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Fast and Robust Object Recognition Based on Reduced Shock-Graphs and Pruned-SIFT

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Advances in Soft Computing and Its Applications (MICAI 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8266))

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Abstract

Fast algorithms for image recogition have become a priority when the number of images to be analyzed can be counted in the number of millions. Therefore, the need for fast algorithms for image recognition. This paper describes an algorithm capable of recognize an object in an image by the fusing information from a reduced version of the Shock-Graph algorithm and the Scale Invariant Feature Transform (SIFT) points. The proposed algorithm uses the reduced Shock-Graph to obtain a skeleton of an object in order to minimize the number of SIFT points to reduce the computational complexity of object image comparison. The proposed algorithm is capable of recognizing objects in a fast way under rotation, deformation and scaling. Using a collection of shapes, we demonstrate the performance of our implementation using a combination of the reduced Shock-Graph and SIFT points.

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References

  1. Dean, T., Ruzon, M., Segal, M., Shlens, J., Vijayanarasimhan, S., Yagnik, J.: Fast, accurate detection of 100,000 object classes on a single machine. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, USA (2013)

    Google Scholar 

  2. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60, 91–110 (2004)

    Article  Google Scholar 

  3. Leonardis, A., Bischof, H.: Robust recognition using eigenimages. Comput. Vis. Image Underst. 78, 99–118 (2000)

    Article  Google Scholar 

  4. Murase, H., Nayar, S.: Illumination planning for object recognition using parametric eigenspaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 16, 1219–1227 (1994)

    Article  Google Scholar 

  5. Siddiqi, K., Shokoufandeh, A., Dickenson, S., Zucker, S.: Shock graphs and shape matching. In: Sixth International Conference on Computer Vision, pp. 222–229 (1998)

    Google Scholar 

  6. Siddiqi, K., Shokoufandeh, A., Dickenson, S., Zucker, S.: Shock graphs and shape matching. In: Sixth International Conference on Computer Vision, pp. 222–229 (1998)

    Google Scholar 

  7. Huttenlocher, D.P., Klanderman, G.A., Kl, G.A., Rucklidge, W.J.: Comparing images using the hausdorff distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 15, 850–863 (1993)

    Article  Google Scholar 

  8. Cour, T., Shi, J.: Recognizing objects by piecing together the segmentation puzzle. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8 (2007)

    Google Scholar 

  9. Macrini, D., Dickinson, S., Shokoufandeh, A., Siddiqi, K., Zucker, S., Trokhimtchouk, M., Phillips, C., Dimitrov, P.: View-based 3-d object recognition using shock graphs. In: Proceedings of the Internal Conference on Pattern Recognition, pp. 24–28 (2002)

    Google Scholar 

  10. Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 1124–1137 (2004)

    Article  Google Scholar 

  11. Blum, H.: A Transformation for Extracting New Descriptors of Shape. Models for the Perception of Speech and Visual Form, 362–380 (1967)

    Google Scholar 

  12. Liu, L., Chambers, E., Letscher, D., Ju, T.: Extended Grassfire Transform on Medial Axes of 2D Shapes, Washington, WA, USA (2011)

    Google Scholar 

  13. Shokoufandeh, A., Dickinson, S.: Graph-theoretical methods in computer vision. In: Khosrovshahi, G.B., Shokoufandeh, A., Shokrollahi, M.A. (eds.) Theoretical Aspects of Computer Science. LNCS, vol. 2292, pp. 148–174. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  14. Achanta, R., Estrada, F., Wils, P., Süsstrunk, S.: Salient region detection and segmentation. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds.) ICVS 2008. LNCS, vol. 5008, pp. 66–75. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  15. Cyr, C., Kimia, B.: 3d object recognition using shape similiarity-based aspect graph. In: Proceedings of the Eighth IEEE International Conference on Computer Vision, ICCV 2001, vol. 1, pp. 254–261 (2001)

    Google Scholar 

  16. Cyr, C.M., Kimia, B.B.: A similarity-based aspect-graph approach to 3d object recognition. International Journal of Computer Vision 57, 5–22 (2004)

    Article  Google Scholar 

  17. Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman & Co., New York (1979)

    Google Scholar 

  18. Treves, R., Rolls, E.T.: Computational analysis of the role of the hippocampus in memory. Hippocampus 4, 374–391 (1994)

    Article  Google Scholar 

  19. Carvalho, H., Heinzelman, W., Murphy, A., Coelho, C.: A general data fusion architecture. Proceedings of the Sixth International Conference of Information Fusion 2, 1465–1472 (2003)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Computer Sciences, Cinvestav, Guadalajara, Jalisco, Mexico

    Rafael Orozco-Lopez & Andres Mendez-Vazquez

Authors
  1. Rafael Orozco-Lopez
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  2. Andres Mendez-Vazquez
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Editor information

Editors and Affiliations

  1. Universidad Autónoma del Estado de Hidalgo, Ciudad Universitaria, Carretera Pachuca–Tulancingo km 4.5, Hidalgo, Mexico

    Félix Castro

  2. Centro de Investigación en Computación, Instituto Politécnico Nacional, Av. Juan Dios Bátiz s/n, Col. Nueva Industrial Vallejo, 07738, Mexico City, Mexico

    Alexander Gelbukh

  3. Tecnológico de Monterrey, Campus Estado de México, Carretera Lago de Guadalupe Km 3.5, Atizapán de Zaragoza, 52926, Estado de México, CP, Mexico

    Miguel González

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Orozco-Lopez, R., Mendez-Vazquez, A. (2013). Fast and Robust Object Recognition Based on Reduced Shock-Graphs and Pruned-SIFT. In: Castro, F., Gelbukh, A., González, M. (eds) Advances in Soft Computing and Its Applications. MICAI 2013. Lecture Notes in Computer Science(), vol 8266. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45111-9_33

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  • DOI: https://doi.org/10.1007/978-3-642-45111-9_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-45110-2

  • Online ISBN: 978-3-642-45111-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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