Registration이란
1. 정의
point set registration(=point matching)
the process of finding a spatial transformation that aligns two point sets.
목적 : merging multiple data sets into a globally consistent model, and mapping a new measurement to a known data set to identify features or to estimate its pose.
확용 분야
- optical character recognition,
- augmented reality
- aligning data from magnetic resonance imaging with computer aided tomography scans.
1.1 Overview of problem
2. 분류
2.1 정합은 점군 데이터의 개수에 따라,
- 데이터가 둘이면 이중 정합(pairwise registration),
- 셋 이상이면 다중 정합(multiview registration)으로 구분할 수 있다.
2.2 물체의 Rigid 여부에 따라
A. Rigid Registration
Given two point sets, rigid registration yields a rigid transformation which maps one point set to the other.
A rigid transformation is defined as a transformation that does not change the distance between any two points.
Typically such a transformation consists of translation and rotation.[2] In rare cases, the point set may also be mirrored.
A rigid transform can register objects that are related by rotation and translation.
예)if you are registering images of a patient’s bones
B. Non-rigid Registration
Given two point sets, non-rigid registration yields a non-rigid transformation which maps one point set to the other.
Non-rigid transformations include affine transformations such as scaling and shear mapping.
However, in the context of point set registration, non-rigid registration typically involves nonlinear transformation.
Non-rigid registration methods are capable of aligning images where correspondence cannot be achieved without localized deformations and can therefore better accomodate anatomical, physiological and pathological variability between patients.
C. 기타
2D용(??)
Affine Registration : The affine transform allows for shearing and scaling in addition to the rotation and translation.
Groupwise Registration : Groupwise registration methods try to mitigate uncertainties associated with any one image by simultaneously registering all images in a population.
Point-based Registation : Point-based registration allows us to help the registration via pre-defined sets of corresponding points.
3. 알고리즘
3.1 Iterative closest point
Besl, Paul; McKay, Neil (1992). "A Method for Registration of 3-D Shapes". IEEE Transactions on Pattern Analysis and Machine Intelligence. 14 (2): 239–256. doi:10.1109/34.121791.
The algorithm performs rigid registration in an iterative fashion
and then finding the least squares rigid transformation
3.2 Robust point matching
3.3 Thin plate spline robust point matching
The thin plate spline robust point matching (TPS-RPM) algorithm by Chui and Rangarajan augments the RPM method to perform non-rigid registration by parametrizing the transformation as a thin plate spline.
3.4 The kernel correlation (KC)
KC approach of point set registration was introduced by Tsin and Kanade.[7]
Compared with ICP, the KC algorithm is more robust against noisy data.
Unlike ICP, where, for every model point, only the closest scene point is considered, here every scene point affects every model point.
3.5 Coherent point drift
Coherent point drift (CPD) was introduced by Myronenko and Song.
The algorithm takes a probabilistic approach to aligning point sets, similar to the GMM KC method.
Unlike earlier approaches to non-rigid registration which assume a thin plate spline transformation model, CPD is agnostic with regard to the transformation model used.
3.6 Sorting the Correspondence Space (SCS)
This algorithm was introduced in 2013 by H. Assalih to accommodate sonar image registration.
These kinds of images tend to have high amounts of noise, so it is expected to have lots of outliers in the point sets to match.
SCS delivers high robustness against outliers and can surpass ICP and CPD performance in the presence of outliers.
SCS doesn’t use iterative optimization in high dimensional space and is neither probabilistic nor spectral.
SCS can match rigid and non-rigid transformations, and performs best when the target transformation is between three and six degrees of freedom.