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Asian Conference on Computer Vision

ACCV 2012: Computer Vision - ACCV 2012 Workshops pp 182–193Cite as

Region Segmentation and Object Extraction Based on Virtual Edge and Global Features

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7728))

Abstract

We have developed a robust statistical edge detection method by combining the ideas of Kundus method, in which the region segmentation of local area is used, and Fukuis method, in which a statistic evaluation value separability is used for edge extraction and also have developed a region segmentation method based on the global features like the statistics of the region. A new region segmentation method is developed by combining these two methods, in which the edge extraction method is used instead of the first step of region segmentation method. We obtained the almost same results as the ones of previous region segmentation method. The proposed one has some advantages because we are able to introduce a new conspicuity degree including a clear contrast value with the adjacent regions, a envelopment degree based on clear edge and so on without much difficulty and it will contribute to develop a further unification algorithm and a new feature extraction method for scene recognition.

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References

  1. Arbetaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and image segmentation resources. IEEE Trans. PAMI 33(5), 898–916 (2011)

    Article  Google Scholar 

  2. Canny, J.: A Computational approach to edge detection. IEEE Trans. PAMI PAMI-8(6), 679–698 (1986)

    Article  Google Scholar 

  3. Carson, C., Belongie, S., Greenspan, H., Malik, J.: Blobworld:Image segmentation using expectation-maximization and its application to image querying. IEEE Trans. PAMI 24(8), 1026–1038 (2002)

    Article  Google Scholar 

  4. Fukui, K.: Edge extraction method based on separability of image features. IEICE Trans. Inf. & Syst. E78-D(12), 1533–1538 (1995)

    Google Scholar 

  5. Fukui, K.: Contour extraction method based on separability of image features. Trans. IEICE J80-D-II(6), 1406–1414 (1997)

    Google Scholar 

  6. Hou, Z., Koh, T.S.: Robust edge detection. Pattern Recognition 36, 2083–2091 (2003)

    Article  MATH  Google Scholar 

  7. Hubel, D.H., Wiesel, T.N.: Receptive fields, binocular interaction and fundamental architecture in the cats visual cortex. J. Physiology 160, 106–154 (1962)

    Google Scholar 

  8. Hueckel, M.: An operator which locates edges in digitized pictures. J. ACM 18(1), 113–125 (1971)

    Article  MATH  Google Scholar 

  9. Kundu, A.: Robust edge detection. Pattern Recognition 23(5), 423–440 (1990)

    Article  MathSciNet  Google Scholar 

  10. Mairal, J., Leordeanu, M., Bach, F., Hebert, M., Ponce, J.: Discriminative Sparse Image Models for Class-Specific Edge Detection and Image Interpretation. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 43–56. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  11. Marr, D., Hildreth, E.: Theory of edge detection. Proc. Royal Soc. London B-207, 187–217 (1980)

    Article  Google Scholar 

  12. Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proc. 8th ICCV, vol. 2, pp. 416–423 (2001)

    Google Scholar 

  13. Mori, F., Yamada, H., Mizuno, M., Sugano, N.: Color image segmentation based on statistics of location and feature similarity. IEEJ Trans. Electronics, Information and Systems 131(11), 2022–2029 (2012)

    Article  Google Scholar 

  14. Mori, F., Yamada, H., Mizuno, M., Sugano, N.: Virtual edge extraction method based on new separability. IEICE Trans. on Information and System (Japanese Edition) J94-D(12), 2105–2113 (2012)

    Google Scholar 

  15. Otsu, N.: A threshold selection method from gray-level histogram. IEEE Trans. SMC SMC-9, 62 (1979)

    Google Scholar 

  16. Robinson, G.S.: Edge detection by compass gradient masks. Computer Graphics and Image Processing 6, 492–501 (1977)

    Article  Google Scholar 

  17. Smith, S.M., Brady, J.M.: SUSAN-a new approach to low level image processing. Proc. IJCV 23(1), 45–78 (1997)

    Article  Google Scholar 

  18. Sobel, I.: An isotropic 3x3 image gradient operater. In: Freeman, H. (ed.) Machine Vision for Three-Dimensional Scenes, pp. 376–379. Academic Press (1990)

    Google Scholar 

  19. Wakasugi, T., Nishiura, M., Yamaguchi, O., Fukui, K.: Lip contour extraction using separability of color distributions. Trans. IEICE J89-D(9), 2025–2032 (2006)

    Google Scholar 

  20. Yakimovsky, Y.: Boundary and object detection in real world images. J. ACM 23(4), 599–618 (1976)

    Article  MathSciNet  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Tamagawa University, Tokyo, Japan

    Fumihiko Mori

  2. Fuji Visual Science Laboratory, Kanagawa, Japan

    Terunori Mori

Authors
  1. Fumihiko Mori
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  2. Terunori Mori
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Editor information

Editors and Affiliations

  1. Computer Science and Engineering, Hanyang University, 222 Wangshimni-ro, Seongdong-gu, 133-791, Seoul, South Korea

    Jong-Il Park

  2. Department of Electrical Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, 305-701, Daejeon, South Korea

    Junmo Kim

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Mori, F., Mori, T. (2013). Region Segmentation and Object Extraction Based on Virtual Edge and Global Features. In: Park, JI., Kim, J. (eds) Computer Vision - ACCV 2012 Workshops. ACCV 2012. Lecture Notes in Computer Science, vol 7728. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37410-4_16

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  • DOI: https://doi.org/10.1007/978-3-642-37410-4_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37409-8

  • Online ISBN: 978-3-642-37410-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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