Skip to main content
Book cover

Visual Form pp 479–493Cite as

View-Class Representation and Matching of 3D Objects

  • Chapter

Abstract

Three-dimensional object recognition is difficult because an object looks different when viewed from different viewpoints. One solution to this problem is to represent the 3D object as a set of 2D models, one for each of a set of view classes. A view class is a set of viewpoints that all produce images with the same or similar features. View-class matching consists of determining the correspondence between the features extracted from an image of an unknown object and the features of a particular view class of a particular object model. View-class matching is used in object recognition, pose estimation, and inspection systems.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Buchanan, C. G., “Determining Surface Orientation from Specular Highlights”, M.S. thesis, Computer Science Department, University of Toronto, 1986.

    Google Scholar 

  2. Camps, O. I., L. G. Shapiro, and R. M. Haralick, “PREMIO: An Overview”, Proceedings of the IEEE Workshop on Automated CAD-Based Vision, June, 1991.

    Google Scholar 

  3. Chakravarty, I. and H. Freeman, “Characteristic Views as a Basis for Three-Dimensional Object Recognition”, Proceedings of SPIE 336 (Robot Vision), 1982, pp. 37-45.

    Google Scholar 

  4. Cook, R. L., and K. E. Torrance, “A reflectance Model for Computer Graphics”, ACM Transactions on Graphics, Vol. 1, 1982, pp. 7–24.

    Article  Google Scholar 

  5. Goad, C., “Special Purpose, Automatic Programming for 3D Model-Based Vision”, DARPA Image Understanding Workshop, 1983, pp. 94-104.

    Google Scholar 

  6. Hansen, C. and T. Henderson, “Toward the Automatic Generation of Recognition Strategies”, Proceedings of the 2nd International Conference on Computer Vision, 1988, pp. 275-279.

    Google Scholar 

  7. Haralick, R. M. and L. G. Shapiro, “The Consistent Labeling Problem: Part I”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 1, No. 2, 1979, pp. 173–184.

    Article  MATH  Google Scholar 

  8. Henikoff, J. and L. G. Shapiro, “Interesting Patterns for Model-Based Vision”, Proceedings of the 3rd International Conference on Computer Vison, 1990, pp. 535-538.

    Google Scholar 

  9. Ikeuchi, K., “Generating an Interpretation Tree from a CAD Model for 3D-Object Recognition in Bin-Picking Tasks”, International Journal of Computer Vision, Vol. 2, 1987, pp. 145–165.

    Article  Google Scholar 

  10. Lowe, D. G., “Three-Dimensional Object Recognition from Single Two-Dimensional Images”, Artificial Intelligence, Vol. 31, 1987, pp. 355–395.

    Article  Google Scholar 

  11. Mohan, R. and R. Nevatia, “Using Perceptual Organization to Extract 3D Structures”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 11, No. 11, 1989, pp. 1121–1139.

    Article  Google Scholar 

  12. Ponce, J. and D. Chelberg, “Finding the limbs and Cusps of Generalized Cylinders”, International Journal of Computer Vision, Vol. 3, 1987, pp. 195–210.

    Google Scholar 

  13. Shapiro, L. G. and H. Lu, “Accumulator-Based Inexact Matching Using Relational Summaries”, Machine Vision and Applications, Vol 3, 1990, pp. 143–158.

    Article  Google Scholar 

  14. Yi, S., R. M. Haralick, and L. G. Shapiro, “Automatic Sensor and Light Source Positioning for Machine Vision”, Proceedings of the 10th International Conference on Pattern Recognition, June, 1990, pp. 55-59.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1992 Springer Science+Business Media New York

About this chapter

Cite this chapter

Shapiro, L.G. (1992). View-Class Representation and Matching of 3D Objects. In: Arcelli, C., Cordella, L.P., di Baja, G.S. (eds) Visual Form. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-0715-8_46

Download citation

  • DOI: https://doi.org/10.1007/978-1-4899-0715-8_46

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4899-0717-2

  • Online ISBN: 978-1-4899-0715-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics