scholarly journals Information-Theoretic Directly Manipulated Free-Form Deformation Labeled Point-Set Registration

2010 ◽  
Author(s):  
Nicholas J. Tustison ◽  
Suyash Awate ◽  
James Gee
Keyword(s):  
Spline Approximation ◽  
Transformation Model ◽  
Free Form ◽  
Theoretic Approach ◽  
Cardinality Constraints ◽  
Information Theoretic ◽  
Point Set Registration ◽  
Local Point ◽  
Free Form Deformation ◽  
Point Set

Our previous contributions to the ITK community include a generalized B-spline approximation scheme as well as a generalized information-theoretic measure for assessing point-set correspondence known as the Jensen-Havrda-Charvat-Tsallis (JHCT) divergence. In this submission, we combine these two contributions for the registration of labeled point-sets. The transformation model which uses the former contribution is denoted as directly manipulated free-form deformation (DMFFD) and has been used for image registration. The information-theoretic approach described not only eliminates exact cardinality constraints which plague exact landmark matching algorithms, but it also incorporates the local point-set structure into the similarity measure calculation. Although theoretical discussion of these two components is deferred to other venues, the implementation details given in this submission should be adequate for those wishing to use our algorithm. Visualization of results is aided by another of our previous contributions. Additionally, we provide the rudimentary command line parsing classes used in our testing routines which were written in the ITK style and also available to use consistent with the open-source paradigm.

scholarly journals A Novel Information-Theoretic Point-Set Measure Based on the Jensen-Havrda-Charvat-Tsallis Divergence

2008 ◽  
Author(s):  
Nicholas J. Tustison ◽  
Suyash Awate ◽  
James Gee
Keyword(s):  
Matrix Decomposition ◽  
Fine Tuning ◽  
Information Theoretic ◽  
Point Set Registration ◽  
Registration Algorithm ◽  
Divergence Point ◽  
Theoretic Point ◽  
Point Set ◽  
Jensen Shannon Divergence ◽  
Decomposition Schemes

A novel point-set registration algorithm was proposed in [6] based on minimization of the Jensen-Shannon divergence. In this contribution, we generalize this Jensen-Shannon divergence point-set measure framework to the Jensen-Havrda-Charvat-Tsallis divergence. This generalization permits a fine-tuning of the actual divergence measure between robustness and specificity. The principle contribution of this submission is theitk::JensenHavrdaCharvatTsallisPointSetMetric class which is derived from the existing itk::PointSetToPointSetMetric. In addition, we provide other classes with utility that would extend beyond the point-set measure framework that we provide in this paper. This includes a point-set analogue of the itk::ImageFunction, i.e. itk::PointSetFunction. From this class we derive the class itk::ManifoldParzenWindowsPointSetFunction which provides a Parzen windowing scheme for learning the local structure of point-sets. Finally, we include the itk::DecomposeTensorFunction class which wraps the different vnl matrix decomposition schemes for easy use within ITK.

Reproducing Existing Nacelle Geometries With the Free-Form Deformation Parametrization

2018 ◽  
Author(s):  
Konstantin Rusch ◽  
Martin Siggel ◽  
Richard-Gregor Becker
Keyword(s):  
Inverse Problem ◽  
Spline Approximation ◽  
Aircraft Design ◽  
Free Form ◽  
Design Phase ◽  
B Spline ◽  
Preliminary Aircraft Design ◽  
Least Squares Fit ◽  
Free Form Deformation ◽  
Grid Points

In the conceptual and preliminary aircraft design phase the Free-Form Deformation (FFD) is one of various parametrization schemes to define the geometry of an engine’s nacelle. This paper presents a method that is able to create a C2 continuous periodic approximation of existing reference nacelles with the B-spline based FFD, which is a generalization of the classical FFD. The basic principle of this method is to start with a rotational symmetric B-spline approximation of the reference nacelle, which is subsequently deformed with a FFD grid that is placed around the initial geometry. A method is derived that computes the displacement of the FFD grid points, such that the deformed nacelle approximates the reference nacelle with minimal deviations. As this turns out to be a linear inverse problem, it can be solved with a linear least squares fit. To avoid overfitting effects — like degenerative FFD grids which imply excessive local deformations — the inverse problem is regularized with the Tikhonov approach. The NASA CRM model and the IAE V2500 engine have been selected as reference geometries. Both resemble nacelles that are typically found on common aircraft models and both deviate sufficiently from the rotational symmetry. It is demonstrated that the mean error of the approximation decreases with an increase of the number of FFD grid points and how the regularization affects these results. Finally, the B-spline based FFD with the classical Bernstein based FFD are compared for both models. The results conceptually prove the usability of the FFD approach for the construction of nacelle geometries in the preliminary aircraft design phase.

scholarly journals Fast Aerial Image Geolocalization Using the Projective-Invariant Contour Feature

2021 ◽  
Vol 13 (3) ◽  
pp. 490
Author(s):  
Yongfei Li ◽  
Shicheng Wang ◽  
Hao He ◽  
Deyu Meng ◽  
Dongfang Yang
Keyword(s):  
Feature Matching ◽  
Projective Transformation ◽  
Aerial Image ◽  
Geometric Feature ◽  
Point Set Registration ◽  
Projective Invariant ◽  
Geometric Consistency ◽  
Local Point ◽  
Contour Feature ◽  
Point Set

We address the problem of aerial image geolocalization over an area as large as a whole city through road network matching, which is modeled as a 2D point set registration problem under the 2D projective transformation and solved in a two-stage manner. In the first stage, all the potential transformations aligning the query road point set to the reference road point set are found by local point feature matching. A local geometric feature, called the Projective-Invariant Contour Feature (PICF), which consists of a road intersection and the closest points to it in each direction, is specifically designed. We prove that the proposed PICF is equivariant under the 2D projective transformation group. We then encode the PICF with a projective-invariant descriptor to enable the fast search of potential correspondences. The bad correspondences are then removed by a geometric consistency check with the graph-cut algorithm effectively. In the second stage, a flexible strategy is developed to recover the homography transformation with all the PICF correspondences with the Random Sample Consensus (RANSAC) method or to recover the transformation with only one correspondence and then refine it with the local-to-global Iterative Closest Point (ICP) algorithm when only a few correspondences exist. The strategy makes our method efficient to deal with both scenes where roads are sparse and scenes where roads are dense. The refined transformations are then verified with alignment accuracy to determine whether they are accepted as correct. Experimental results show that our method runs faster and greatly improves the recall compared with the state-of-the-art methods.

Multi-View Image Color Correction Using 3D Point Set Registration

2021 ◽  
Author(s):  
Hyeonwoo Jeong ◽  
Byunghyun Yoon ◽  
Honggu Jeong ◽  
Kang-Sun Choi
Keyword(s):  
Color Correction ◽  
Point Set Registration ◽  
Point Set
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