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Automatic 3D point cloud registration algorithm based on triangle similarity ratio consistency

IET IMAGE PROCESSING 2020年第14期14卷 3314-3323页

作者： Zou, Xuyan He, Hanwu Wu, Yueming Chen, Youbin Xu, Mingxi Guangdong Univ Technol Guangzhou Guangdong Peoples R China Yibin Univ Yibin Sichuan Peoples R China

Three-dimensional (3D) point cloud registration is a fundamental key issue in 3D reconstruction, 3D object recognition and augmented reality. In this study, the authors propose a novel local feature descriptor called local angle statistics histogram (LASH) for efficient 3D point cloud registration. LASH forms a description of local shape geometries by encoding their properties on angles between the normal vector of the point and the vector formed by the point and other points in its local neighbourhood. In addition, they propose a 3D point cloud registration algorithm based on LASH. The registration algorithm firstly detects triangle matching points with consistent similarity ratios, and then aggregates each pair of triangular matching points into a set of matching points. They can use these matching sets to calculate multiple transformations between two point clouds. Finally, they use the error function to identify the best transformation and to achieve coarse alignment of the two point clouds. Experiments and comparisons with other global algorithms demonstrate that the proposed approach can be applied to register point clouds with considerable or limited overlaps and is robust to noise.

关键词： image reconstruction augmented reality object recognition feature extraction image representation image matching image registration point clouds triangle similarity ratio consistency three-dimensional point cloud registration 3D object recognition novel local feature descriptor local angle statistics histogram LASH local shape geometries local neighbourhood registration algorithm triangle matching points consistent similarity ratios triangular matching points

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Fast 3D registration of MR brain images using the projection correlation registration algorithm

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 1998年第1期36卷 107-111页

作者： Didon, JP Langevin, F Universite Technologie Compiegne DIM CIMA F-60205 Compiegne France

Cross-correlation can be used to match 2D images in translation and in rotation. An extension to 30 mono-modality matching is presented. This process allows comparison of two sets of data along the same orientation. To decrease computation time, oblique projections of a 'wandering slice' are used. The precision is about +/-0.2 degrees in rotation and +/-0.2 mm in translation. Some examples, applied to pre-and post-therapy comparison, are given.

关键词： MRI 3D imaging registration algorithm projection cross-correlation coefficient

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Two points registration algorithm for terrestrial laser scanner point clouds

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SURVEY REVIEW 2019年第366期51卷 238-243页

作者： Abdelhafiz, Ahmed Hassan, Taher Assiut Univ Fac Engn Civil Engn Dept Asyut Egypt

Despite all modern techniques in registering terrestrial laser scanner point clouds, registration of multiple scan positions is still a crucial issue, especially in large projects. Processes like the detection of corresponding points in scans and placing targets in fields consume a lot of effort, time and money. Three points are the minimum number of common points required for registration. In this work, a new two points registration (TPR) algorithm is developed to register terrestrial laser scanner point clouds using only two common points automatically. Decreasing the number of the required common points to two points reduces one-third of the registration work in both field and laboratory. An accuracy assessment study is then performed to compare the registration accuracy of the developed algorithm and the traditional algorithm employing both the time of flight laser scanner and the phase shift one. The results show that the accuracy of the two algorithms is approximately equal within the considered point clouds accuracy in both cases.

关键词： Laser scanning registration algorithm Point clouds

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A Modular registration algorithm for Medical Images

A Modular Registration Algorithm for Medical Images

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10th International Conference on Image Analysis and Recognition (ICIAR)

作者： Bertoluzza, Silvia Maggi, Giulia Tomatis, Stefano CNR IMATI Enrico Magenes Pavia Italy Univ Pavia Dept Math F Casorati Pavia Italy Humanitas Clin Inst Milan Italy

ISBN: (纸本)9783642390944;9783642390937

The aim of this communication is to present the design of a code for image registration based on a modular structure which allows to easily combine and interchange different image models, transformation classes and image "distance" functionals, which are dealt with by independent modules which can be implemented transparently to each other. The code is tested by comparing several registration strategies on medical images.

关键词： registration algorithm medical imaging optimization

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Tetrahedral Framework registration algorithm for the robot-assisted surgical navigation system

Tetrahedral Framework registration algorithm for the robot-a...

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IEEE International Conference on Information and Automation (ICIA)

作者： Zhu, Jiebao Sun, Yu Gao, Peng Ma, Kailin Hu, Ying Cao, Yong Chinese Acad Sci Harbin Inst Technol Guangdong Prov Key Lab Robot & Intelligent Syst Shenzhen Inst Adv TechnolShenzhen Grad Sch Beijing Peoples R China Harbin Inst Technol Shenzhen Grad Sch Dept Mech Engn & Automat Harbin Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Beijing Peoples R China Chinese Acad Sci Key Lab Human Machine Intelligence Synergy Syst Beijing Peoples R China Chinese Univ Hong Kong Hong Kong Hong Kong Peoples R China Xi An Jiao Tong Univ Xian Peoples R China

ISBN: (纸本)9781509041022

The accuracy of registration is one of the key issues in surgical navigation, which directly affects the result of operation. In this paper, we proposed a new registration algorithm, Tetrahedral Framework (TF), to improve precision and computing speed and conducted a calibration experiment by using the wax model with markers. The image data are obtained by Micro-CT scanning and loaded in VTK to gain the coordinate of the markers, while the tracking data in real space are collected from NDI equipment. The proposed TF algorithm can be described as follow. Firstly, the two point-sets are respectively divided into a tetrahedral framework part and a few of particles;secondly, the scale coefficient is calculated for mapping the image data into the real space;finally, the particle swarm optimization (PSO) is used to adjust the positions and attitude of image framework to acquire a maximal projection on the tracking framework planes. In order to improve the precision of registration, we combined the particles information in an optimization process. Besides, the experimental results show that the algorithm can satisfy the requirement in surgical navigation.

关键词： surgical robot registration algorithm image navigation tetrahedral framework error analysis

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Research on registration algorithm of Pyramid Edge

Research on Registration Algorithm of Pyramid Edge

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5th International Conference on Digital Image Processing (ICDIP)

作者： Tao Yongpeng Wang Yaxuan Guo Wu Dalian Int Studies Univ Sch Software Dalian Peoples R China

ISBN: (纸本)9780819493057

A fast image matching algorithm would be constructed based on improved Powell method, for the problem of which point to start and the lack of restrictions in searching range in conventional mean pyramid structure search algorithm. Improved LOG operators can be used to extracted edge images, and build edge Pyramid images: first match with low-resolution edge images, and then it can be implemented stepwise up to the full-resolution image matching. The results showed that the algorithm can reduce calculation amount and improve the efficiency distinctly.

关键词： LOG Operators Pyramid Edge registration algorithm

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RegisTree: a registration algorithm to enhance forest inventory plot georeferencing

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ANNALS OF FOREST SCIENCE 2019年第2期76卷 30-30页

作者： Fadili, Maryem Renaud, Jean-Pierre Bock, Jerome Vega, Cedric Inst Natl Informat Geog & Forestiere IGN Lab Inventaire Forestier 14 Rue Girardet F-54000 Nancy France ONF Dept RDI 8 Allee Longchamp F-54600 Villers Les Nancy France ONF Dept RDI 42 Quai Charles Roissart F-73026 Chambery France

Key messageThe accuracy of remote sensing-based models of forest attributes could be improved by controlling the spatial registration of field and remote sensing data. We have demonstrated the potential of an algorithm matching plot-level field tree positions with lidar canopy height models and derived local maxima to achieve a precise registration *** accuracy of remote sensing-based estimates of forest parameters depends on the quality of the spatial registration of the training *** study introduces an algorithm called RegisTree to correct field plot positions by matching a spatialized field tree height map with lidar canopy height models (CHMs).MethodsRegisTree is based on a point (field positions) to surface (CHM) adjustment approach modified to ensure that at least one field tree position corresponds to CHM local *** has been validated with respect to positioning errors and the performance of lidar-derived estimation of plot volume. Overall, RegisTree enabled to register field plots surveyed in a range of forest conditions with a precision of 1.5m (1.23m), but a higher performance for conifer plots, and a limited efficiency in homogeneous stands, having similar heights. Improved plot positions were found to have a limited impact on volume predictions under the range of tested conditions, with a gain up to 1.3%.Conclusion RegisTree could be used to improve the forest plot position from field surveys collected with low-grade GPS and to contribute to the development of processing chains of 3D remote sensing-based models of forest parameters.

关键词： Forest inventory Lidar Plot positioning registration algorithm Forest parameter estimation

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Random noise filtering method based on the inter-frame registration

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INFRARED PHYSICS & TECHNOLOGY 2014年 67卷 1-7页

作者： Yan, Qihuan Sui, Xiubao NUST Sch Elect Engn & Optoelect Technol Nanjing 210094 Jiangsu Peoples R China

One of the problems to be solved in image processing is how to eliminate image noise effectively. In this work, we brought forward a random noise filtering method based on the inter-frame registration. Firstly, we calculated the relative displacement of the adjacent frames by a registration algorithm. Then we divided the image into the overlapping area and the non-overlapping area according to the relative displacement. Finally, we do noise reduction processing for these two areas respectively. The experiments results indicate that the proposed method can reduce noise in both spatial and time domain of video images. The main advantage is that it cannot only remove noise, but also effectively protect the image edge and detail information. Besides, it not only maintains the de-noising effect of traditional inter-frame algorithm, but also is suitable for moving targets. It has better real-time performance and wider application range. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Video image de-noising Inter-frame filtering registration algorithm Hardware implementation

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Deformable multi-modal image registration for the correlation between optical measurements and histology images

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JOURNAL OF BIOMEDICAL OPTICS 2024年第6期29卷 066007页

作者： Feenstra, Lianne Lambregts, Maud Ruers, Theo J. M. Dashtbozorg, Behdad Netherlands Canc Inst Dept Surg Oncol Image Guided Surg Amsterdam Netherlands Univ Twente Fac Sci & Technol Dept Nanobiophys Enschede Netherlands

Significance: The accurate correlation between optical measurements and pathology relies on precise image registration, often hindered by deformations in histology images. We investigate an automated multi-modal image registration method using deep learning to align breast specimen images with corresponding histology images. Aim: We aim to explore the effectiveness of an automated image registration technique based on deep learning principles for aligning breast specimen images with histology images acquired through different modalities, addressing challenges posed by intensity variations and structural differences. Approach: Unsupervised and supervised learning approaches, employing the VoxelMorph model, were examined using a dataset featuring manually registered images as ground truth. Results: Evaluation metrics, including Dice scores and mutual information, demonstrate that the unsupervised model exceeds the supervised (and manual) approaches significantly, achieving superior image alignment. The findings highlight the efficacy of automated registration in enhancing the validation of optical technologies by reducing human errors associated with manual registration processes. Conclusions: This automated registration technique offers promising potential to enhance the validation of optical technologies by minimizing human-induced errors and inconsistencies associated with manual image registration processes, thereby improving the accuracy of correlating optical measurements with pathology labels.

关键词： multi-modal image registration histology deformations validation registration algorithm optical techniques

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registration scheme suitable to Mueller matrix imaging for biomedical applications

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OPTICS EXPRESS 2007年第12期15卷 7393-7400页

作者： Guyot, Steve Anastasiadou, Makrina Delechelle, Eric De Martino, Antonello Ecole Polytech CNRS LPICM F-91120 Palaiseau France Univ Paris 12 LiSSi EA 3956 F-94010 Creteil France

Most Mueller matrix imaging polarimeters implement sequential acquisition of at least 16 raw images of the same object with different incident and detected light polarizations. When this technique is implemented in vivo, the unavoidable motions of the subject may shift and distort the raw images to an extent such that the final Mueller images cannot be extracted. We describe a registration algorithm which solves this problem for the typical conditions of in vivo imaging, e.g. with spatially inhomogeneous medium to strong depolarization. The algorithm, based on the so called "optical flow", is validated experimentally by comparing the Mueller images of a pig skin sample taken in static and in dynamic conditions. (c) 2007 Optical Society of America

关键词： .sequential Pig Acquisition optical flow polarimetry registration algorithm polarized light Depolarizing polarimetric polarizations Mueller matrix biomedical applications raw images skin sample

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