Boundary extraction using statistical shape descriptor

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Boundary extraction using statistical shape descriptor

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Published

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.

Inspec keywords: feature extraction; infrared imaging; statistical analysis

Other keywords: low-quality image; Bayesian objective function; global shape; occlusions; infrared sensors; pattern recognition; statistical shape descriptor; statistical curvature model; point distribution model; pose variation; object boundary extraction; training data set

Subjects: Other topics in statistics; Other topics in statistics; Image recognition; Image recognition

References

    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)
      • 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.
    3. 3)
      • M. Kass , A. Witkin , D. Terzopoulos . Snakes active contour model. Int. J. Comput. Vis. , 321 - 331
    4. 4)
      • J.F. Canny . A computational approach to edge-detection. IEEE Trans. Pattern Anal. Mach. Intell. , 6 , 679 - 698
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