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JRM Vol.21 No.6 pp. 672-679
doi: 10.20965/jrm.2009.p0672
(2009)

Paper:

3D Object Modeling and Segmentation Using Image Edge Points in Cluttered Environments

Masahiro Tomono

Future Robotics Technology Center, Chiba Institute of Technology

Received:
May 31, 2009
Accepted:
September 30, 2009
Published:
December 20, 2009
Keywords:
object segmentation, object modeling, object recognition, map building
Abstract

Object models are indispensable for robots to recognize objects when conducting tasks. This paper proposes a method of creating object models from images captured in real environments using a monocular camera. In our framework, an object model consists of a 3D model composed of 3D points reconstructed from image edge points and 2D models composed of image edge points, each having a SIFT descriptor for object recognition. To address the difficulty in creating object models of separating objects from background clutter, we separate the object of interest by finding edge points which cooccur in images with different backgrounds. We employ supervised and unsupervised schemes to provide training images for segmentation. Experimental results demonstrated that detailed 3D object models are successfully separated and created.

Cite this article as:
Masahiro Tomono, “3D Object Modeling and Segmentation Using Image Edge Points in Cluttered Environments,” J. Robot. Mechatron., Vol.21, No.6, pp. 672-679, 2009.
Data files:
References
  1. [1] D. G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” Int. J. of Computer Vision, 60(2), pp. 91-110, 2004.
  2. [2] F. Rothganger, S. Lazebnik, C. Schmid, and J. Ponce, “3D Object Modeling and Recognition Using Affine-Invariant Patches and Multi-View Spatial Constraints,” Proc. of CVPR2003, 2003.
  3. [3] I. Skrypnyk and D. G. Lowe, “Scene Modelling, Recognition and Tracking with Invariant Image Features,” Proc. of ISMAR2004, 2004.
  4. [4] M. Tomono, “3-D Object Map Building Using Dense Object Models with SIFT-based Recognition Features,” Proc. of IROS2006, 2006.
  5. [5] H. I. Christensen, N. O. Kirkeby, S. Kristensen, L. Knudsen, and E. Granum, “Model-driven vision for in-door navigation,” Robotics and Autonomous Systems, 12, pp. 199-207, 1994.
  6. [6] A. Hauck and N. O. Stöffler, “A Hierarchic World Model Supporting Video-Based Localization, Exploration and Object Identification,” Proc. of ACCV’95, pp. III-176-III-180, Dec. 1995.
  7. [7] A. Kosaka and J. Pan, “Purdue Experiments in Model-Based Vision for Hallway Navigation,” Proc. of Workshop on Vision for Robots in IROS’95, pp. 87-96, 1995.
  8. [8] T. Tsubouchi and S. Yuta, “Map Assisted Vision System of Mobile Robots for Reckoning in a Building Environment,” Proc. of ICRA’87, pp. 1978-1984, 1987.
  9. [9] Z. Lin, S. Kim, and I. Kweon, “Recognition-based Indoor Topological Navigation Using Robust Invariant Features,” Proc. of IROS2005, 2005.
  10. [10] Y. Hirano, K. Kitahama, and S. Yoshizawa, “Image-based Object Recognition and Dexterous Hand/Arm Motion Planning Using RRTs for Grasping in Cluttered Scene,” Proc. of IROS2005, 2005.
  11. [11] K. Mikolajczyk and C. Schmid, “An affine invariant interest point detector,” Proc. of ECCV2002, 2002.
  12. [12] K. Mikolajczyk, A. Zisserman, and C. Schmid, “Shape recognition with edge-based features,” Proc. of BMVC2003, 2003.
  13. [13] S. Lazebnik, C. Schmid, and J. Ponce, “Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories,” Proc. of CVPR2006, 2006.
  14. [14] M. Marszalek and C Schmid, “Spatial Weighting for Bag-of-Features,” Proc. of CVPR2006, 2006.
  15. [15] D. Parikh and T. Chen, “Unsupervised Identification of Multiple Objects of Interest from Multiple Images: dISCOVER,” Proc. of ACCV2007, 2007.
  16. [16] B. C. Russell, A. A. Efros, J. Sivic, W. T. Freeman, and A. Zisserman, “Using Multiple Segmentations to Discover Objects and their Extent in Image Collections,” Proc. of CVPR2006, 2006.
  17. [17] J. Sivic, B. C. Russell, A. A. Efros, A. Zisserman, and W. T. Freeman, “Discovering objects and their location in images,” Proc. of ICCV2005, 2005.
  18. [18] J. Shi and C. Tomasi, “Good Features to Track,” Proc. of CVPR’94, pp. 593-600, 1994.
  19. [19] C. Tomasi and T. Kanade, “Shape and Motion from Image Streams under Orthography: A Factorization Approach,” Int. J. of Computer Vision, 9(2), pp. 137-154, 1992.
  20. [20] R. Hartley and A. Zisserman, “Multiple View Geometry in Computer Vision,” Cambridge University Press, 2000.
  21. [21] J. Canny, “A Computational Approach to Edge Detection,” IEEE Trans. PAMI, Vol.8, No.6, pp. 679-698, 1986.
  22. [22] M. Tomono, “Dense Object Modeling for 3-D Map Building Using Segment-based Surface Interpolation,” Proc. of ICRA2006, 2006.
  23. [23] T. Lindberg, “Feature Detection with Automatic Scale Selection,” Int. J. of Computer Vision, 30(2), pp. 79-116, 1998.
  24. [24] J. S. Beis and D. G. Lowe, “Shape Indexing Using Approximate Nearest-Neighbour Search in High-Dimensional Spaces,” Proc. of CVPR, 1997.
  25. [25] M. Fischler and R. Bolles, “Random Sample Consensus: a Paradigm for Model Fitting with Application to Image Analysis and Automated Cartography,” Communications ACM, 24, pp. 381-395, 1981.

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