scholarly journals An improved 3D shape context registration method for non-rigid surface registration

2010 ◽  
Author(s):  
Di Xiao ◽  
David Zahra ◽  
Pierrick Bourgeat ◽  
Paula Berghofer ◽  
Oscar Acosta Tamayo ◽  
...  
Keyword(s):  
Surface Registration ◽  
Rigid Surface ◽  
Shape Context ◽  
Registration Method ◽  
3D Shape

An accurate 3D shape context based non-rigid registration method for mouse whole-body skeleton registration

2011 ◽  
Author(s):  
Di Xiao ◽  
David Zahra ◽  
Pierrick Bourgeat ◽  
Paula Berghofer ◽  
Oscar Acosta Tamayo ◽  
...  
Keyword(s):  
Whole Body ◽  
Shape Context ◽  
Registration Method ◽  
Rigid Registration ◽  
3D Shape

An improved 3D shape context based non-rigid registration method and its application to small animal skeletons registration

2010 ◽  
Vol 34 (4) ◽  
pp. 321-332 ◽  
Author(s):  
Di Xiao ◽  
David Zahra ◽  
Pierrick Bourgeat ◽  
Paula Berghofer ◽  
Oscar Acosta Tamayo ◽  
...  
Keyword(s):  
Small Animal ◽  
Shape Context ◽  
Registration Method ◽  
Rigid Registration ◽  
3D Shape

scholarly journals 3D shape context surface registration for cortical mapping

2010 ◽  
Author(s):  
Oscar Acosta ◽  
Jurgen Fripp ◽  
Andrea Rueda ◽  
Di Xiao ◽  
Erik Bonner ◽  
...  
Keyword(s):  
Surface Registration ◽  
Cortical Mapping ◽  
Shape Context ◽  
3D Shape

Monte Carlo simulation and analysis of free-form surface registration

1997 ◽  
Vol 211 (8) ◽  
pp. 605-617 ◽  
Author(s):  
D Brujic ◽  
M Ristic
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Confidence Intervals ◽  
Computer Aided Design ◽  
Maximum Error ◽  
Surface Registration ◽  
Performance Criteria ◽  
Free Form ◽  
Registration Method ◽  
Free Form Surfaces

Accurate dimensional inspection and error analysis of free-form surfaces requires accurate registration of the component in hand. Registration of surfaces defined as non-uniform rational B-splines (NURBS) has been realized through an implementation of the iterative closest point method (ICP). The paper presents performance analysis of the ICP registration method using Monte Carlo simulation. A large number of simulations were performed on an example of a precision engineering component, an aero-engine turbine blade, which was judged to possess a useful combination of geometric characteristics such that the results of the analysis had generic significance. Data sets were obtained through CAD (computer aided design)-based inspection. Confidence intervals for estimated transformation parameters, maximum error between a measured point and the nominal surface (which is extremely important for inspection) mean error and several other performance criteria are presented. The influence of shape, number of measured points, measurement noise and some less obvious, but not less important, factors affecting confidence intervals are identified through statistical analysis.

scholarly journals Automatic Defect Detection for Web Offset Printing Based on Machine Vision

2019 ◽  
Vol 9 (17) ◽  
pp. 3598 ◽  
Author(s):  
Erhu Zhang ◽  
Yajun Chen ◽  
Min Gao ◽  
Jinghong Duan ◽  
Cuining Jing
Keyword(s):  
Image Registration ◽  
Defect Detection ◽  
Feature Matching ◽  
Reference Image ◽  
Printing Industry ◽  
Offset Printing ◽  
Shape Context ◽  
Image Subtraction ◽  
Registration Method

In the printing industry, defect detection is of crucial importance for ensuring the quality of printed matter. However, rarely has research been conducted for web offset printing. In this paper, we propose an automatic defect detection method for web offset printing, which consists of determining first row of captured images, image registration and defect detection. Determining the first row of captured images is a particular problem of web offset printing, which has not been studied before. To solve this problem, a fast computational algorithm based on image projection is given, which can convert 2D image searching into 1D feature matching. For image registration, a shape context descriptor is constructed by considering the shape concave-convex feature, which can effectively reduce the dimension of features compared with the traditional image registration method. To tolerate the position difference and brightness deviation between the detected image and the reference image, a modified image subtraction is proposed for defect detection. The experimental results demonstrate the effectiveness of the proposed method.

scholarly journals Finite Element Surface Registration Incorporating Curvature, Volume Preservation, and Statistical Model Information

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Thomas Albrecht ◽  
Andreas Dedner ◽  
Marcel Lüthi ◽  
Thomas Vetter
Keyword(s):  
Finite Element ◽  
Adaptive Mesh Refinement ◽  
Adaptive Mesh ◽  
Surface Registration ◽  
Deformation Model ◽  
Registration Method ◽  
Element Discretization ◽  
Volume Preservation ◽  
Continuous Formulation ◽  
Statistical Deformation Model

We present a novel method for nonrigid registration of 3D surfaces and images. The method can be used to register surfaces by means of their distance images, or to register medical images directly. It is formulated as a minimization problem of a sum of several terms representing the desired properties of a registration result: smoothness, volume preservation, matching of the surface, its curvature, and possible other feature images, as well as consistency with previous registration results of similar objects, represented by a statistical deformation model. While most of these concepts are already known, we present a coherent continuous formulation of these constraints, including the statistical deformation model. This continuous formulation renders the registration method independent of its discretization. The finite element discretization we present is, while independent of the registration functional, the second main contribution of this paper. The local discontinuous Galerkin method has not previously been used in image registration, and it provides an efficient and general framework to discretize each of the terms of our functional. Computational efficiency and modest memory consumption are achieved thanks to parallelization and locally adaptive mesh refinement. This allows for the first time the use of otherwise prohibitively large 3D statistical deformation models.

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