Notes for VOSM fitting process

As I made the notes on papers, I scanned it and upload it as images for record.
Main fitting program begins:
It reads the input configuration and trained model for fitting.

For each testing image (2D), detect the key points position before try to fit model to target. The labeled function will be shown in a second.

 The function "VO_FirstEstimationBySingleWarp()" calculate transformation between model and target based on their corresponding key points. I also marked the OpenCV function for 3D case:

 Here is the place where the main fitting algorithm happens. Before trying to fit the target, we make sure the model after the transformation is within acceptable shape variance. The fitting contain several iteration for different image pyramid (scaled by 2, 4, 8 etc.). In my case, I will only use one pyramid (original size) since the model from Kinect and MRI are both in real human size.

The function "VO_CalcAllParams4AnyShapeWithConstrain()" project the current fitting model to the shape parameters which are constrained by the corresponding trained eigenvalues. These acceptable parameters are then back-projected to the original fitting shape using PCA.

For each image pyramid the function "PyramidFit()" try to update the current shape so that it is more and more similar to the target. As the update process may ruin the shape, the "VO_CalcAllParams4AnyShapeWithConstrain()" function is called again to prevent that happens. The fitting process will be terminated for two reasons: 1) Enough proportion of landmarks are fitted to the target; 2) Certain number of iteration has been went through so the fitting process won't go forever.

Now is the core function where the landmarks are moved to the target. As shown in the notes, each landmarks is moved to the best location according to matching profile. In my case, since the intensity is not available for the training data, I will discuss another strategy to find the best location of landmarks in future post.

Here is the last function, "VO_FindBestMatchingProfile1D". Although I will not find the best matching based on profile, it is still worth to investigate the idea.

To wrap up, the whole procedure can be refined as following flow chart: