PhD Research Journals

Registration is an essential issue to be addressed in various machine vision and medical imaging research areas. The process of registration determines a one-to-one mapping or transformation between the coordinates system in one space to other, such that points in the two spaces that correspond to the same anatomy are mapped to each other. In general, the registration procedure can be illustrated as three stages: (1) choice of transformation (e.g., rigid or non-rigid); (2) surface representation (e.g., points or curves) and similarity criterion (e.g., mutual information, distance or intensity difference); (3) matching, optimization and transformation computation. Registration methods based on intrinsic information are preferred in current researches for the marker-based registration will either result in unfavorable disadvantages of invasiveness or low registration accuracy. Based on the nature of face model as the object, to be registered with other face model modalities (2D or 3D), rigid and non-rigid registration is investigated respectively.

Rigid registration deals with 6 degrees of freedom, i.e., translation and rotation. While non-rigid should be applied when handle transformation of more than 6 degrees of freedom. Similarity criterion is closely related to the choice of matching primitives in terms of the size of transformation or weather the transformation is rigid or non-rigid.

(1) choice of transformation
(has been explained briefly yesterday)

(2) Surface representation and similarity criterion
There are four approaches to represent a surface for the sake of registration and they are:
a) Feature based
- Attempts to express surface morphology as a set of features which are extracted by a pre-processing step. Such feature provides a compact description of the surface.
- similar criterion: comparison of scalar measure

b) Point-based
- do not attempt to reduce the surface representation to a more compact description, rather they use all or a large subset of all points
- the primitive used is often the surface point itself
- similar criterion: minimal distance between a pair of surface points

c) model-based
- do not attempt to reduce the surface representation to a more compact description, rather they use all or a large subset of all points
- similar criterion: often an implicit criterion is used (external force or halting condition driven by two sets of data, with which an evolving deformable surface model must be reconciled.

d) global similarity


Argument: Feature-based and global approach are normally used for large motion/transformation while point or model-based methods are attractive in the case of small or iteratively estimated motion (high redundancy of information) and useful for estimating locally non-rigid transformation.


Above literature review are from:
http://mrcas.mpe.ntu.edu.sg/research/urobot/registration.htm

Audette M, Ferrie F and Peters T (1999), An algorithmic overview of surface registrations techniques for medical imaging, Medical Image Analaysis, Oxford University Press