Contour-based Shape Recognition using Perceptual Turning Points

Loke Kar Seng

Abstract

This paper presents a new biological and psychologically motivated edge contour feature that could be used for shaped based object recognition. Our experiments indicate that this new feature perform as well or better than existing methods. This method have the advantage that computation is comparatively is simpler.

References

  1. F. Attneave, 1954. "Some informational aspects of visual perception," Psychological Review, vol. 61, pp. 183- 193.
  2. X. Bai, Q. Li, L. J. Latecki, and W. Liu, 2009. "Shape band: A deformable object detection approach," in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Miami, Florida, pp. 1335-1342.
  3. S. Belongie, J. Malik, and J. Puzhicha, 2002. Shape Matching and Object Recognition Using Shape Contexts," IEEE Transactions of Pattern Analysis and Machine Intelligence, vol. 24, pp. 509-522.
  4. J. Feldman and M. Singh, 2005. "Information Along Contours and Object Boundaries," psychological Review, vol. 112, pp. 263-252.
  5. V. Ferrari, F. Jurie, and C. Schmid, 2010. "From Images to Shape Models for Object Detection," International Journal in Computer Vision, vol. 87.
  6. D. D. Hoffman and M. Singh, 1997. "Salience of visual parts.," Cognition, vol. 63, pp. 29-78.
  7. K. Kpalma, M. Yang, and J. Ronsin, 2008. "Planar Shapes Descriptors Based on the Turning Angle Scalogram," in ICIAR 7808 Proceedings of the 5th international conference on Image Analysis and Recognition, pp. 547-556.
  8. A. Kristjansson and P. U. Tse, 2001. "Curvature discontinuities are cues for rapid shape analysis," Perception & Psychophysics, vol. 3, pp. 390-403.
  9. A. Opelt, A. Pinz, and A. Zisserman, 2006. "A BoundaryFragment Model for Object Detection.," in European Conference on Computer Vision, pp. 575-588.
  10. A. Pasupathy and C. E. Connor, 2001. "Shape representation in area V4: Position-specific tuning for boundary conformation," The Journal of Neurophysiology, vol. 86, pp. 2505-2519.
  11. M. Rusinol, P. Dosch, and J. Llados, 2007. "Boundary Shape Recognition Using Accumulated Length and Angle Information," Lecture Notes in Computer Science, vol. 4478, pp. 210-217.
  12. J. Shotton, A. Blake, and R. Cipolla, 2008. "Multi-Scale Categorical Object Recognition Using Contour Fragments," IEEE Transactions of Pattern Analysis and Machine Intelligence.
  13. J. D. Winter and J. Wagemans, 2008. "Perceptual saliency of points along the contour of everyday objects: A large-scale study," Perception & Psychophysics, vol. 1, pp. 50-64.
Download


Paper Citation


in Harvard Style

Seng L. (2013). Contour-based Shape Recognition using Perceptual Turning Points . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-47-1, pages 487-491. DOI: 10.5220/0004304804870491


in Bibtex Style

@conference{visapp13,
author={Loke Kar Seng},
title={Contour-based Shape Recognition using Perceptual Turning Points},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={487-491},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004304804870491},
isbn={978-989-8565-47-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)
TI - Contour-based Shape Recognition using Perceptual Turning Points
SN - 978-989-8565-47-1
AU - Seng L.
PY - 2013
SP - 487
EP - 491
DO - 10.5220/0004304804870491