The ASM method we described is based on the 2D coloring image. However, such technique can also be applied to handle the range images obtained from the 3D scanner. In this section, describe the general process of how to extend the 2D ASM method to handling the range images. The range image is a 2D image which each pixel stores the distance of captured surface point from the observer. Thus, 3D surface of the observed object can be reconstructed from the range image. The general process of matching features in range images using ASM is the same as the way in 2D coloring images. However, due to the different meaning of pixel values, there still face lots of difficulties in matching features in range image using ASM. We describe the ASM method for matching the features on range image by first review the overall process of ASM on the range image, and then point out the challenges in finding the featured landmarks in range image and the transformation in 3D space.
we extend this technique to handle 3D image data. The active shape model we mentioned before can only handle rotations which are present in the manually landmark training 2D images. If we turn into 3D image data, although 3DASM is no more than rotation, translation and scale parameters, as presented in [26], it will not be robustness. For example, as we considering on facial tracking, a problem arises when the head turns to one side and only a profile view is visible. Then the number of feature points on two image will different and cause out of correspondence, we call this trouble as occlusion or data missing. Moritz Kaiser, et al.[27] propose brightness constancy assumption to decrease error norm of image correspondence.
Ying Chen, et al.[50] propose active conditional models, it learns the conditional relation between a reference view of the object and other view points. In future, dense real-time 3D face tracking and 3D deformation modeling will be a challenging
Chapter 4 Future work
Face correspondence is a challenging and interesting problem in computer vi-sion, the active shape model is one of the simplest methods for image correspondence.
In this thesis, we arrange the development of active shape method in the last two decades, include in 2D and 3D spaces. Even though there is no significant effect in facial recovery, there is still work to be done. Future work includes how to handle the image constructed by triangular mesh and combine other model-based method to give the best correspondence results for a class of shapes.
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