On-Line, Incremental Learning of a Robust Active Shape Model

摘要

Active Shape Models are commonly used to recognize and locate different aspects of known rigid objects. However, they require an off-line learning stage, such that the extension of an existing model requires a complete new retraining phase. Furthermore, learning is based on principal component analysis and requires perfect training data that is not corrupted by partial occlusions or imperfect segmentation. The contribution of this paper is twofold: First, we present a novel robust Active Shape Model that can handle corrupted shape data. Second, this model can be created on-line through the use of a robust incremental PCA algorithm. Thus, an already partially learned Active Shape Model can be used for segmentation of a new image in a level set framework and the result of this segmentation process can be used for an on-line update of the robust model. Our experimental results demonstrate the robustness and the flexibility of this new model, which is at the same time computationally much more efficient than previous ASMs using batch or iterated batch PCA.