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Iterative most-likely point registration (IMLP): a robust algorithm for computing optimal shape alignment.

Billings SD, Boctor EM, Taylor RH - PLoS ONE (2015)

Bottom Line: Our GTLS approach has improved accuracy, efficiency, and stability compared to prior methods presented for this problem and offers a straightforward implementation using standard least squares.We evaluate the performance of IMLP relative to a large number of prior algorithms including ICP, a robust variant on ICP, Generalized ICP (GICP), and Coherent Point Drift (CPD), as well as drawing close comparison with the prior anisotropic registration methods of GTLS-ICP and A-ICP.The performance of IMLP is shown to be superior with respect to these algorithms over a wide range of noise conditions, outliers, and misalignments using both mesh and point-cloud representations of various shapes.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Johns Hopkins University, Baltimore, MD, United States of America.

ABSTRACT
We present a probabilistic registration algorithm that robustly solves the problem of rigid-body alignment between two shapes with high accuracy, by aptly modeling measurement noise in each shape, whether isotropic or anisotropic. For point-cloud shapes, the probabilistic framework additionally enables modeling locally-linear surface regions in the vicinity of each point to further improve registration accuracy. The proposed Iterative Most-Likely Point (IMLP) algorithm is formed as a variant of the popular Iterative Closest Point (ICP) algorithm, which iterates between point-correspondence and point-registration steps. IMLP's probabilistic framework is used to incorporate a generalized noise model into both the correspondence and the registration phases of the algorithm, hence its name as a most-likely point method rather than a closest-point method. To efficiently compute the most-likely correspondences, we devise a novel search strategy based on a principal direction (PD)-tree search. We also propose a new approach to solve the generalized total-least-squares (GTLS) sub-problem of the registration phase, wherein the point correspondences are registered under a generalized noise model. Our GTLS approach has improved accuracy, efficiency, and stability compared to prior methods presented for this problem and offers a straightforward implementation using standard least squares. We evaluate the performance of IMLP relative to a large number of prior algorithms including ICP, a robust variant on ICP, Generalized ICP (GICP), and Coherent Point Drift (CPD), as well as drawing close comparison with the prior anisotropic registration methods of GTLS-ICP and A-ICP. The performance of IMLP is shown to be superior with respect to these algorithms over a wide range of noise conditions, outliers, and misalignments using both mesh and point-cloud representations of various shapes.

No MeSH data available.


Related in: MedlinePlus

Human hip- and femur-bone meshes used in the registration studies.The red points represent a typical randomly generated source shape as sampled from the mesh surface. (A): The hip mesh is used in registration Experiments 2–5. (B): The femur mesh is used for the sub-shape registration study of Experiment 6, where points for the source shape are sampled from the shaded region of the mesh.
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pone.0117688.g001: Human hip- and femur-bone meshes used in the registration studies.The red points represent a typical randomly generated source shape as sampled from the mesh surface. (A): The hip mesh is used in registration Experiments 2–5. (B): The femur mesh is used for the sub-shape registration study of Experiment 6, where points for the source shape are sampled from the shaded region of the mesh.

Mentions: In this study, we evaluate the performance of the IMLP algorithm for registering a target shape represented by a triangular mesh. The experiment is divided into two sub-experiments (Experiments 2A and 2B) in order to evaluate the algorithm’s performance under different magnitudes of shape misalignment. The shape being registered in both cases is a human hip model segmented from CT imaging to form a surface mesh (Fig. 1A).


Iterative most-likely point registration (IMLP): a robust algorithm for computing optimal shape alignment.

Billings SD, Boctor EM, Taylor RH - PLoS ONE (2015)

Human hip- and femur-bone meshes used in the registration studies.The red points represent a typical randomly generated source shape as sampled from the mesh surface. (A): The hip mesh is used in registration Experiments 2–5. (B): The femur mesh is used for the sub-shape registration study of Experiment 6, where points for the source shape are sampled from the shaded region of the mesh.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4352012&req=5

pone.0117688.g001: Human hip- and femur-bone meshes used in the registration studies.The red points represent a typical randomly generated source shape as sampled from the mesh surface. (A): The hip mesh is used in registration Experiments 2–5. (B): The femur mesh is used for the sub-shape registration study of Experiment 6, where points for the source shape are sampled from the shaded region of the mesh.
Mentions: In this study, we evaluate the performance of the IMLP algorithm for registering a target shape represented by a triangular mesh. The experiment is divided into two sub-experiments (Experiments 2A and 2B) in order to evaluate the algorithm’s performance under different magnitudes of shape misalignment. The shape being registered in both cases is a human hip model segmented from CT imaging to form a surface mesh (Fig. 1A).

Bottom Line: Our GTLS approach has improved accuracy, efficiency, and stability compared to prior methods presented for this problem and offers a straightforward implementation using standard least squares.We evaluate the performance of IMLP relative to a large number of prior algorithms including ICP, a robust variant on ICP, Generalized ICP (GICP), and Coherent Point Drift (CPD), as well as drawing close comparison with the prior anisotropic registration methods of GTLS-ICP and A-ICP.The performance of IMLP is shown to be superior with respect to these algorithms over a wide range of noise conditions, outliers, and misalignments using both mesh and point-cloud representations of various shapes.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Johns Hopkins University, Baltimore, MD, United States of America.

ABSTRACT
We present a probabilistic registration algorithm that robustly solves the problem of rigid-body alignment between two shapes with high accuracy, by aptly modeling measurement noise in each shape, whether isotropic or anisotropic. For point-cloud shapes, the probabilistic framework additionally enables modeling locally-linear surface regions in the vicinity of each point to further improve registration accuracy. The proposed Iterative Most-Likely Point (IMLP) algorithm is formed as a variant of the popular Iterative Closest Point (ICP) algorithm, which iterates between point-correspondence and point-registration steps. IMLP's probabilistic framework is used to incorporate a generalized noise model into both the correspondence and the registration phases of the algorithm, hence its name as a most-likely point method rather than a closest-point method. To efficiently compute the most-likely correspondences, we devise a novel search strategy based on a principal direction (PD)-tree search. We also propose a new approach to solve the generalized total-least-squares (GTLS) sub-problem of the registration phase, wherein the point correspondences are registered under a generalized noise model. Our GTLS approach has improved accuracy, efficiency, and stability compared to prior methods presented for this problem and offers a straightforward implementation using standard least squares. We evaluate the performance of IMLP relative to a large number of prior algorithms including ICP, a robust variant on ICP, Generalized ICP (GICP), and Coherent Point Drift (CPD), as well as drawing close comparison with the prior anisotropic registration methods of GTLS-ICP and A-ICP. The performance of IMLP is shown to be superior with respect to these algorithms over a wide range of noise conditions, outliers, and misalignments using both mesh and point-cloud representations of various shapes.

No MeSH data available.


Related in: MedlinePlus