A Noise Robust Statistical Texture Model | Klaus B. Hilger, Mikkel B. Stegmann, Rasmus Larsen
| | Abstract | The study presents a noise robust low dimensional representation of texture variation present in a training set. The conventional analysis of training textures in the Active Appearance Models (AAMs) segmentation frame-work is extended by the new representation. This is accomplished by augmenting the model with an estimate of the covariance of the noise present in the training data. A compact model is obtained maximizing the signal-to-noise ratio (SNR), thus favouring subspaces rich on signal, and low on noise. The extended method is evaluated on a set of left cardiac ventricles obtained using magnetic resonance imaging (MRI). | | Type | Misc [Poster] | | Journal/Book/Conference | Medical Image Computing and Computer-Assisted Intervention - MICCAI 2002, 5th Int. Conference, Tokyo, Japan | | Year | 2002 | | Electronic version(s) | [pdf] | | BibTeX data | [bibtex] | | IMM Group(s) | Image Analysis & Computer Graphics |
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