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Analyzing incomplete longitudinal clinical trial data by Molenberghs G., Thijs H., Jansen I.

By Molenberghs G., Thijs H., Jansen I.

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Extra resources for Analyzing incomplete longitudinal clinical trial data

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Resolution (up to 300fps), however the spatial resolution is low (only tens of markers on face are feasible). Appearance details due to facial deformation, therefore, is handled using our flexible appearance model presented in chapter 6. The Microsoft data, collected by by Guenter et al. , 1998], use 153 markers. 2 shows an example of the markers. 2 (b) and (c). 2. , 1998]. (a): The markers are shown as small white dots. (b) and (c): The mesh is shown in two different viewpoints. 3. Learning Holistic Linear Subspace To make complex facial deformation tractable in computational models, people have usually assumed that any facial deformation can be approximated by a linear combination of some basic deformation.

In similar spirit to [Noh and Neumann, 2001], we design our MU fitting as a two-step process: (1) face geometry based MU-adjustment; and (2) MU re-sampling. These two steps can be improved in a systematic way if enough MU sets are collected. For example, if MU statistics over a large set of different face geometries are available, one can systematically derive the geometry-to-MU mapping using machine-learning techniques. On the other hand, If multiple MU sets are available, which sample different positions of the same face, it is possible to combine them to increase the spatial resolution of MU because markers in MU are usually sparser than face geometry mesh.

For the adaptation, it requires that the face be in its neutral position in the first image 36 3D FACE PROCESSING: MODELING, ANALYSIS AND SYNTHESIS frame and the facial feature locations are detected. In the original system, it is already done interactively via a GUI. In the future, we plan to detect these feature locations automatically such that the whole fitting could be automatic. 1. Typical tracked frames and corresponding animated face models. (a): The input image frames. (b): The tracking results visualized by yellow mesh overlayed on input images.

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