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Extended SURF Algorithm with Color Invariant Feature and Global Feature  

Yoon, Hyun-Sup (Department of Electrical Engineering, Soongsil University)
Han, Young-Joon (Department of Electrical Engineering, Soongsil University)
Hahn, Hern-Soo (Department of Electrical Engineering, Soongsil University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.46, no.6, 2009 , pp. 58-67 More about this Journal
Abstract
A correspondence matching is one of the important tasks in computer vision, and it is not easy to find corresponding points in variable environment where a scale, rotation, view point and illumination are changed. A SURF(Speeded Up Robust Features) algorithm have been widely used to solve the problem of the correspondence matching because it is faster than SIFT(Scale Invariant Feature Transform) with closely maintaining the matching performance. However, because SURF considers only gray image and local geometric information, it is difficult to match corresponding points on the image where similar local patterns are scattered. In order to solve this problem, this paper proposes an extended SURF algorithm that uses the invariant color and global geometric information. The proposed algorithm can improves the matching performance since the color information and global geometric information is used to discriminate similar patterns. In this paper, the superiority of the proposed algorithm is proved by experiments that it is compared with conventional methods on the image where an illumination and a view point are changed and similar patterns exist.
Keywords
Color Invariant Feature; Global Feature; SURF; SIFT; Correspondence point matching;
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1 K. van de Sande, T. Gevers, C. Snoek, "A comparison of color features for visual concept classification," Conference On Image And Video Retrieval, pp. 141-150, 2008   DOI
2 D. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," Int'l J. Computer Vision ,Vol. 60, no. 2, pp. 91-110, 2004   DOI
3 C. Harris and M. Stephens, "A Combined Corner and Edge Detector," Proc. Alvey Vision Conf., pp. 147-151, 1988
4 A. A. Farag and A. E. Abdel-Hakim. "Detection, categorization and recognition of road signs for autonomous navigation," Advanced Concepts for Intelligent Vision Systems, pp. 125-130, 2004
5 J. Weijer and C. Schmid, "Coloring local feature extraction," European Conference on Computer Vision, Vol. 3952, pp. 334-348, 2006   DOI
6 H. Bay, T. Tuytelaars, and L. V. Gool, "Surf: Speeded up robust features," European Conference on Computer Vision, Vol. 3951, pp. 404-417, 2006   DOI   ScienceOn
7 T. Gevers, A. W. M. Smeulders, "Color based object recognition", Pattern Recognition, Vol. 32, no. 3, pp. 453-464, 1999   DOI   ScienceOn
8 T. Lindeberg, "Feature detection with automatic scale selection," International Journal of Computer Vision, Vol. 30, no. 3, pp. 79-116, 1998   DOI
9 G. J. Burghouts, J. M. Geusebroek, "Performance evaluation of local colour invariants," Computer Vision and Image Understanding, Vol. 113, no. 1, pp. 48-62, 2009   DOI   ScienceOn
10 P. Viola, M. Jones, "Rapid Object Detection using a Boosted Cascade of Simple Features," Computer Vision and Pattern Recognition, Vol. 1, pp. 511-518, 2001
11 E. N. Mortensen, H. Deng, and L. Shapiro, "A SIFT Descriptor with Global Context," Computer Vision and Pattern Recognition, Vol. 1, pp. 184-190, 2005   DOI
12 K. Mikolajczyk and C. Schmid, "Indexing based on scale invariant interest points," International Conference Computer Vision, Vol. 1 pp. 525-531, 2001
13 A. E. Abdel-Hakim and A. A. Farag "CSIFT: A SIFT Descriptor with Color Invariant Characteristics," Computer Vision and Pattern Recognition, Vol. 2. pp. 1978-1983, 2006
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   DOI   ScienceOn