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A Scale and Rotation Invariant Interest Points Detector Based on Gabor Filters

  • Conference paper
Signal Processing, Image Processing and Pattern Recognition (SIP 2009)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 61))

Abstract

This paper presents a new method for detecting scale and rotation invariant interest points. The method is based on a representation of the image that involves both spatial and spatial-frequency variables in its description. The method is based on two main conclusions: 1) Interest points can be extracted based on the local maxima of the normalized local energy maps. 2) Local extrema over scale of a new established Gabor scale-space indicate the presence of characteristic local structures. Our method first extract interest points at multi-scales from the local energy map constructed by Gabor filter responses, and then select points at which a local measure is maximal over scales. This allows a selection of distinctive points for which the characteristic scale is known. The interest points are invariant to scale and rotation and give repeatable results (geometric stable). Comparative evaluation using the repeatability criteria shows the good performance of our approach.

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References

  1. Ohbuchi, R., Osada, K., Furuya, T., Banno, T., et al.: Salient Local Visual Features for Shape-Based 3d Model Retrieval. In: IEEE International Conference on Shape Modeling and Applications, Stony Brook, NY, pp. 93–102 (2008)

    Google Scholar 

  2. Snavely, N., Seitz, S.M., Szeliski, R.: Photo Tourism: Exploring Photo Collections in 3d, pp. 835–846. ACM, New York (2006)

    Google Scholar 

  3. Mikolajczyk, K., Schmid, C.: Indexing Based On Scale Invariant Interest Points. In: Eighth IEEE International Conference on Computer Vision, Vancouver, Canada, pp. 525–531 (2001)

    Google Scholar 

  4. Nister, D., Stewenius, H.: Scalable Recognition with a Vocabulary Tree. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2161–2168. IEEE Computer Society, Los Alamitos (2006)

    Google Scholar 

  5. Deselaers, T., Keysers, D., Ney, H.: Features for Image Retrieval: An Experimental Comparison. Information Retrieval 11, 77–107 (2008)

    Article  Google Scholar 

  6. Lowe, D.G.: Distinctive Image Features From Scale-Invariant Keypoints. International Journal of Computer Vision 60, 91–110 (2004)

    Article  Google Scholar 

  7. Lindeberg, T.: Feature Detection with Automatic Scale Selection. International Journal of Computer Vision 30, 79–116 (1998)

    Article  Google Scholar 

  8. Tuytelaars, T., Van, G.L.: Matching Widely Separated Views Based On Affine Invariant Regions. International Journal of Computer Vision 59, 61–85 (2004)

    Article  Google Scholar 

  9. Brown, M., Lowe, D.G.: Invariant Features From Interest Point Groups. In: British Machine Vision Conference, Cardiff, Wales, pp. 656–665 (2002)

    Google Scholar 

  10. Mikolajczyk, K., Schmid, C.: Scale & Affine Invariant Interest Point Detectors. International Journal of Computer Vision 60, 63–86 (2004)

    Article  Google Scholar 

  11. Schmid, C., Mohr, R.: Local Grayvalue Invariants for Image Retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 530–535 (1997)

    Article  Google Scholar 

  12. Sebe, N., Lew, M.S.: Comparing Salient Point Detectors. Pattern recognition letters 24, 89–96 (2003)

    Article  MATH  Google Scholar 

  13. Schmid, C., Mohr, R., Bauckhage, C.: Evaluation of Interest Point Detectors. International Journal of computer vision 37, 151–172 (2000)

    Article  MATH  Google Scholar 

  14. Lowe, D.G.: Object Recognition From Local Scale-Invariant Features. In: Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece, pp. 1150–1157 (1999)

    Google Scholar 

  15. Bay, H., Ess, A., Tuytelaars, T., Van, G.L., et al.: Speeded-Up Robust Features (Surf). Computer Vision and Image Understanding 110, 346–359 (2008)

    Article  Google Scholar 

  16. Grabner, M., Grabner, H., Bischof, H.: Fast Approximated Sift. In: 7th Asian Conference of Computer Vision, Hyderabad, India, pp. 918–927 (2006)

    Google Scholar 

  17. Lee, T.S.: Image Representation Using 2d Gabor Wavelets. IEEE Transaction on pattern analysis and machine intelligence 18, 959–971 (1996)

    Article  Google Scholar 

  18. Wuertz, R.P., Lourens, T.: Corner Detection in Color Images through a Multiscale Combination of End-Stopped Cortical Cells. Image and Vision Computing 18, 531–541 (2000)

    Article  Google Scholar 

  19. Fdez-Vidal, X.R., Garc, J.A., Fdez-Valdivia, J.: Using Models of Feature Perception in Distortion Measure Guidance. Pattern Recognition Letters 19, 77–88 (1998)

    Article  Google Scholar 

  20. Kovesi, P.: Image Features From Phase Congruency. Videre: Journal of Computer Vision Research 1, 1–26 (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Mechatronics Engineering and Automation, National University of Defence Technology, Changsha, China

    Wanying Xu, Xinsheng Huang, Yongbin Zheng & Yuzhuang Yan

  2. College of Computer, National University of Defence Technology, Changsha, China

    Wei Zhang

Authors
  1. Wanying Xu
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  2. Xinsheng Huang
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  3. Yongbin Zheng
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  4. Yuzhuang Yan
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  5. Wei Zhang
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Editor information

Editors and Affiliations

  1. University of Warsaw and Infobright Inc., Poland

    Dominik Ślęzak

  2. Indian Statistical Institute, India

    Sankar K. Pal

  3. University of Tasmania, 7001, Hobart, Australia

    Byeong-Ho Kang

  4. Lab. of Multimedia Information Technology (MMIT), East China Normal University (ECNU), P.O. Box, 200062, Shanghai, China

    Junzhong Gu

  5. Nagasaki University, Japan

    Hideo Kuroda

  6. Hannam University, 306-791, Daejeon, South Korea

    Tai-hoon Kim

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© 2009 Springer-Verlag Berlin Heidelberg

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Xu, W., Huang, X., Zheng, Y., Yan, Y., Zhang, W. (2009). A Scale and Rotation Invariant Interest Points Detector Based on Gabor Filters. In: Ślęzak, D., Pal, S.K., Kang, BH., Gu, J., Kuroda, H., Kim, Th. (eds) Signal Processing, Image Processing and Pattern Recognition. SIP 2009. Communications in Computer and Information Science, vol 61. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10546-3_12

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  • DOI: https://doi.org/10.1007/978-3-642-10546-3_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10545-6

  • Online ISBN: 978-3-642-10546-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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