Browse > Article
http://dx.doi.org/10.3837/tiis.2010.12.016

A New Shape Adaptation Scheme to Affine Invariant Detector  

Liu, Congxin (Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University)
Yang, Jie (Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University)
Zhou, Yue (Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University)
Feng, Deying (Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.4, no.6, 2010 , pp. 1253-1272 More about this Journal
Abstract
In this paper, we propose a new affine shape adaptation scheme for the affine invariant feature detector, in which the convergence stability is still an opening problem. This paper examines the relation between the integration scale matrix of next iteration and the current second moment matrix and finds that the convergence stability of the method can be improved by adjusting the relation between the two matrices instead of keeping them always proportional as proposed by previous methods. By estimating and updating the shape of the integration kernel and differentiation kernel in each iteration based on the anisotropy of the current second moment matrix, we propose a coarse-to-fine affine shape adaptation scheme which is able to adjust the pace of convergence and enable the process to converge smoothly. The feature matching experiments demonstrate that the proposed approach obtains an improvement in convergence ratio and repeatability compared with the current schemes with relatively fixed integration kernel.
Keywords
Local image feature; affine invariant feature; scale invariant feature; adaptive kernel shape;
Citations & Related Records

Times Cited By Web Of Science : 0  (Related Records In Web of Science)
Times Cited By SCOPUS : 0
연도 인용수 순위
  • Reference
1 T. Lindeberg and J. Garding, "Shape-adapted smoothing in estimation of 3-D shape cues from affine deformations of local 2-D brightness structure," Image and Vision Computing, vol. 15, no. 6, pp. 415-434, 1997.   DOI   ScienceOn
2 A. Baumberg, "Reliable feature matching across widely separated views," in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, Hilton Head Island, South Carolina, USA, pp. 774-781, 2000.
3 T. Tuytelaars and K. Mikolajczyk. "Local invariant feature detectors: A survey," Foundations and Trends in Computer Graphics and Vision, vol. 3, no. 3, pp. 177-280, 2008.
4 T. Lindeberg, "Feature detection with automatic scale selection," International Journal of Computer Vision, vol. 30, no. 2, pp. 79-116, 1998.   DOI   ScienceOn
5 D.G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, 2004.   DOI
6 H. Bay, T. Tuytelaars and L.V. Gool, "SURF: speeded up robust features," in Proc. of European Conf. on Computer Vision, pp.404-417, 2006.
7 G. Dorkó and C. Schmid, "Maximally stable local description for scale selection," in Proc. of European Conference on Computer Vision, 2006.
8 Wei-Ting Lee and Hwann-Tzong Chen, "Histogram-based interest point detectors, " in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, 2009.
9 Wolfgang F¨orstner, Timo Dickscheid and Falko Schindler, "Detecting interpretable and accurate scale-invariant keypoints," in Proc. of the IEEE Conf. on Computer vision and Pattern Recognition, Kyoto, Japan, 2009.
10 Wolfgang Förstner, "A framework for low level feature extraction," in Proc. of European conf. on computer vision, Stockholm, Sweden, vol. 3, pp. 383-394, 1994.
11 J. Matas, O. Chum and M. Urban, and T. Pajdla, "Robust wide baseline stereo from maximally stable extremal regions," IVC, vol. 22, no. 10, pp. 761-767, 2004.   DOI
12 T. Tuytelaars and L.V. Gool, "Matching widely separated views based on affine invariant regions," International Journal of Computer Vision, vol. 59, no. 1, pp. 61-85, 2004.   DOI
13 F. Schaffalitzky and A. Zisserman, "Multi-view matching for unordered image sets," in Proc. of European Conference on Computer Vision, pp. 414-431, 2002.
14 C. Harris and M. Stephens, "A combined corner and edge detector," in Proc. of Alvey Vision Conf., pp. 189-192, 1988.
15 C. Schmid, R. Mohr and C. Bauckhage, "Evaluation of interest point detectors," International Journal of Computer Vision, vol. 37, no. 2, pp. 151-172, 2000.   DOI   ScienceOn
16 K. Mikolajczyk and C. Schmid, "Scale & affine invariant interest point detectors," International Journal of Computer Vision, vol. 60, no. 1, pp. 63-86, 2004.   DOI
17 Timor Kadir, Andrew Zisserman and Michael Brady, "An affine invariant salient region detector," in Proc. of European Conference on Computer Vision, pp.228-241, 2004.
18 K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir and L.V. Gool, "A comparison of affine region detectors," International Journal of Computer Vision, vol. 65, no. 1/2, pp. 43-72, 2005.   DOI   ScienceOn
19 http://www.robots.ox.ac.uk/vgg/research/affine/