Abstract
A complete and practical system for object recognition with occlusion has been developed which is very robust with respect to noise and local deformations of shape as well as scale changes and rigid motions of the objects. The system has been tested on a wide variety of 3-D objects with different shapes and surface properties. No restrictive assumptions have been made about the shapes of admissible objects. An industrial application with a controlled environment is envisaged. The Curvature Scale Space technique [4, 5] is used to obtain a novel multi-scale segmentation of the image contour and the model contours using curvature zero-crossing points. Multi-scale segmentation renders the system substantially more robust with respect to noise and local shape differences. Object indexing [9] is used to narrow down the search-space and avoid an exhaustive investigation of all model segments. A local matching algorithm applies candidate generation, selection, merging, extension and grouping to select the best matching models.
Chapter PDF
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
M. Kass, A. Witkin, and D. Terzopoulos. Snakes: active contour models. In Proc International Conference on Computer Vision, pages 259–268, 1987.
F. Mokhtarian. Fingerprint theorems for curvature and torsion zero-crossings. In Proc IEEE Conference on Computer Vision and Pattern Recognition, pages 269–275, San Diego, CA, 1989.
F. Mokhtarian. Silhouette-based isolated object recognition through curvature scale space. IEEE Trans Pattern Analysis and Machine Intelligence, 17(5), 1995.
F. Mokhtarian and A. K. Mackworth. Scale-based description and recognition of planar curves and two-dimensional shapes. IEEE Trans Pattern Analysis and Machine Intelligence, 8(1):34–43, 1986.
F. Mokhtarian and A. K. Mackworth. A theory of multi-scale, curvature-based shape representation for planar curves. IEEE Trans Pattern Analysis and Machine Intelligence, 14(8):789–805, 1992.
F. Mokhtarian and H. Murase. Silhouette-based object recognition through curvature scale space. In Proc International Conference on Computer Vision, pages 269–274, Berlin, 1993.
F. Mokhtarian and S. Naito. Scale properties of curvature and torsion zero-crossings. In Proc Asian Conference on Computer Vision, pages 303–308, Osaka, Japan, 1993.
C. A. Rothwell, D. A. Forsyth, A. Zisserman, and J. L. Mundy. Extracting projective structure from single perspective views of 3d point sets. In Proc International Conference on Computer Vision, Berlin, 1993.
F. Stein and G. Medioni. Structural indexing: Efficient 3-d object recognition. IEEE Trans Pattern Analysis and Machine Intelligence, 14:125–145, 1992.
A. P. Witkin. Scale space filtering. In Proc International Joint Conference on Artificial Intelligence, pages 1019–1023, Karlsruhe, Germany, 1983.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1996 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mokhtarian, F. (1996). Silhouette-based object recognition with occlusion through curvature scale space. In: Buxton, B., Cipolla, R. (eds) Computer Vision — ECCV '96. ECCV 1996. Lecture Notes in Computer Science, vol 1064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0015567
Download citation
DOI: https://doi.org/10.1007/BFb0015567
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-61122-6
Online ISBN: 978-3-540-49949-7
eBook Packages: Springer Book Archive