Multiple Registration Research Articles

Accurate reconstruction of turbine blade point cloud and multiple point cloud registration based on structured light

In recent years, the industry has increasingly adopted vision-guided laser processing techniques to machine cooling holes in turbine blades, placing higher demands on the accuracy of visual measurement. Therefore, to meet the need for high-precision measurement of the three-dimensional shape of turbine blades in the context of laser processing of film cooling holes, this paper proposes a structured light-based method for precise reconstruction of turbine blade point clouds and multi-point cloud registration. Specifically, to improve the accuracy of point cloud reconstruction, an improved phase matching and reconstruction method based on epipolar constraints is proposed. To enhance the accuracy and stability of multi-point cloud registration, a weighted iterative closest point (ICP) method combined with a mark point registration strategy is proposed, optimizing the stability of the ICP algorithm and achieving high-quality multiple point cloud stitching. Experimental results validate the effectiveness and accuracy of the proposed method.

Coarse-to-fine hybrid network for robust medical image registration in the presence of large deformations

Medical image registration is a fundamental and core technology in the field of medical image processing and analysis. In recent years, deep learning-based registration methods have made significant progress and gradually replaced traditional registration methods. However, many existing methods still face challenges when dealing with large deformations caused by significant structural differences or topological changes. To address this issue, we propose a competitive hybrid registration network. Specifically, our network combines the advantages of Vision Transformer (ViT) and Convolutional Neural Network (CNN) in feature extraction, allowing it to enhance long-range dependencies between images while preserving the integrity of local features. Furthermore, we have developed a novel coarse-to-fine registration strategy in which the deformation fields (DFs) are refined dynamically to obtain more accurate and robust estimation. We extensively evaluated our method on multiple registration tasks, and the experiments demonstrate that our approach outperforms other deep learning methods in terms of accuracy, robustness, and generalization.

Laser in-situ measurement in robotic machining of large-area complex parts

High-precision manufacturing of key components in aerospace plays a crucial role in determining their operational performance, and shape measurement during machining is essential for controlling component accuracy. This article proposes a laser in-situ high-precision measurement method based on improved ICP registration during robotic machining to resolve the contradiction between measurement accuracy and range. Firstly, a robotic in-situ measurement system based on line laser scanning is constructed. Subsequently, the FM-BiKD-ICP algorithm based on geometric feature matching and bi-directional KD-tree proposed in this article is used to complete the high-precision registration of multi-million-scale point cloud data of complex parts with large surface area. Finally, the standard ball target ruler is measured, with the RMSE is under 0.079 mm after multiple registrations, and the average error of hole center distance is less than 0.037 mm on large-area complex parts. These results indicate the high precision of the proposed robot in-situ measurement system.

Talar head fractures: An observational study of 33 talar head fractures derived from the Swedish Fracture Register

BackgroundReports in the literature on talar head fractures are rare and limited to case reports and small case series. Questions/PurposesThis study aimed to describe a national cohort of talar head fractures for fracture characteristics, associated injuries, treatment, and reoperations. MethodsWe reviewed all radiographs of patients enrolled in the SFR between 2011 and 2021 showing talar head fractures (AO/OTA 81-A3). We excluded those with talar head avulsion fractures, misclassified, multiple registrations, or with a privacy notice in their medical records. The cohort was reviewed using medical records and radiographs at a minimum 2-year follow-up. ResultsThe study included 32 patients (33 fractures) ≥18 years of age. The median age was 32 (range 18–65) years, 84 % were men, and the main trauma mechanisms were motor vehicle accidents (28 %) and falls from heights (28 %) and graded as high-energy injuries in 50 % of the cases. 18 (55 %) were displaced and 15 (45 %) had comminuted fractures. 14 talar head fractures (42 %) had multiple injuries to the same foot. 21 patients (66 %) underwent surgical treatment, most commonly with screw fixation. Surgery was performed in 15 of 18 patients with displaced fractures. Four patients were reoperated, one with arthrodesis of the talonavicular joint and three for implant removal. No cases of avascular necrosis were identified. ConclusionsTalar head fractures are rare and occur mainly in men. They are associated with other foot injuries. Most talar head fractures are treated operatively. In this case series, we did not identify any case of avascular necrosis. Levels of EvidenceIV, retrospective observational cohort study

Health in children during schooling

Introduction. The basis of preventive activities in pediatrics is medical examination, based on active dynamic monitoring of sick and healthy children. An important part of medical examination is preventive examinations, which are carried out with the aim of timely detection of deviations in the health status in children and the management of necessary treatment, preventive and health measures. Purpose of the study: based on the results of a dynamic 10-year observation, present the data on the health status of a single cohort of schoolchildren to update the main preventive areas for children’s practice over schooling process. Materials and methods. The study was conducted on the basis of a study of the health status of four hundred thirty seven schoolchildren at different stages of schooling (before entering school, at the end of first grade, during the transition to subject education and at the end of school education), according to a single protocol, on one sample and a single methodology with sequential multiple registration of selected indices of the health status in schoolchildren and annual analysis over a period of 10 years. Results. An analysis of the distribution of those examined cases by membership in health groups clearly demonstrated a decrease in the number of children in health groups I and II and an increase in schoolchildren in health groups III and IV. A dynamic assessment of diagnosed pathological conditions in their compliance with a certain ICD-10 class over a 10-year observation period revealed an increase in the proportion of schoolchildren suffering from diseases of the musculoskeletal system, the organ of vision, cardio- and endocrinopathies. Research limitations.The study has regional (Krasnoyarsk Territory) limitations and concerns 6 to 17 year children. Conclusion. The data obtained established the possibilities of early diagnosis in risk groups, and preventive measures aimed at developing healthy lifestyle skills in schoolchildren and their parents within the framework of priority national projects in Russia.

Linearity Study of Color Measurement Methods in Digital Image Colorimetry

Color metrology is steadily developing in the interests of fundamental and applied science, improving industrial production processes. The author considers the problem of ensuring metrological traceability of color measurements under natural conditions.Digital image colorimetry methods demonstrate good linearity and precision in relatively small application ranges in studies of same type and mainly non-luminous objects. For complex scenes with a wide range of brightnesses, nonlinear functional color rendering models are used that are focused on visual perception, peak brightness of the monitor and reference viewing conditions, which complicates the processing of measurement results and reduces a confidence interval.The described method of measuring the chromaticity coordinates of an object by its digital image is based on tensor calculus and consists of multiple registration of an object and sets of reference samples with different exposure times, building calibration dependencies forming a multidimensional colorimetric scale in color space and determining chromaticity coordinates as points of intersection of color tensors with the plane of the color locus. In order to ensure accuracy and reliability of the measurement results in the software and hardware environments, a validation study of the performance indicators of this method was carried out, one of which is linearity. An experiment based on comparing the developed method with the reference measurement method showed the effectiveness of the developed wide-range measurement method with good linearity (correlation coefficient R=0.97… 0.99, residual relative standard deviation s0≈1–2%) when adjusting from the subjective factor and display device.The experimental materials may be useful to metrologists solving problems of determining the metrological traceability of the color of digital images in media. The article will be of interest to a wide range of readers with professional interests related to the colorimetry of digital images.

Learnable Graph Matching: A Practical Paradigm for Data Association.

Data association is at the core of many computer vision tasks, e.g., multiple object tracking, image matching, and point cloud registration. however, current data association solutions have some defects: they mostly ignore the intra-view context information; besides, they either train deep association models in an end-to-end way and hardly utilize the advantage of optimization-based assignment methods, or only use an off-the-shelf neural network to extract features. In this paper, we propose a general learnable graph matching method to address these issues. Especially, we model the intra-view relationships as an undirected graph. Then data association turns into a general graph matching problem between graphs. Furthermore, to make optimization end-to-end differentiable, we relax the original graph matching problem into continuous quadratic programming and then incorporate training into a deep graph neural network with KKT conditions and implicit function theorem. In MOT task, our method achieves state-of-the-art performance on several MOT datasets. For image matching, our method outperforms state-of-the-art methods on a popular indoor dataset, ScanNet. For point cloud registration, we also achieve competitive results.

SGRTmreg: A Learning-Based Optimization Framework for Multiple Pairwise Registrations.

Point cloud registration is a fundamental task in computer vision and graphics, which is widely used in 3D reconstruction, object tracking, and atlas reconstruction. Learning-based optimization and deep learning methods have been widely developed in pairwise registration due to their own distinctive advantages. Deep learning methods offer greater flexibility and enable registering unseen point clouds that are not trained. Learning-based optimization methods exhibit enhanced robustness and stability when handling registration under various perturbations, such as noise, outliers, and occlusions. To leverage the strengths of both approaches to achieve a less time-consuming, robust, and stable registration for multiple instances, we propose a novel computational framework called SGRTmreg for multiple pairwise registrations in this paper. The SGRTmreg framework utilizes three components-a Searching scheme, a learning-based optimization method called Graph-based Reweighted discriminative optimization (GRDO), and a Transfer module to achieve multi-instance point cloud registration.Given a collection of instances to be matched, a template as a target point cloud, and an instance as a source point cloud, the searching scheme selects one point cloud from the collection that closely resembles the source. GRDO then learns a sequence of regressors by aligning the source to the target, while the transfer module stores and applies the learned regressors to align the selected point cloud to the target and estimate the transformation of the selected point cloud. In short, SGRTmreg harnesses a shared sequence of regressors to register multiple point clouds to a target point cloud. We conduct extensive registration experiments on various datasets to evaluate the proposed framework. The experimental results demonstrate that SGRTmreg achieves multiple pairwise registrations with higher accuracy, robustness, and stability than the state-of-the-art deep learning and traditional registration methods.

Delineating excess comorbidities in idiopathic pulmonary fibrosis: an observational study

BackgroundOur study examined whether prevalent and incident comorbidities are increased in idiopathic pulmonary fibrosis (IPF) patients when compared to matched chronic obstructive pulmonary disease (COPD) patients and control subjects without IPF or COPD.MethodsIPF and age, gender and smoking matched COPD patients, diagnosed between 01/01/1997 and 01/01/2019 were identified from the Clinical Practice Research Datalink GOLD database multiple registrations cohort at the first date an ICD-10 or read code mentioned IPF/COPD. A control cohort comprised age, gender and pack-year smoking matched subjects without IPF or COPD. Prevalent (prior to IPF/COPD diagnosis) and incident (after IPF/COPD diagnosis) comorbidities were examined. Group differences were estimated using a t-test. Mortality relationships were examined using multivariable Cox proportional hazards adjusted for patient age, gender and smoking status.ResultsAcross 3055 IPF patients, 38% had 3 or more prevalent comorbidities versus 32% of COPD patients and 21% of matched control subjects. Survival time reduced as the number of comorbidities in an individual increased (p < 0.0001).In IPF, prevalent heart failure (Hazard ratio [HR] = 1.62, 95% Confidence Interval [CI]: 1.43–1.84, p < 0.001), chronic kidney disease (HR = 1.27, 95%CI: 1.10–1.47, p = 0.001), cerebrovascular disease (HR = 1.18, 95%CI: 1.02–1.35, p = 0.02), abdominal and peripheral vascular disease (HR = 1.29, 95%CI: 1.09–1.50, p = 0.003) independently associated with reduced survival. Key comorbidities showed increased incidence in IPF (versus COPD) 7–10 years prior to IPF diagnosis.InterpretationThe mortality impact of excessive prevalent comorbidities in IPF versus COPD and smoking matched controls suggests that multiorgan mechanisms of injury need elucidation in patients that develop IPF.

Practical Multi-Mesh Registration for Few-View Poly-Chromatic X-Ray Inspection

Accurate 3D mesh registration is essential in many industrial applications of X-ray imaging, as it allows quality assessment and inspection of manufactured objects. Conventional methods rely mainly on time-consuming and expensive X-ray computed tomography (X-CT) or ancillary camera systems. Instead, we propose a novel approach for efficient 3D multi-mesh registration in few-view industrial X-ray imaging scenarios. Our approach harnesses the capabilities of CAD-ASTRA, an X-ray mesh projector, compatible with the ASTRA toolbox and popular GPU libraries such as CuPy and PyTorch, for the simulation of X-ray projec tions from a known object surface mesh. As a differentiable program, CAD-ASTRA allows iterative improvement of the objects’ position in space by back-propagation of a differentiable measure of the projection error. The potential of this approach is demonstrated through tests on simultaneous multiple object registration in a poly-chromatic imaging, even in cases where the spectral characteristics of the imaging system are unknown. Results from a diverse set of real experiments highlight the efficacy of mesh registration, achieving successful registrations even when only two projections at a 10∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\\circ $$\\end{document} angle relative to the scanning system center are available. The mesh projector facilitates resource-efficient registration in industrial applications with few viewpoints, thereby reducing the demand for resources and eliminating the need for X-CT reconstruction.

Present and Future Challenges of the Special VAT Scheme for Travel Agents

The special scheme for travel agents and tour operators was designed as a simplification measure to avoid multiple registration obligations for businesses in this sector and to allocate VAT revenues to the Member State of consumption. However, the scheme raises a number of practical difficulties, largely due to the different interpretations of the scheme in the Member States, which makes it difficult for taxable persons to manage. In this article, the author discusses some of these practical difficulties and points out what he believes may be possible future solutions.

Design and Implementation of a Secured, Real-Time Internet-based Voting System

The Internet of Voting system presented in this paper represents a modern solution to the challenges faced by the traditional electoral processes. In an era characterized by technological advancements, this system harnesses digital innovation to enhance the efficiency, security, and accessibility of elections. Evolving from a rich history of voting systems, this digital platform integrates several influential works and leverages cutting-edge technology to create a user-friendly interface with robust security measures. The system addresses critical issues that have plagued traditional paper and electronic ballots, such as voter confusion and fraud, by implementing stringent encryption techniques, password renewal mechanisms, and real-time prevention of duplicated votes. It enables the elimination of multiple registrations, streamlining the voter registration process. Online voting systems have gained prominence as a means of conducting elections efficiently, reducing logistical challenges, and increasing voter accessibility. The system offers three distinct categories for voting: Regional Elections, Local Elections, and Presidential Elections, each with its own set of independent candidates.

The Dresden in vivo OCT dataset for automatic middle ear segmentation

Endoscopic optical coherence tomography (OCT) offers a non-invasive approach to perform the morphological and functional assessment of the middle ear in vivo. However, interpreting such OCT images is challenging and time-consuming due to the shadowing of preceding structures. Deep neural networks have emerged as a promising tool to enhance this process in multiple aspects, including segmentation, classification, and registration. Nevertheless, the scarcity of annotated datasets of OCT middle ear images poses a significant hurdle to the performance of neural networks. We introduce the Dresden in vivo OCT Dataset of the Middle Ear (DIOME) featuring 43 OCT volumes from both healthy and pathological middle ears of 29 subjects. DIOME provides semantic segmentations of five crucial anatomical structures (tympanic membrane, malleus, incus, stapes and promontory), and sparse landmarks delineating the salient features of the structures. The availability of these data facilitates the training and evaluation of algorithms regarding various analysis tasks with middle ear OCT images, e.g. diagnostics.

Evolutionary Multiform Optimization With Two-Stage Bidirectional Knowledge Transfer Strategy for Point Cloud Registration

Point cloud registration is an important task in computer vision, where the goal is to estimate a transformation to align a pair of point clouds. Most of the existing registration methods face the problems of poor robustness and getting stuck in local optima. Evolutionary multitasking is an effective paradigm to enhance global search capability and improve convergence characteristics through knowledge transfer across multiple related tasks. Inspired by evolutionary multitasking, this paper proposes a multiform optimization approach through evolutionary multitasking for solving the point cloud registration problems. we first construct two related registration tasks with different functional landscapes to form a multiform optimization problem. Compared with methods that only focus on a single registration attribute, the two proposed tasks focus on robustness and precision of registration, respectively. Then, a new two-stage bidirectional knowledge transfer strategy is presented, which can implement efficient knowledge transfer among two related tasks. Finally, both simulations and real experiments show the power of our method. The proposed method is robust to noise, outliers, and partial overlaps, and is effective in multiple real registration scenarios, such as object registration, scene reconstruction, simultaneous localization and mapping.

Proposed Online Voters’ Registration Automated System in Election

Voters’ registration is a process which qualifies a citizen to vote and also be voted for in any given election. A list of voter is generated from the voter registration called voter’s list. The manual voters register maintained in most of the under developed countries is causing a lot of electoral malpractices. The proposed system will maintain a central database for all registered voters, which will include their pictures and voters ID to guide against having multiple registrations. This study examined electoral act: national register of voters and qualification for registration. Appropriate conclusion was made.

A Revised Approach to Orthodontic Treatment Monitoring From Oralscan Video.

Research on orthodontic treatment monitoring from oralscan video is a new direction in dental digitalization. We designed an approach to reconstruct, segment, and estimate the pose of individual teeth to measure orthodontic treatment. To handle the semantic gap in heterogeneous data on the condition that they are combined linearly, we present a multimedia interaction network (MIN) to combine heterogeneous information in point cloud segmentation by extending the graph attention mechanism. Moreover, a structure-aware quadruple loss is designed to explore the relation between multiple and diverse unmatched points in point cloud registration. The performance of our approach is evaluated on multiple tooth registration datasets, and extensive experiments show that our approach improves the accuracy by a margin of 1.4% in the inlier ratio on the Aoralscan3 dataset when it is compared with prevailing approaches.

Harmonization of Geographical Indication (GI) Registration: A Case Study of Doi Tung Coffee

Coffee is one of the world’s most valuable commodities. However, both the refining procedure and the growing environment make each source of coffee unique. Recognizing this, the concept of geographical indication has emerged as a vital legal instrument to safeguard the intellectual property rights associated with specific products. Since this protection is jurisdiction-based, each nation can customize its own safeguarding measures and registration procedures to its internal policies and preferences, resulting in differing costs to producers. Multiple registrations in different jurisdictions are required for obtaining protection under the geographical indication realm, consequently creating a complex landscape to certain producers. This article aims to delve into the issues surrounding multiple registrations by examining the experiences of Doi Tung Coffee’s registrations in Thailand, the European Union, and Japan. Furthermore, harmonization of registration is proposed as a potential solution. By streamlining the registration process across territories, the harmonization approach seeks to minimize the costs associated with multiple registrations and ultimately bolster the competitive edge of producers in the global market. It also underscores the significance of providing adequate protection to promote the growth and success of coffee producers worldwide.

Registration between 2D and 3D Ultrasound Images to Track Liver Blood Vessel Movement.

For the accurate positioning of surgical tools, conventional intraoperative navigation systems have been developed to recognize the relationship between target positions and the tools. However, since an internal organ is deformed during the operation, registration between realtime two-dimensional (2D) ultrasound images and three-dimensional (3D) CT or MRI images is not always effective. Therefore, this study developed image registration between 2D and 3D ultrasound images considering deformation for tracking target vessel movement in the liver. 3D ultrasound image was obtained in advance with 3D coordinates, including the target vessel. Then real-time 2D images and ultrasound probe position were simultaneously acquired using a 3D position sensor. We applied multiple image resolution registration, where rapid and fine optimizations can be expected at higher and lower levels, respectively. Meanwhile, the gradient descent method was adopted for the optimization, which determines the relative arrangements to obtain maximum similarity between 2D and 3D images. We experimentally established resolution level parameters using a phantom before applying it to track liver blood vessel movements in a normal healthy subject. Comparing the 2D images and the registered images, although the approach has some limitations in tracking large displacement, we confirmed that the cross-section of the target blood vessel was clearly visualized. This method has the potential for an ultrasound therapy targeting blood vessels under natural respiration conditions.

Benchmark of multi-view Terrestrial Laser Scanning Point Cloud data registration algorithms

This study addresses multi-view Terrestrial Laser Scanning Point Cloud data registration methods. Multiple rigid point cloud data registration is mandatory for aligning all scans into a common reference frame and it is still considered a challenge looking from a large-scale surveys point of view. The goal of this work is to support the development of cutting-edge registration methods in geoscience and mobile robotics domains. This work evaluates 3 data sets of total 20 scenes available in the literature. This paper provides a novel open-source framework for multi-view Terrestrial Laser Scanning Point Cloud data registration benchmarks. The goal was to verify experimentally which registration variant can improve the open-source data looking from the quantitative and qualitative points of view. In particular, the following scanners provided measurement data: Z+F TLS Imager 5006i, Z+F TLS Imager 5010C, Leica ScanStation C5, Leica ScanStation C10, Leica P40 and Riegl VZ-400. The benchmark shows an impact of the metric e.g. point to point, point to projection onto a plane, plane to plane etc..., rotation matrix parameterization (Tait–Bryan, quaternion, Rodrigues) and other implementation variations (e.g. multi-view Normal Distributions Transform, Pose Graph SLAM approach) onto the multi-view data registration accuracy and performance. An open-source project is created and it can be used for improving existing data sets reported in the literature, it is the added value of the presented research. The combination of metrics, rotation matrix parameterization and optimization algorithms creates hundreds of possible approaches. It is shown that chosen metric is a dominant factor in data registration. The rotation parameterization and other degrees of freedom of proposed variants are rather negligible compared with chosen metric. Most of the proposed approaches improve registered reference data provided by other researchers. Only for 2 from 20 scenes it was not possible to provide significant improvement. The largest improvements are evident for large-scale scenes. The project is available and maintained at https://github.com/MapsHD/HDMapping.

Enabling action: Re-envisaging education of health professionals in Aotearoa New Zealand

Healthcare in Aotearoa New Zealand is changing with the aim of becoming truly universal. Development of a new curriculum model in the education of health professionals can aid this goal through increased focus on community needs and flexibility for multiple health professional registrations. Universal healthcare and disability support are promoted as a defining feature of Aotearoa New Zealand, yet inequities are blatantly evident, with increasing calls for equity. Complexity of the issue and the multitude of stakeholders favour action research founded on Vision Matāuranga, a problem-solving philosophy which embraces the potential for innovation of Māori – people, knowledge and resources. Action research embraces collaboration with stakeholders to identify and implement solutions. Government ministries determine policy, with Responsible Authorities accrediting educational institutions, which in turn provide educational programs. Changing what and how students learn can increase their understanding of equity and community needs when they become practitioners, with the voice of service users being paramount.