ICA vs. PCA Active Appearance Models: Application to Cardiac MR Segmentation

Abstract. Statistical shape models generally use Principal Component Analysis (PCA) to describe the main directions of shape variation in a training set of ex-ample shapes. However, PCA has the restriction that the input data must be drawn from a Gaussian distribution and is only able to describe global shape variations. In this paper we evaluate the use of an alternative shape decomposi-tion, Independent Component Analysis (ICA), for two reasons. ICA does not require a Gaussian distribution of the input data and is able to describe localized shape variations. With ICA however, the resulting vectors are not ordered, therefore a method for ordering the Independent Components is presented in this paper. To evaluate ICA-based Active Appearance Models (AAMs), 10 leave-15-out models were trained on a set of 150 short-axis cardiac MR Images with PCA-based as well as ICA-based AAMs. The median values for the aver-age and maximal point-to-point distances between the expert drawn and auto-matically segmented contours for the PCA-based AAM were 2.95 and 8.39 pix-els. For the ICA-based AAM these distances were 1.86 and 5.01 pixels respec-tively. From this, we conclude that the use of ICA results in a substantial im-provement in border localization accuracy over a PCA-based model.

Coarse-to-fine autoencoder networks (CFAN) for real-time face alignment

Abstract. Accurate face alignment is a vital prerequisite step for most face perception tasks such as face recognition, facial expression analysis and non-realistic face re-rendering. It can be formulated as the nonlinear inference of the facial landmarks from the detected face region. Deep network seems a good choice to model the nonlinearity, but it is nontrivial to apply it directly. In this paper, instead of a straightforward application of deep network, we propose a Coarse-to-Fine Auto-encoder Networks (CFAN) approach, which cascades a few successive Stacked Auto-encoder Networks (SANs). Specifically, the first SAN predicts the landmarks quickly but accurately enough as a preliminary, by taking as input a low-resolution version of the detected face holistically. The following SANs then progressively refine the landmark by taking as input the local features extracted around the current landmarks (output of the previous SAN) with higher and higher resolution. Extensive experiments conducted on three challenging datasets demonstrate that our CFAN outperforms the state-of-the-art methods and performs in real-time(40+fps excluding face detection on a desktop)