Probabilistic contour extraction using hierarchical shape representation

In this paper, we address the issue of extracting contour of the object with a specific shape. A hierarchical graphical model is proposed to represent shape variations. A complex shape is decomposed into several components which are described as principal component analysis (PCA) based models in various levels. The hierarchical representation allows for chain-like conditional dependency within a single level and bidirectional communication between different levels. Additionally, a sequential Monte-Carlo (SMC) based inference algorithm that can explore the graphical structure is proposed to estimate the contour. The experiments performed on real-world hand and face images show that the proposed method is effective in combating occlusion and cluttered background. Moreover, it is possible to isolate the localization error to an individual component of a shape attributed to the hierarchical representation.