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Coarse-to-Fine Statistical Shape Model by Bayesian Inference

  • Ran He
  • Stan Li
  • Zhen Lei
  • ShengCai Liao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4843)

Abstract

In this paper, we take a predefined geometry shape as a constraint for accurate shape alignment. A shape model is divided in two parts: fixed shape and active shape. The fixed shape is a user-predefined simple shape with only a few landmarks which can be easily and accurately located by machine or human. The active one is composed of many landmarks with complex shape contour. When searching an active shape, pose parameter is calculated by the fixed shape. Bayesian inference is introduced to make the whole shape more robust to local noise generated by the active shape, which leads to a compensation factor and a smooth factor for a coarse-to-fine shape search. This method provides a simple and stable means for online and offline shape analysis. Experiments on cheek and face contour demonstrate the effectiveness of our proposed approach.

Keywords

Active shape model Bayesian inference statistical image analysis segmentation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Ran He
    • 1
  • Stan Li
    • 1
  • Zhen Lei
    • 1
  • ShengCai Liao
    • 1
  1. 1.Institute of Automation, Chinese Academy of Sciences, BeijingChina

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