Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Shin, Dongjoe; Tjahjadi, Tardi (2010)
Publisher: Pergamon
Languages: English
Types: Article
Subjects: TK, QA76

Classified by OpenAIRE into

arxiv: Computer Science::Computer Vision and Pattern Recognition
Assuming that the image distortion between corresponding regions of a stereo pair of images with wide baseline can be approximated as an affine transformation if the regions are reasonably small, recent image matching algorithms have focused on affine invariant region (IR) detection and its description to increase the robustness in matching. However, the distinctiveness of an intensity-based region descriptor tends to deteriorate when an image includes homogeneous texture or repetitive pattern. To address this problem, we investigated the geometry of a local IR cluster (also called a clique) and propose a new clique-based image matching method. In the proposed method, the clique of an IR is estimated by Delaunay triangulation in a local affine frame and the Hausdorff distance is adopted for matching an inexact number of multiple descriptor vectors. We also introduce two adaptively weighted clique distances, where the neighbour distance in a clique is appropriately weighted according to characteristics of the local feature distribution. Experimental results show the clique-based matching method produces more tentative correspondences than variants of the SIFT-based method.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] T. Tuytelaars, L. J. V. Gool, Wide baseline stereo matching based on local, affinely invariant regions, in: Proc. Brit. Machine Vision Conf., 2000, pp. 42-56.
    • [2] D. G. Lowe, Distinctive image features from scale-invariant keypoints, Int. J. Compt. Vis. 60 (2) (2004) 91-110.
    • [3] K. Mikolajczyk, C. Schmid, A performance evaluation of local descriptors, IEEE Trans. Pattern Anal. Machine Intell. 27 (10) (2005) 1615-1630.
    • [4] K. Mikolajczyk, C. Schmid, Scale and affine invariant interest point detectors, Int. J. Compt. Vis. 60 (1) (2004) 63-86.
    • [5] T. Tuytelaars, L. J. V. Gool, Matching widely separated views based on affine invariant regions, Int. J. Compt. Vis. 59 (1) (2004) 61-85.
    • [6] J. Matas, O. Chum, M. Urban, T. Pajdla, Robust wide baseline stereo from maximally stable extremal regions, Image Vision Compt. 22 (10) (2004) 761-767.
    • [7] P. E. Forssen, Maximally stable colour regions for recognition and matching, in: Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2007, pp. 1-8.
    • [8] P. Meer, D. Mintz, D. Y. Kim, A. Rosenfeld, Robust regression methods for computer vision: A review, Int. J. Compt. Vis. 6 (1) (1991) 59-70.
    • [9] R. Hartley, A. Zisserman, Multiple view geometry, 1st Edition, Cambridge, 2000.
    • [10] F. Schaffalitzky, A. Zisserman, Viewpoint invariant texture matching and wide baseline stereo, in: Proc. IEEE Int. Conf. Computer Vision, 2001, pp. 636-643.
    • [11] P. E. Forssen, D. G. Lowe, Shape descriptors for maximally stable extremal regions, in: Proc. IEEE Int. Conf. Computer Vision, 2007, pp. 1-8.
    • [12] O. Chum, J. Matas, Geometric hashing with local affine frames, in: Proc. IEEE Conf. Computer Vision and Pattern Recognition, Vol. 1, 2006, pp. 879-884.
    • [13] Affine covariant features, Available: http://www.robots.ox.ac.uk/∼vgg/research/affine/index.html#publications [accessed on 3 Nov 2009].
    • [14] F. Aurenhammer, Voronoi diagrams: A survey of a fundamental geometric data structure, ACM Comput. Surv. 23 (3) (1991) 345-405.
    • [15] A. M. Finch, R. C. Wilson, E. R. Hancock, Matching delaunay graphs, Pattern Recognit. 30 (1) (1997) 123-140.
    • [16] D. P. Huttenlocher, G. A. Klanderman, W. J. Rucklidge, Comparing images using the hausdorff distance, IEEE Trans. Pattern Anal. Machine Intell. 15 (9) (1993) 850-863.
    • [17] M. P. D. Jolly, A. K. Jain, A modified Hausdorff distance for object matching, in: Proc. Int. Conf. Pattern Recognition, 1994, pp. A:566-568.
    • [18] J. M. Geusebroek, G. J. Burghouts, and A. W. M. Smeulders, The Amsterdam library of object images, Int. J. Comput. Vis., 61 (1) (2005) 103-112.
    • [19] D. Sheskin, Handbook of parametric and nonparametric statistical procedures, Third Edition, Chapman & Hall/CRC, 2004, pp. 189-202.
  • No related research data.
  • Discovered through pilot similarity algorithms. Send us your feedback.

Share - Bookmark

Cite this article