Journal of the Institute of Electronics Engineers of Korea SP (대한전자공학회논문지SP)

The Institute of Electronics and Information Engineers (대한전자공학회)
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Extended SURF Algorithm with Color Invariant Feature and Global Feature

컬러 불변 특징과 광역 특징을 갖는 확장 SURF(Speeded Up Robust Features) 알고리즘

  • 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)
  • Published : 2009.11.25
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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.

대응점 정합은 컴퓨터 비전에서 중요한 작업 중에 하나지만 스케일, 조명, 시점이 변한 환경에서 대응점을 찾는 과정은 매우 어렵다. 대응점 정합 알고리즘인 SURF(Speeded Up Robust Features) 기법은 SIFT(Scale Invariant Feature Transform) 기법에 비해 정합 속도가 매우 빠르고 비슷한 정합 성능을 보여 널리 사용되고 있다. 하지만 SURF 기법은 흑백 영상과 지역 공간정보를 사용하기 때문에 유사한 패턴이 존재하는 영상에서 대응점의 정합 성능이 매우 떨어진다. 이런 문제점을 해결하기 위해 본 논문에서는 강인한 컬러 특징 정보와 광역적 특징 정보를 이용하는 확장 SURF 알고리즘을 제안한다. 제안하는 알고리즘은 비슷한 패턴이 존재하더라도 색상정보과 광역 공간 정보를 추가로 사용되기 때문에 대응점 매칭 성능을 크게 향상시킨다. 본 논문에서는 제안하는 방법의 우수성을 조명과 시점이 변화하고 유사한 패턴들을 갖는 영상들에 적용하여 기존의 방법들과 비교 실험함으로서 입증하였다.

Keywords

References

  1. 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 https://doi.org/10.1016/j.cviu.2008.07.003
  2. 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 https://doi.org/10.1145/1386352.1386376
  3. 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 https://doi.org/10.1109/TPAMI.2005.188
  4. D. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," Int'l J. Computer Vision ,Vol. 60, no. 2, pp. 91-110, 2004 https://doi.org/10.1023/B:VISI.0000029664.99615.94
  5. H. Bay, T. Tuytelaars, and L. V. Gool, "Surf: Speeded up robust features," European Conference on Computer Vision, Vol. 3951, pp. 404-417, 2006 https://doi.org/10.1007/11744023_32
  6. T. Gevers, A. W. M. Smeulders, "Color based object recognition", Pattern Recognition, Vol. 32, no. 3, pp. 453-464, 1999 https://doi.org/10.1016/S0031-3203(98)00036-3
  7. C. Harris and M. Stephens, "A Combined Corner and Edge Detector," Proc. Alvey Vision Conf., pp. 147-151, 1988
  8. T. Lindeberg, "Feature detection with automatic scale selection," International Journal of Computer Vision, Vol. 30, no. 3, pp. 79-116, 1998 https://doi.org/10.1023/A:1008045108935
  9. K. Mikolajczyk and C. Schmid, "Indexing based on scale invariant interest points," International Conference Computer Vision, Vol. 1 pp. 525-531, 2001
  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. 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
  12. J. Weijer and C. Schmid, "Coloring local feature extraction," European Conference on Computer Vision, Vol. 3952, pp. 334-348, 2006 https://doi.org/10.1007/11744047_26
  13. 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
  14. 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 https://doi.org/10.1109/CVPR.2005.45