Boundary extraction using statistical shape descriptor

Boundary extraction using statistical shape descriptor

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
Electronics Letters — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

An algorithm is proposed for extracting an object boundary from a low-quality image obtained by infrared sensors. With the training data set, the global shape is modelled by incorporating the statistical curvature model into the point distribution model (PDM). Simulation results show better performance than the PDM in the sense of computation speed and distortion under noise, pose variation and some kinds of occlusions.


    1. 1)
      • Y. Wang , L.H. Staib . Boundary finding with prior-shape and smoothness methods. IEEE Trans. Pattern Anal. Mach. Intell. , 7 , 738 - 743
    2. 2)
      • J.F. Canny . A computational approach to edge-detection. IEEE Trans. Pattern Anal. Mach. Intell. , 6 , 679 - 698
    3. 3)
      • Mokhtarian, F., Suomela, R.: `Curvature scale space for image point feature detection', Proc. 7th Int. Conf. on Image Processing, 1999, Manchester, UK, 1, p. 206–210.
    4. 4)
      • M. Kass , A. Witkin , D. Terzopoulos . Snakes active contour model. Int. J. Comput. Vis. , 321 - 331

Related content

This is a required field
Please enter a valid email address