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The parallel iterative closest point algorithm

by Christian Langis, Michael Greenspan, Guy Godin - In Proceedings of Fourth International Conference on 3-D Digital Imaging and Modeling , 2001
"... This paper describes a parallel implementation developed to improve the time performance of the Iterative Closest Point Algorithm. Within each iteration, the correspon-dence calculations are distributed among the processor re-sources. At the end of each iteration, the results of the cor-respondence ..."
Abstract - Cited by 12 (0 self) - Add to MetaCart
This paper describes a parallel implementation developed to improve the time performance of the Iterative Closest Point Algorithm. Within each iteration, the correspon-dence calculations are distributed among the processor re-sources. At the end of each iteration, the results of the cor

The Trimmed Iterative Closest Point Algorithm

by D. Chetverikov, D. Svirko, D. Stepanov, Pavel Krsek - In International Conference on Pattern Recognition , 2002
"... The problem of geometric alignment of two roughly preregistered, partially overlapping, rigid, noisy 3D point sets is considered. A new natural and simple, robustified extension of the popular Iterative Closest Point (ICP) algorithm [1] is presented, called the Trimmed ICP (TrICP). The new algorithm ..."
Abstract - Cited by 69 (4 self) - Add to MetaCart
The problem of geometric alignment of two roughly preregistered, partially overlapping, rigid, noisy 3D point sets is considered. A new natural and simple, robustified extension of the popular Iterative Closest Point (ICP) algorithm [1] is presented, called the Trimmed ICP (TrICP). The new

Morphological Iterative Closest Point Algorithm

by Vavoulidis And, C. P. Vavoulidis, I. Pitas - IEEE Trans. On Image Processing , 1999
"... . This paper describes a method for accurate and computationally efficient registration of 3\GammaD shapes including curves and surfaces. The method is based on the iterative closest point (ICP) algorithm. The real strength of our algorithm is the use of Morphological Voronoi tessellation method to ..."
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. This paper describes a method for accurate and computationally efficient registration of 3\GammaD shapes including curves and surfaces. The method is based on the iterative closest point (ICP) algorithm. The real strength of our algorithm is the use of Morphological Voronoi tessellation method

VICP: Velocity Updating Iterative Closest Point Algorithm

by Seungpyo Hong, Heedong Ko, Jinwook Kim
"... Abstract — We propose a novel method to enhance a family of ICP(iterative closest point) algorithms by updating velocity. Even though ICP algorithms play a dominant role in a model based tracking, it is difficult to avoid an accumulated tracking error during a continuous motion. It is because that t ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
Abstract — We propose a novel method to enhance a family of ICP(iterative closest point) algorithms by updating velocity. Even though ICP algorithms play a dominant role in a model based tracking, it is difficult to avoid an accumulated tracking error during a continuous motion. It is because

An approximate EM Homographical Iterative Closest Point algorithm.

by Markus Louw, Fred Nicolls
"... This paper describes an approximately expectationmaximization (EM) formulation of a homographical iterative closest point registration approach (henceforth HICP). We show that such an EM approach allows the algorithm to converge faster, and more robustly in the presence of noise. Although this algor ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
This paper describes an approximately expectationmaximization (EM) formulation of a homographical iterative closest point registration approach (henceforth HICP). We show that such an EM approach allows the algorithm to converge faster, and more robustly in the presence of noise. Although

Iterative Closest Point Algorithm in the Presence of Anisotropic Noise

by L. Maier-hein, T. R. Dos Santos, A. M. Franz, H. -p. Meinzer
"... Abstract. The Iterative closest point (ICP) algorithm is a widely used method for 3D point set registration. It iteratively establishes point correspondences between two input data sets and computes a rigid transformation accordingly. From a statistical point of view, the algorithm implicitly assume ..."
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Abstract. The Iterative closest point (ICP) algorithm is a widely used method for 3D point set registration. It iteratively establishes point correspondences between two input data sets and computes a rigid transformation accordingly. From a statistical point of view, the algorithm implicitly

The dual-bootstrap iterative closest point algorithm with application to retinal image registration

by Charles V. Stewart, Chia-ling Tsai, Badrinath Roysam - IEEE Trans. Med. Img , 2003
"... Abstract—Motivated by the problem of retinal image registration, this paper introduces and analyzes a new registration algorithm called Dual-Bootstrap Iterative Closest Point (Dual-Bootstrap ICP). The approach is to start from one or more initial, low-order estimates that are only accurate in small ..."
Abstract - Cited by 88 (19 self) - Add to MetaCart
Abstract—Motivated by the problem of retinal image registration, this paper introduces and analyzes a new registration algorithm called Dual-Bootstrap Iterative Closest Point (Dual-Bootstrap ICP). The approach is to start from one or more initial, low-order estimates that are only accurate in small

Registration of Range Data Using a Hybrid Simulated Annealing and Iterative Closest Point Algorithm

by Jason P. Luck, Hoff, William A. Hoff (corresponding, Little, Underwood, Charles Q. Little , 2000
"... This paper presents a novel approach for registering two sets of 3-D range data points, using an optimization algorithm that is both robust and efficient. The algorithm combines the speed of an iterative closest point algorithm with the robustness of a simulated annealing algorithm. Additionally, a ..."
Abstract - Cited by 29 (0 self) - Add to MetaCart
This paper presents a novel approach for registering two sets of 3-D range data points, using an optimization algorithm that is both robust and efficient. The algorithm combines the speed of an iterative closest point algorithm with the robustness of a simulated annealing algorithm. Additionally, a

Face recognition from 3D data using Iterative Closest Point algorithm and Gaussian mixture models

by Jamie Cook, Vinod Chandran, Sridha Sridharan, Clinton Fookes - PROC. SECOND INT’L SYMP. 3D DATA PROCESSING, VISUALIZATION, AND TRANSMISSION , 2004
"... A new approach to face verification from 3D data is presented. The method uses 3D registration techniques designed to work with resolution levels typical of the irregular point cloud representations provided by Structured Light scanning. Preprocessing using a-priori information of the human face and ..."
Abstract - Cited by 19 (1 self) - Add to MetaCart
and the Iterative Closest Point algorithm are employed to establish correspondence between test and target and to compensate for the non-rigid nature of the surfaces. Statistical modelling in the form of Gaussian Mixture Models is used to parameterise the distribution of errors in facial surfaces after registration

3D Face Tracking Using Appearance Registration and Robust Iterative Closest Point Algorithm

by Fadi Dornaika
"... Abstract. Recently, researchers proposed deterministic and statistical appearance-based 3D face tracking methods that can successfully tackle the image variability and drift problems. However, appearance-based methods dedicated to 3D face tracking may suffer from inaccuracies since they are not very ..."
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registration technique (e.g., the Iterative Closest Point algorithm). In this paper, we propose a 3D face tracker that is based on appearance registration and on a fast variant of a robust Iter-ative Closest Point algorithm. The resulting 3D face tracker combines the advantages of both appearance
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