Automatic Alignment Research Articles

Automatic Multiview Alignment of RGB-D Range Maps of Upper Limb Anatomy.

Digital representations of anatomical parts are crucial for various biomedical applications. This paper presents an automatic alignment procedure for creating accurate 3D models of upper limb anatomy using a low-cost handheld 3D scanner. The goal is to overcome the challenges associated with forearm 3D scanning, such as needing multiple views, stability requirements, and optical undercuts. While bulky and expensive multi-camera systems have been used in previous research, this study explores the feasibility of using multiple consumer RGB-D sensors for scanning human anatomies. The proposed scanner comprises three Intel® RealSenseTM D415 depth cameras assembled on a lightweight circular jig, enabling simultaneous acquisition from three viewpoints. To achieve automatic alignment, the paper introduces a procedure that extracts common key points between acquisitions deriving from different scanner poses. Relevant hand key points are detected using a neural network, which works on the RGB images captured by the depth cameras. A set of forearm key points is meanwhile identified by processing the acquired data through a specifically developed algorithm that seeks the forearm's skeleton line. The alignment process involves automatic, rough 3D alignment and fine registration using an iterative-closest-point (ICP) algorithm expressly developed for this application. The proposed method was tested on forearm scans and compared the results obtained by a manual coarse alignment followed by an ICP algorithm for fine registration using commercial software. Deviations below 5 mm, with a mean value of 1.5 mm, were found. The obtained results are critically discussed and compared with the available implementations of published methods. The results demonstrate significant improvements to the state of the art and the potential of the proposed approach to accelerate the acquisition process and automatically register point clouds from different scanner poses without the intervention of skilled operators. This study contributes to developing effective upper limb rehabilitation frameworks and personalized biomedical applications by addressing these critical challenges.

MULTI-SATELLITE IMAGE ALIGNMENT OVER LARGE AREAS WITH FEATURELESS REGIONS

Abstract. There is a great deal of interest in fusing together the information provided by different satellites for real-time change detection on the surface of the earth. For detecting important types of changes on the ground, it is necessary to inject geometry into the data provided by low-res satellites using multi-view imaging satellites that record each point on the ground from multiple perspectives. Combining these perspectives and generating a DSM (Digital Surface Model) gives us the geometry needed for a more meaningful analysis of satellite data. Before such an analysis can be carried out, it is necessary to align the images from all available satellites. Automatic image alignment, however, requires features on the ground that can be identified and correctly matched across different images using computer vision algorithms. While such features are common in urban areas, that is not always the case in predominantly rural areas that present a more-or-less uniform texture to the sensors. In this paper we present methods for automatic identification and alignment of featureless regions. Featureless regions are identified using point spread maps, which are a byproduct of DSM generation. The subsequent strategy for aligning featureless regions depends on the proportion of featureless regions to feature-rich regions. If most of the AOI (Area of Interest) is feature-rich, we ignore featureless regions when estimating inter-satellite image alignment parameters and apply those parameters to the entire AOI. Finally, we present a technique to propagate and fuse parameters from feature-rich regions to featureless regions.

Robotic Platform for Automatic Alignment and Placement of Fabric Patterns for Smart Manufacturing in Garment Industry

Robotic Platform for Automatic Alignment and Placement of Fabric Patterns for Smart Manufacturing in Garment Industry

Design of a real-time automatic resonator alignment method for unstable resonators

The auto-alignment method for unstable resonators proposed in this paper innovatively replaces the secondary mirror of traditional unstable resonators with a double-convex lens coated with a high reflective film on one side, and installs the primary mirror on a designed electrically adjustable bracket. During the laser emission process, the computer calculates the relative displacement of the centroid of the far-field spot on the camera detection surface to determine the magnitude of the resonator misalignment, and simultaneously controls the electrically adjustable bracket to complete the compensation for the resonator misalignment. Finally, the experimental results show that when using the real-time automatic resonator alignment method, the output power of the laser remains very stable with a relative standard deviation of the normalized power less than 1.1% (if the auto-alignment method is not adopted, the output power will decay to 89.8% of the peak value). Therefore, this method can effectively solve the resonator misalignment problem of unstable resonator lasers. In addition, this method has the advantages of simple optical path structure and high detection speed while ensuring high accuracy.

Development and validation of a specialized system for self-tapping medical bone screw testing.

A specialized system for accurate self-tapping medical bone screw testing is developed, fully meeting the requirements of ASTM F543-A4 (YY/T 1505-2016). The onset of self-tap is identified automatically according to a change in the slope of the torque curve. Precise load control is applied to determine the self-tapping force accurately. A simple mechanical platform is embedded to ensure the automatic axial alignment of a tested screw with the pilot hole in a test block. In addition, comparative experiments are conducted on different self-tapping screws to verify the system's effectiveness. By the automatic identification and alignment method, both torque curves and axial force curves for each screw exhibit significant consistency. The self-tapping time point derived from the torque curve agrees well with the turning point of the axial displacement curve. The determined self-tapping forces' mean values and standard deviations are both small, which are proved to be effective and accurate in the insertion tests. This work contributes to improving the standard test method for accurate determination of the self-tapping performance of medical bone screws.

Computer-assisted pre-operative automatic segmentation and registration tool for malunited radius osteotomy: A proof-of-concept study

Corrective osteotomy is a standard treatment for distal radius fractures in malunited radius cases. In order to increase the efficiency of the osteotomy pre-operative plan, in this study, a proof-of-concept framework of automatic computer-assisted segmentation and registration tool was developed for the purpose of malunited radius osteotomy pre-operative planning. The program consisted of the functions of segmentation, virtual cutting, automatic alignment and registration. One computed tomography (CT) scanning dataset of a patient's bilateral forearm was employed as an illustration example in this study. Three templates of 3D models including the healthy radius, and the pre- and post-correction injured radius were output as STL geometries for pre-operative plan purposes.

Automatic wheel Alignment Detector with Alarm System

Automatic wheel Alignment Detector with Alarm System

Accurate volume alignment of arbitrarily oriented tibiae based on a mutual attention network for osteoarthritis analysis.

Damage to cartilage is an important indicator of osteoarthritis progression, but manual extraction of cartilage morphology is time-consuming and prone to error. To address this, we hypothesize that automatic labeling of cartilage can be achieved through the comparison of contrasted and non-contrasted Computer Tomography (CT). However, this is non-trivial as the pre-clinical volumes are at arbitrary starting poses due to the lack of standardized acquisition protocols. Thus, we propose an annotation-free deep learning method, D-net, for accurate and automatic alignment of pre- and post-contrasted cartilage CT volumes. D-Net is based on a novel mutual attention network structure to capture large-range translation and full-range rotation without the need for a prior pose template. CT volumes of mice tibiae are used for validation, with synthetic transformation for training and tested with real pre- and post-contrasted CT volumes. Analysis of Variance (ANOVA) was used to compare the different network structures. Our proposed method, D-net, achieves a Dice coefficient of 0.87, and significantly outperforms other state-of-the-art deep learning models, in the real-world alignment of 50 pairs of pre- and post-contrasted CT volumes when cascaded as a multi-stage network.

Automatic Alignment Method of Underwater Charging Platform Based on Monocular Vision Recognition

To enhance the crypticity and operational efficiency of unmanned underwater vehicle (UUV) charging, we propose an automatic alignment method for an underwater charging platform based on monocular vision recognition. This method accurately identifies the UUV number and guides the charging stake to smoothly insert into the charging port of the UUV through target recognition. To decode the UUV’s identity information, even in challenging imaging conditions, an encryption encoding method containing redundant information and an ArUco code reconstruction method are proposed. To address the challenge of underwater target location determination, a target location determination method was proposed based on deep learning and the law of refraction. The method can determine the two-dimensional coordinates of the target location underwater using the UUV target spray position. To meet the real-time control requirements and the harsh underwater imaging environment, we proposed a target recognition algorithm to guide the charging platform towards the target direction. The practical underwater alignment experiments demonstrate the method’s strong real-time performance and its adaptability to underwater environments. The final alignment error is approximately 0.5548 mm, meeting the required alignment accuracy and ensuring successful alignment.

A New Digital Method to Quantify the Effects Produced by Carriere Motion Appliance.

The aim of this study was to analyze a novel digital technique to quantify the distal tooth displacement and derotation angle produced by the Carriere Motion Appliance (CMA). Twenty-one patients with a class II molar and canine relationship underwent orthodontic treatment with CMA. All patients were exposed before (STL1) and after the CMA placement (STL2), submitted to a digital impression, and afterwards, data were uploaded to specific cephalometric software to allow automatic mesh network alignment of the STL digital files. Subsequently, the distal tooth displacement of the upper canines and first upper molars, as well as the derotation angle of the first upper molars were analyzed using the Pearson correlation coefficient (ρ). Repeatability and reproducibility were analyzed using Gage R&R statistical analysis. An increase in canine displacement was correlated with an increase in contralateral canine displacement (ρ = 0.759; p < 0.000). An increase in canine displacement was correlated with an increase in molar displacement (ρ = 0.715; p < 0.001). An increase in upper first molar displacement was correlated with an increase in the contralateral upper first molar displacement (ρ = 0.609; p < 0.003) and the canine displacement (ρ = 0.728; p < 0.001). The distal tooth displacement showed a repeatability of 0.62% and reproducibility of 7.49%, and the derotation angle showed a repeatability of 0.30% and reproducibility of 0.12%. The novel digital measurement technique is a reproducible, repeatable, and accurate method for quantifying the distal tooth displacement of the upper canine and first upper molar, as well as the derotation angle of the first upper molars after using CMA.

Fullerene-Based Nanosurfactant for the Automatic Homeotropic Alignment of Liquid Crystals

Fullerene-Based Nanosurfactant for the Automatic Homeotropic Alignment of Liquid Crystals

Three-Dimensional Evaluation of Skeletal Stability following Surgery-First Orthognathic Approach: Validation of a Simple and Effective Method

Background The three-dimensional (3D) evaluation of skeletal stability after orthognathic surgery is a time-consuming and complex procedure. The complexity increases further when evaluating the surgery-first orthognathic approach (SFOA). Herein, we propose and validate a simple time-saving method of 3D analysis using a single software, demonstrating high accuracy and repeatability. Methods This retrospective cohort study included 12 patients with skeletal class 3 malocclusion who underwent bimaxillary surgery without any presurgical orthodontics. Computed tomography (CT)/cone-beam CT images of each patient were obtained at three different time points (preoperation [T0], immediately postoperation [T1], and 1 year after surgery [T2]) and reconstructed into 3D images. After automatic surface-based alignment of the three models based on the anterior cranial base, five easily located anatomical landmarks were defined to each model. A set of angular and linear measurements were automatically calculated and used to define the amount of movement (T1-T0) and the amount of relapse (T2-T1). To evaluate the reproducibility, two independent observers processed all the cases, One of them repeated the steps after 2 weeks to assess intraobserver variability. Intraclass correlation coefficients (ICCs) were calculated at a 95% confidence interval. Time required for evaluating each case was recorded. Results Both the intra- and interobserver variability showed high ICC values (more than 0.95) with low measurement variations (mean linear variations: 0.18 mm; mean angular variations: 0.25 degree). Time needed for the evaluation process ranged from 3 to 5 minutes. Conclusion This approach is time-saving, semiautomatic, and easy to learn and can be used to effectively evaluate stability after SFOA.

Automatic Alignment of Fractured Femur: Integration of Robot and Optical Tracking System

This paper presents Robossis, a surgical robotic system for automated femur fracture alignment. Robossis is the integration of an optical tracking system with a 6-degree-of-freedom (6-DOF) 3-armed parallel robot that satisfies the clinical and mechanical design requirements for femur alignment surgery. In real-time, the optical tracking system obtains the spatial position of the distal and proximal parts of the fractured femur. Then, an auto-alignment algorithm uses this data to guide the robot to automatically and accurately align the bone fragments while overcoming the muscle payload surrounding the femur. A graphical user interface (GUI) provides a real-time visual guide for the surgeon to check the deviation from alignment while the robot is automatically bringing the bone into an aligned position. To demonstrate the capabilities of Robossis, laboratory and cadaver experiments are performed, showing that Robossis can successfully manipulate bone in 6 DOFs in space, achieving alignment with submillimeter accuracy.

Efficacy and volume stability of a customized allogeneic bone block for the reconstruction of advanced alveolar ridge deficiencies at the anterior maxillary region: a retrospective radiographic evaluation

ObjectivesThe aim of this retrospective case series was to evaluate the efficacy and volume stability of a customized allogeneic bone block (CABB) for the hard tissue reconstruction of severely atrophied anterior maxillary ridges.Materials and methodsHard tissue alterations between baseline (T1), 2-month follow-up (T2), and 6-month follow-up (T3) cone-beam computed tomography scans were evaluated with semi-automatic segmentation. Following automatic spatial alignment of the datasets, 3D subtraction analysis was performed. The volume stability of the inserted allogeneic bone block was determined on the basis of the ratio of the T3 and T2 hard tissue volumes.ResultsThe newly formed hard tissue volume at T2 averaged at of 0.75 cm3 ± 0.57 cm3, whereas at T3, an average of 0.52 cm3 ± 0.42 cm3 volumetric hard tissue gain could be detected. The T3/T2 ratio was found to be 67.83% ± 18.72% on average. The dice similarity coefficient between the T2 and T3 hard tissue models averaged at 0.73 ± 0.15.ConclusionsCancellous CABBs are a reliable option for the reconstruction of severely atrophied alveolar ridges. The resorption rates of these grafts are similar to those found in the literature; however, with precise manufacturing and proper intraoperative flap management, the resorption rates may be reduced.Clinical relevanceWith precise knowledge of the resorption patterns, the shape of blocks can be altered in the future to compensate for the volumetric loss.

An automatic alignment of the business process and business value models: a novel MDA method

With the massive development of end-users requirements, the model alignment has become an essential stage in software engineering, especially in the model driven architecture (MDA) approach, to absorb the end-user’s need. The purpose is to facilitate the alignment of new models from existing ones. Our contribution in this paper is to deal with the MDA higher abstraction lever by focusing on the automatic alignment of the business value with the business process models for the information system (IS). For our case, the data-flow diagram (DFD) illustrates the business process model, and the E&lt;sup&gt;3&lt;/sup&gt;value model illustrates the business value model. However, the ATLAS-transformation language (ATL) ensures automatic alignment. The main goal is to facilitate and accelerate IS implementation while enhancing its quality.

An Artificial Intelligence-Aided Robotic Platform for Ultrasound-Guided Transcarotid Revascularization

Transcarotid Artery Revascularization (TCAR) is typically performed by manual catheter insertion and implies radiation exposure for both the patient and the surgeon. Taking advantage from robotics and artificial intelligence (AI), this letter presents a robotic ultrasound (RUS) platform for improving the procedure. To this purpose, ultrasound (US) imaging is considered both in the pre-operative stage for procedure planning and in the intra-operative stage to track a catheter. 3D vascular volumes can be precisely reconstructed from sequences of 2D images exploiting robotic probe manipulation and AI-based image analysis. The method proved a median reconstruction error lower than 1 mm. Pre-operative information are mapped to the intra-operative scenario thanks to a US-based registration routine. The automatic probe alignment on the target vessel demonstrated to be as precise as 0.84°. The reconstructed 3D model can be exploited to automatically generate a catheter trajectory based on user inputs. Such trajectory enabled automatic insertion of a magnetic catheter steered by an external permanent magnet actuated by the RUS platform. Our results demonstrate a catheter tip target reaching error of 3.3 mm. We believe that these results can open the way for the introduction of robotics and AI in TCAR procedures enabling precise and automatic small-scale intravascular devices control.

Artificial intelligence and augmented reality for guided implant surgery planning: A proof of concept

PurposeTo present a novel protocol for authentic three-dimensional (3D) planning of dental implants, using artificial intelligence (AI) and augmented reality (AR). MethodsThe novel protocol consists of (1) 3D data acquisition, with an intraoral scanner (IOS) and cone-beam computed tomography (CBCT); (2) application of AI for CBCT segmentation to obtain standard tessellation language (STL) models and automatic alignment with IOS models; (3) loading of selected STL models within the AR system and surgical planning with holograms; (4) surgical guide design with open-source computer-assisted-design (CAD) software; and (5) surgery on the patient. ResultsThis novel protocol is effective and time-efficient when used for planning simple cases of static guided implant surgery in the partially edentulous patient. The clinician can plan the implants in an authentic 3D environment, without using any radiological guided surgery software. The precision of implant placement looks clinically acceptable, with minor deviations. ConclusionsAI and AR technologies can be successfully used in guided implant surgery for authentic 3D planning that may replace conventional software. However, further clinical studies are needed to validate this protocol. Statement of clinical relevanceThe combined use of AI and AR may change the perspectives of modern guided implant surgery for authentic 3D planning that may replace conventional software.

Plasmodium knowlesi circumsporozoite protein: genetic characterisation and predicted antigenicity of the central repeat region.

Circumsporozoite protein (CSP) central repeat region is one of the main target regions of the RTS,S/AS01 vaccine for falciparum infection as it consists of immunodominant B cell epitopes. However, there is a lack of study for P. knowlesi CSP central repeat region. This study aims to characterise the CSP repeat motifs of P. knowlesi isolates in Peninsular Malaysia. CSP repeat motifs of 64 P. knowlesi isolates were identified using Rapid Automatic Detection and Alignment of Repeats (RADAR). Antigenicity of the repeat motifs and linear B cell epitopes were predicted using VaxiJen 2.0, BepiPred-2.0 and BCPred, respectively. A total of 35 dominant repeat motifs were identified. The repeat motif "AGQPQAQGDGANAGQPQAQGDGAN" has the highest repeat frequency (n=15) and antigenicity index of 1.7986. All the repeat regions were predicted as B cell epitopes. In silico approaches revealed that all repeat motifs were antigenic and consisted of B cell epitopes which could be designed as knowlesi malaria vaccine.

Using the Corpus of North American Spoken English to explore regional variation in millions of eɪ diphthongs

Increases in the quality of automatic speech recognition (ASR) systems and forced alignment algorithms have opened up new horizons for the study of acoustic properties of naturalistic speech (Coto-Solano et al., 2021). This paper describes the procedures used for the automatic extraction and alignment of audio and ASR transcript excerpts containing eɪ diphthongs from the Corpus of North American Spoken English, a 1.3-billion-word corpus of geolocated ASR transcripts of videos uploaded to YouTube channels of local government entities. In total, more than 2 million eɪ tokens were extracted from naturalistic speech in over 2000 locations in the US and Canada. The pipeline utilizes python code to extract audio segments from the DASH manifest of YouTube videos, then feeds them together with the corresponding transcript excerpt to the Montreal Forced Aligner. Targeted segments are then filtered using functions in Parselmouth, a python port of Praat. Because the underlying corpus is searchable and words are annotated with timing information, the extraction procedure is suitable for retrieving acoustic data from a range of naturalistic speech configurations (for example from discussions of particular topics or in the context of specific utterance types such as questions or replies). The corpus contains a variety of speech genres, making it suitable for studies of geophonetic and sociophonetic variation, and pipelines can be constructed to target specific phenomenona in YouTube videos, depending on the underlying research question.

Interaction Multi-Agent Models' Automatic Alignment with MDA Higher Abstraction Level

With the massive growth of the software sector as well as the erratic needs of end users, agent-based information systems and Model Driven Architecture (MDA) approach are among the liveliest and significant fields of experimentation and improvement to emerge in the recent decade. In this vein, we suggest in this research an innovative method that automates the construction and the generation processes of the interaction multi-agent models from the business requirements engineering models at the MDA highest abstraction levels. So, our defiance is to align the Agent Modeling Language (AML) Communicative Interaction diagram with the E3value model dealing with the MDA approach. The ATLAS-Transformation Language (ATL) is applied to automate the model alignment process. The goal is to reduce project effort, time, and development costs as all alignment process is automatically done, boosting the chances of being more competitive in the software business.