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http://dx.doi.org/10.9708/jksci.2014.19.1.023

Filtering Feature Mismatches using Multiple Descriptors  

Kim, Jae-Young (School of Electrical Engineering, University of Ulsan)
Jun, Heesung (School of Electrical Engineering, University of Ulsan)
Publication Information
Journal of the Korea Society of Computer and Information / v.19, no.1, 2014 , pp. 23-30 More about this Journal
Abstract
Feature matching using image descriptors is robust method used recently. However, mismatches occur in 3D transformed images, illumination-changed images and repetitive-pattern images. In this paper, we observe that there are a lot of mismatches in the images which have repetitive patterns. We analyze it and propose a method to eliminate these mismatches. MDMF(Multiple Descriptors-based Mismatch Filtering) eliminates mismatches by using descriptors of nearest several features of one specific feature point. In experiments, for geometrical transformation like scale, rotation, affine, we compare the match ratio among SIFT, ASIFT and MDMF, and we show that MDMF can eliminate mismatches successfully.
Keywords
feature matching; feature descriptor; feature extraction; object recognition; repetitive pattern;
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  • Reference
1 D. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," International Journal of Computer Vision, Vol. 60, No. 2, pp. 91-110, Nov. 2004.   DOI   ScienceOn
2 Y. Ke, R. Sukthankar, "PCA-SIFT: a more distinctive representation for local image descriptors," IEEE International Conference on Computer Vision and Pattern Recognition, pp 506-513, 2004.
3 K. Mikolajczyk, C. Schmid, "A performance evaluation of local descriptors," IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 27, No. 10, pp. 1615-1630, 2005.   DOI   ScienceOn
4 J. M. Morel, G. Yu, "ASIFT: a new framework for fully affine invariant image comparison," SIAM Journal on Imaging Sciences, Vol. 2, No. 2, pp. 438-469, 2009.   DOI
5 P. Pritchett, and A. Zisserman, "Wide baseline stereo matching," IEEE International Conference on Computer Vision, pp. 754-760, Jan. 1998.
6 W. Wei, H. Jun, T. Yiping, "Image Matching for Geomorphic Measurement Based on SIFT and RANSAC Methods," International Conference on Computer Science and Software Engineering, vol. 2. pp. 317-320, Dec. 2008.
7 M. Leodeanu, M. Hebert, "A Spectral Technique for Correspondence Problems Using Pairwise Constraints," IEEE International Conference on Computer Vision, vol. 2, pp. 1482-1489, Oct. 2005.
8 O. Duchenne, F. Bach, K. Inso, J. Ponce, "A Tensor-Based Algorithm for High-Order Graph Matching," IEEE International Conference on Computer Vision and Patter Recognition, pp. 1980-1987, June 2009.
9 S. Leutenegger, M. Chli, R. Y. Siegwart, "BRISK : Binary Robust invariant scalable keypoints," IEEE International Conference on Computer Vision, pp. 2548-2555, Nov. 2011.
10 E. Rublee, V. Rabaud, K. Konolige, G. Bradski, "ORB: An efficient alternative to SIFT or SURF," IEEE International Conference on Computer Vision, pp. 2564-2571, Nov. 2011.
11 Affine-SIFT(ASIFT) web site, http://www.cmap. polytechnique.fr/-yu/research/ASIFT/demo.html
12 OpenCV (Open Source Computer Vision), http://opencv.org/
13 J. Wu, J.M. Rehg, "CENTRIST: a visual descriptor for scene categorization," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, No. 8, pp. 1489-1501, 2011.   DOI   ScienceOn
14 T. Tuytelaars, L. Van Gool, "Matching widely separated views based on affine invariant region," International Journal of Computer Vision, Vol.59, No. 1, pp. 56-75, 2004.
15 G. Zhang, Y. Wang, "Robust 3D face recognition based on resolution invariant features," Pattern Recognition Letters Vol. 32, No. 7, pp. 1009-1019, 2011.   DOI   ScienceOn