser. Lecture Notes in Computer Science, 2009, vol. 5863, pp. 716–723.
Mr. Stephen Ching-Feng Lin
The University of New South Wales - Australia
stephen.lin@unsw.edu.au
Mr. Chin Yeow Wong
The University of New South Wales - Australia
Mr. Guanna Jiang
The University of New South Wales - Australia
Mr. Md Arifur Rahman
The University of New South Wales - Australia
Dr. Ngai Ming Kwok
The University of New South Wales - Australia