Inter-Subject Non-Rigid Registration: An Overview with Classification and the Romeo Algorithm

2007 ◽  
pp. 273-337
Author(s):  
Pierre Hellier
Keyword(s):  
Rigid Registration

Research on the Topology Preservation of the Demons Non-rigid Registration Algorithm

2010 ◽  
Vol 36 (1) ◽  
pp. 179-183
Author(s):  
Xiang-Bo LIN ◽  
Tian-Shuang QIU ◽  
Su RUAN ◽  
NICOLIER Frédéric
Keyword(s):  
Topology Preservation ◽  
Rigid Registration ◽  
Registration Algorithm

Non-rigid registration of biomedical image for radiotherapy based on adaptive feature density flow

2021 ◽  
Vol 68 ◽  
pp. 102691
Author(s):  
Jinghua Xu ◽  
Mingzhe Tao ◽  
Shuyou Zhang ◽  
Xue Jiang ◽  
Jianrong Tan
Keyword(s):  
Rigid Registration ◽  
Biomedical Image ◽  
Density Flow

An Error-Adaptive, Non-Rigid Registration Method for the Analysis of Springback in Sheet Metal Forming

2015 ◽  
Vol 651-653 ◽  
pp. 1015-1020 ◽  
Author(s):  
Matthias Schweinoch ◽  
Alexei Sacharow ◽  
Dirk Biermann ◽  
Christoph Buchheim
Keyword(s):  
Sheet Metal ◽  
Sheet Metal Forming ◽  
Metal Forming ◽  
Simulation Software ◽  
Registration Method ◽  
Rigid Registration ◽  
Reference Shape ◽  
Geometric Deviations ◽  
Error Metric ◽  
Test Shape

Springback effects, as occuring in sheet metal forming processes, pose a challenge to manufacturingplanning: the as-built part may deviate from the desired shape rendering it unusable forits intended purpose. A compensation can be achieved by modifying the forming tools to counteractthe shape deviations. A prerequisite to compensation is the knowledge of correspondences (ui; vj),between points ui on the desired and vj on the actual shape. FEM-based simulation software providesmeans to both virtually predict springback and directly obtain correspondences. In case of experimentalprototyping and validation, however, finding correspondences requires solving a registrationproblem: given a test shape Q (scan points of the as-built geometry) and a reference shape R (CADdata of the desired geometry), a transformation S has to be found to fit both objects. Correspondencesbetween S(Q) and R may then be computed based on a metric.If S is restricted to Euclidean transformations, then S(Q) results in a rigid transformation, whereevery point of Q is subject to the same translation and rotation. Local geometric deviations due tospringback are not considered, often resulting in invalid correspondences. In this contribution, a nonrigidregistration method for the efficient analysis of springback is therefore presented. The test shape Q is iteratively partitioned into segments with respect to an error metric. The segments are locally registeredusing rigid registration subject to regulatory conditions. Resulting discontinuities are addressedby minimization of the deformation energy. The error metric uses information about the deviationscomputed based on the correspondences of the previous iteration, e.g. maximum errors or changes ofthe sign. This adaptive per-segment registration allows appropriate correspondences to be determinedeven under local geometric deviations.

Coupling tumor growth with brain deformation: a constrained parametric non-rigid registration problem

2010 ◽  
Author(s):  
Andreas Mang ◽  
Stefan Becker ◽  
Alina Toma ◽  
Thorsten M. Buzug
Keyword(s):  
Tumor Growth ◽  
Rigid Registration ◽  
Brain Deformation

Intracranial Hemorrhage Brain Image Non-rigid Registration from Real-world Dataset to Reference Space

2021 ◽  
Author(s):  
Nhat Tan Le ◽  
Shoji Kobashi ◽  
Koichi Arimura ◽  
Koji Iihara ◽  
Sozo Inoue
Keyword(s):  
Intracranial Hemorrhage ◽  
Real World ◽  
Brain Image ◽  
Rigid Registration ◽  
Reference Space

scholarly journals AAM and Non-rigid Registration in Augmented Reality

10.5772/7132 ◽  
2010 ◽  
Author(s):  
Yuan Tian ◽  
Tao Guan ◽  
Cheng Wang
Keyword(s):  
Augmented Reality ◽  
Rigid Registration
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