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Keypoints Derivation for Object Class Detection with SIFT Algorithm

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Artificial Intelligence and Soft Computing – ICAISC 2006 (ICAISC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4029))

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Abstract

The following paper proposes a procedure for SIFT keypoints derivation for the purpose of object class detection. The main idea of the method is to build appropriate object class keypoints by extracting information that corresponds to characteristic class features. The proposed procedure is composed of two main steps: clustering of similar SIFT keypoints and derivation of appropriate keypoint descriptors. Face detection in images has been selected as a sample application for the proposed approach performance evaluation.

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References

  1. Lowe, D.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)

    Article  Google Scholar 

  2. Harris, C., Stephens, M.: A combined corner and edge detector. In: Fourth Alvey Vision Conference, Manchester, UK, pp. 147–151 (1988)

    Google Scholar 

  3. Mikolajczyk, K., Zisserman, A., Schmid, C.: Shape recognition with edge-based features. In: British Machine Vision Conference, Norwich, U.K, vol. 2, pp. 779–788 (2003)

    Google Scholar 

  4. Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. In: Conference on Computer Vision and Pattern Recognition (2), Madison, Wisconsin, pp. 257–263 (2003)

    Google Scholar 

  5. Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. In: Conference on Computer Vision and Pattern Recognition, Madison, Wisconsin, pp. 264–271 (2003)

    Google Scholar 

  6. Helmer, S., Lowe, D.G.: Object recognition with many local features. In: Workshop on Generative Model Based Vision, Washington, D.C (2004)

    Google Scholar 

  7. Zhang, W., Yu, B., Zelinsky, G.J., Samaras, D.: Object Class Recognition Using Multiple Layer Boosting with Heterogeneous Features. In: International Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 323–330 (2005)

    Google Scholar 

  8. Kaufman, L., Rousseeuw, P.J.: Finding groups in data: An introduction to cluster analysis. John Wiley, Chichester (1990)

    Google Scholar 

  9. The BioID Face Database, - http://www.bioid.com/downloads/facedb/

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Author information

Authors and Affiliations

  1. Lodz and Academy of Humanities and Economics, Institute of Electronics, Technical University of Lodz, Wolczanska 211/215, 90-924, Lodz, Poland

    Krzysztof Slot

  2. Division of Electronics and Information Engineering, Chonbuk National University, 561-756, Chonju, Republic of Korea

    Hyongsuk Kim

Authors
  1. Krzysztof Slot
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  2. Hyongsuk Kim
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Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Academy of Humanities and Economics, Poland

    Leszek Rutkowski

  2. Institute of Automatics, AGH University of Science and Technology, Al. Mickiewicza 30, PL-30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, Berkeley Initiative in Soft Computing (BISC), University of California, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Żurada

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© 2006 Springer-Verlag Berlin Heidelberg

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Slot, K., Kim, H. (2006). Keypoints Derivation for Object Class Detection with SIFT Algorithm. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2006. ICAISC 2006. Lecture Notes in Computer Science(), vol 4029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11785231_89

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  • DOI: https://doi.org/10.1007/11785231_89

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-35748-3

  • Online ISBN: 978-3-540-35750-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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