3D Matching Research Articles

Emerging Two-Dimensional Materials for Proton-Based Energy Storage.

The rapid diffusion kinetics and smallest ion radius make protons the ideal cations toward the ultimate energy storage technology combining the ultrafast charging capabilities of supercapacitors and the high energy densities of batteries. Despite the concept existing for centuries, the lack of satisfactory electrode materials hinders its practical development. Recently, the rapid advancement of the emerging two-dimensional (2D) materials, characterized by their ultrathin morphology, interlayer van der Waals gaps, and distinctive electrochemical properties, injects promises into future proton-based energy storage systems. In this perspective, we comprehensively summarize the current advances in proton-based energy storage based on 2D materials. We begin by providing an overview of proton-based energy storage systems, including proton batteries, pseudocapacitors and electrical double layer capacitors. We then elucidate the fundamental knowledge about proton transport characteristics, including in electrolytes, at electrolyte/electrode interfaces, and within electrode materials, particularly in 2D material systems. We comprehensively summarize specific cases of 2D materials as proton electrodes, detailing their design concepts, proton transport mechanism and electrochemical performance. Finally, we provide insights into the prospects of proton-based energy storage systems, emphasizing the importance of rational design of 2D electrode materials and matching electrolyte systems.

Marker points 3D matching based on Delaunay triangfulation structure and error dispersion

Abstract Point cloud registration techniques based on marker points are widely used in optical 3D industrial measurements. However, in this process, marker points 3D matching methods are often haunted by low efficiency and accuracy. To improve the performance of marker points 3D matching, we propose a two-step method of ‘matching-verification’. In the matching process, Delaunay triangulation is introduced to extract the 3D structure of the marker points set, and then the 3D structure is deconstructed into 2D units for matching, which simplifies complexity and improves the efficiency of the algorithm. In the verification process, the mismatched pairs of points are located and removed by the method that is based on the error dispersion of initial matched results, and the initial transformation results are iteratively verified to obtain the optimal transformation matrix. The experimental results show that our method takes an average of 2.2 s for each matching, the average error of coarse registration point cloud is 0.075 mm and the root mean square is 0.219 mm, which effectively solves the problem of the low efficiency and accuracy of marker points 3D matching methods.

Assessment Accuracy of 2D vs. 3D Imaging for Custom-Made Acetabular Implants in Revision Hip Arthroplasty.

Revision total hip arthroplasty (rTHA) presents significant challenges, particularly in patients with severe acetabular bone defects. Traditional treatment options often fall short, leading to the emergence of custom-made 3D-printed acetabular implants. Accurate assessment of implant positioning is crucial for ensuring optimal postoperative outcomes and for providing feedback to the surgical team. This single-center, retrospective cohort study evaluates the accuracy of standard 2D radiographs versus 3D CT scans in assessing the positioning of these implants, aiming to determine if 2D imaging could serve as a viable alternative for the postoperative evaluation. We analyzed the implant positions of seven rTHA patients with severe acetabular defects (Paprosky ≥ Type IIIA) using an alignment technique that integrates postoperative 2D radiographs with preoperative CT plans. Two independent investigators, one inexperienced and one experienced, measured the positioning accuracy with both imaging modalities. Measurements included translational shifts from the preoperatively templated implant position in the craniocaudal (CC), lateromedial (LM), and ventrodorsal (VD) directions, as well as rotational differences in anteversion (AV) and inclination (INCL). The study demonstrated that 2D radiographs, when aligned with preoperative CT data, could accurately assess implant positions with precision nearly comparable to that of 3D CT scans. Observed deviations were 1.4 mm and 2.7 mm in CC and LM directions, respectively, and 3.6° in AV and 0.7° in INCL using 2D imaging, all within clinically acceptable ranges. For 3D CT assessments, mean interobserver variability was up to 0.9 mm for translational shifts and 1.4° for rotation, while for 2D alignment, observer differences were 1.4 mm and 3.2° for translation and rotation, respectively. Comparative analysis of mean results from both investigators, across all dimensions (CC, LM, AV, and INCL) for 2D and 3D matching, showed no significant differences. In conclusion, conventional anteroposterior 2D radiographs of the pelvis can sufficiently determine the positioning of custom-made acetabular implants in rTHA. This suggests that 2D radiography is a viable alternative to 3D CT scans, potentially enhancing the implementation and quality control of advanced implant technologies.

2D3D-DescNet: Jointly Learning 2D and 3D Local Feature Descriptors for Cross-Dimensional Matching

The cross-dimensional matching of 2D images and 3D point clouds is an effective method by which to establish the spatial relationship between 2D and 3D space, which has potential applications in remote sensing and artificial intelligence (AI). In this paper, we propose a novel multi-task network, 2D3D-DescNet, to learn 2D and 3D local feature descriptors jointly and perform cross-dimensional matching of 2D image patches and 3D point cloud volumes. The 2D3D-DescNet contains two branches with which to learn 2D and 3D feature descriptors, respectively, and utilizes a shared decoder to generate the feature maps of 2D image patches and 3D point cloud volumes. Specifically, the generative adversarial network (GAN) strategy is embedded to distinguish the source of the generated feature maps, thereby facilitating the use of the learned 2D and 3D local feature descriptors for cross-dimensional retrieval. Meanwhile, a metric network is embedded to compute the similarity between the learned 2D and 3D local feature descriptors. Finally, we construct a 2D-3D consistent loss function to optimize the 2D3D-DescNet. In this paper, the cross-dimensional matching of 2D images and 3D point clouds is explored with the small object of the 3Dmatch dataset. Experimental results demonstrate that the 2D and 3D local feature descriptors jointly learned by 2D3D-DescNet are similar. In addition, in terms of 2D and 3D cross-dimensional retrieval and matching between 2D image patches and 3D point cloud volumes, the proposed 2D3D-DescNet significantly outperforms the current state-of-the-art approaches based on jointly learning 2D and 3D feature descriptors; the cross-dimensional retrieval at TOP1 on the 3DMatch dataset is improved by over 12%.

A cascaded GRU-based stereoscopic matching network for precise plank measurement

Wooden plank images in industrial measurements often contain numerous textureless areas. Furthermore, due to the thin plate structure, the three-dimensional (3D) disparity of these planks is predominantly confined to a narrow range. Consequently, achieving accurate 3D matching of wooden plank images has consistently presented a challenging task within the industry. In recent years, deep learning has progressively supplanted traditional stereo matching methods due to its inherent advantages, including rapid inference and end-to-end processing. Nonetheless, the acquisition of datasets for stereo matching networks poses an additional challenge, primarily attributable to the difficulty in obtaining accurate disparity data. Thus, this paper presents a novel stereo matching method incorporating three key innovations. Firstly, an enhanced gated recurrent unit network is introduced, accompanied by a redesigned structure to achieve higher matching accuracy. Secondly, an efficient preprocessing module is proposed, aimed at improving the algorithm’s efficiency. Lastly, in response to the challenges posed by datasets acquisition, we innovatively employed image simulation software to obtain a high-quality simulated dataset of wooden planks. To assess the feasibility of our approach, we conducted both simulated and real experiments. The experiments results clearly exhibit the superiority of our method when compared to existing approaches in terms of both stability and accuracy. In the simulation experiment, our method attained a bad1.0 score of 2.1% (compared to the baseline method’s 9.76%); In the real experiment, our method achieved an average error of 0.104 mm (compared to the baseline method’s 0.268 mm). It is worth noting that our study aims to address the challenge of acquiring datasets for deep learning and bridging the gap between simulated and real data, resulting in increased applicability of deep learning in more industrial measurement domains.

Deep learning generation of preclinical positron emission tomography (PET) images from low-count PET with task-based performance assessment.

Preclinical low-count positron emission tomography (LC-PET) imaging offers numerous advantages such as facilitating imaging logistics, enabling longitudinal studies of long- and short-lived isotopes as well as increasing scanner throughput. However, LC-PET is characterized by reduced photon-count levels resulting in low signal-to-noise ratio (SNR), segmentation difficulties, and quantification uncertainties. We developed and evaluated a novel deep-learning (DL) architecture-Attention based Residual-Dilated Net (ARD-Net)-to generate standard-count PET (SC-PET) images from LC-PET images. The performance of the ARD-Net framework was evaluated for numerous low count realizations using fidelity-based qualitative metrics, task-based segmentation, and quantitative metrics. Patient Derived tumor Xenograft (PDX) with tumors implanted in the mammary fat-pad were subjected to preclinical [18F]-Fluorodeoxyglucose (FDG)-PET/CT imaging. SC-PET images were derived from a 10min static FDG-PET acquisition, 50min post administration of FDG, and were resampled to generate four distinct LC-PET realizations corresponding to 10%, 5%, 1.6%, and 0.8% of SC-PET count-level. ARD-Net was trained and optimized using 48 preclinical FDG-PET datasets, while 16 datasets were utilized to assess performance. Further, the performance of ARD-Net was benchmarked against two leading DL-based methods (Residual UNet, RU-Net; and Dilated Network, D-Net) and non-DL methods (Non-Local Means, NLM; and Block Matching 3D Filtering, BM3D). The performance of the framework was evaluated using traditional fidelity-based image quality metrics such as Structural Similarity Index Metric (SSIM) and Normalized Root Mean Square Error (NRMSE), as well as human observer-based tumor segmentation performance (Dice Score and volume bias) and quantitative analysis of Standardized Uptake Value (SUV) measurements. Additionally, radiomics-derived features were utilized as a measure of quality assurance (QA) in comparison to true SC-PET. Finally, a performance ensemble score (EPS) was developed by integrating fidelity-based and task-based metrics. Concordance Correlation Coefficient (CCC) was utilized to determine concordance between measures. The non-parametric Friedman Test with Bonferroni correction was used to compare the performance of ARD-Net against benchmarked methods with significance at adjusted p-value ≤0.01. ARD-Net-generated SC-PET images exhibited significantly better (p ≤ 0.01 post Bonferroni correction) overall image fidelity scores in terms of SSIM and NRMSE at majority of photon-count levels compared to benchmarked DL and non-DL methods. In terms of task-based quantitative accuracy evaluated by SUVMeanand SUVPeak, ARD-Net exhibited less than 5% median absolute bias for SUVMean compared to true SC-PET and lower degree of variability compared to benchmarked DL and non-DL based methods in generating SC-PET. Additionally, ARD-Net-generated SC-PET images displayed higher degree of concordance to SC-PET images in terms of radiomics features compared to non-DL and other DL approaches. Finally, the ensemble score suggested that ARD-Net exhibited significantly superior performance compared to benchmarked algorithms (p ≤ 0.01 post Bonferroni correction). ARD-Net provides a robust framework to generate SC-PET from LC-PET images. ARD-Net generated SC-PET images exhibited superior performance compared other DL and non-DL approaches in terms of image-fidelity based metrics, task-based segmentation metrics, and minimal bias in terms of task-based quantification performance for preclinical PET imaging.

Multiparametric prenatal imaging characterization of fetal brain edema in Chiari II malformation might help to select candidates for fetal surgery

ObjectiveTo identify brain edema in fetuses with Chiari II malformation using a multiparametric approach including structural T2-weighted, diffusion tensor imaging (DTI) metrics, and MRI-based radiomics.MethodsA single-center retrospective review of MRI scans obtained in fetuses with Chiari II was performed. Brain edema cases were radiologically identified using the following MR criteria: brain parenchymal T2 prolongation, blurring of lamination, and effacement of external CSF spaces. Fractional anisotropy (FA) values were calculated from regions of interest (ROI), including hemispheric parenchyma, internal capsule, and corticospinal tract, and compared group-wise. After 1:1 age matching and manual single-slice 2D segmentation of the fetal brain parenchyma using ITK-Snap, radiomics features were extracted using pyradiomics. Areas under the curve (AUCs) of the features regarding discriminating subgroups were calculated.ResultsNinety-one fetuses with Chiari II underwent a total of 101 MRI scans at a median gestational age of 24.4 weeks and were included. Fifty scans were visually classified as Chiari II with brain edema group and showed significantly reduced external CSF spaces compared to the nonedema group (9.8 vs. 18.3 mm, p < 0.001). FA values of all used ROIs were elevated in the edema group (p < 0.001 for all ROIs). The 10 most important radiomics features showed an AUC of 0.81 (95%CI: 0.71, 0.91) for discriminating between Chiari II fetuses with and without edema.ConclusionsBrain edema in fetuses with Chiari II is common and radiologically detectable on T2-weighted fetal MRI sequences, and DTI-based FA values and radiomics features provide further evidence of microstructure differences between subgroups with and without edema.Clinical relevance statementA more severe phenotype of fetuses with Chiari II malformation is characterized by prenatal brain edema and more postnatal clinical morbidity and disability. Fetal brain edema is a promising prenatal MR imaging biomarker candidate for optimizing the risk-benefit evaluation of selection for fetal surgery.Key PointsBrain edema of fetuses prenatally diagnosed with Chiari II malformation is a common, so far unknown, association.DTI metrics and radiomics confirm microstructural differences between the brains of Chiari II fetuses with and without edema.Fetal brain edema may explain worse motor outcomes in this Chiari II subgroup, who may substantially benefit from fetal surgery.Graphical

Algorithms to create realistic virtual asphalt mixtures

This paper presents a set of algorithms for creating realistic three-dimensional (3D) models of asphalt mixtures based on its composition. The process begins by measuring the shape of the aggregates in the asphalt mixture. Using these measurements, an algorithm creates 3D volumes that approximate the size and shape of the aggregates. These volumes are then added to a virtual mixer in the same proportions as the aggregates in the real mixture and are pressed together until the air void content of the 3D model matches that of the real asphalt. This results in realistic models of aggregate skeletons in the asphalt mixture. Next, sections are taken from the aggregate skeletons and a mortar mixture, made up of bitumen, filler and fine aggregates, is applied to the sections. Geometrical rules are used to determine which areas of the sections are covered in mortar and which are air voids. The validation of virtual aggregates and air voids has demonstrated good correlations with actual ones. The resulting models are highly realistic and can be used to predict the mechanical or physical properties of asphalt mixtures in future studies.

An Intelligent Computer Aided Diagnosis System for Classification of Ovarian Masses using Machine Learning Approach

Ovarian cancer, a difficult and often asymptomatic malignancy, remains a substantial global health concern in women. An ovary is a female reproductive organ, which lies on each side of the uterus and used to store eggs. Computer-aided diagnosis (CAD) is an approach that involves using computer algorithms and machine learning techniques to assist medical professionals in diagnosing ovarian malignancies, benign tumors or Poly-cystic ovaries (PCOS). The need for models that can effectively predict benign ovarian tumors and ovarian cancer has led to the use of machine learning techniques. Our research objective is to propose a machine learning-based system for accurate and early ovarian mass detection utilizing novel annotated ovarian masses. We have used an actual patient database whose input features were extracted from 187 transvaginal ultrasound images from database. The input image is preprocessed using the Block Matching 3D filter. The process involves employing binary and watershed segmentation techniques, followed by the integration of Gabor, Gray-Level Co-Occurrence Matrix (GLCM), Tamura, and edge feature extraction methods. K-Nearest Neighbors (KNN) and Random Forest (RF) are two classifiers used for classification. Based on our results, we are able to demonstrate that binary segmentation with RF classifiers is more accurate (above 86%) than KNN classifiers (under 84%).

Application of computer vision techniques for 3D matching and retrieval of archaeological objects

Background As cultural institutions embark in projects oriented to digitise art and archaeological collections in three dimensions, the need for developing means to access the resulting 3D models has become imperative. Shape recognition techniques developed in the field of computer vision can help in this task. Methods This paper describes the implementation of three shape descriptors, specifically shape distributions, reflective symmetry and spherical harmonics as part of the development of a search engine that retrieves 3D models from an archaeological database without the need of using keywords as query criteria. Use case The usefulness of this system is obvious in the context of cultural heritage museums, where it is essential to provide automatic access to archaeological and art collections. The prototype described in this paper uses, as study case, 3D models of archaeological objects belonging to Museo del Templo Mayor, a Mexican institution that preserves one of the largest collections of Aztec cultural heritage. Conclusions This work is part of an ongoing project focused on creating generic methodologies and user-friendly computational tools for shape analysis for the benefit of scholars and students interested in describing, interpreting and disseminating new knowledge about the morphology of cultural objects.

IINet: Implicit Intra-inter Information Fusion for Real-Time Stereo Matching

Recently, there has been a growing interest in 3D CNN-based stereo matching methods due to their remarkable accuracy. However, the high complexity of 3D convolution makes it challenging to strike a balance between accuracy and speed. Notably, explicit 3D volumes contain considerable redundancy. In this study, we delve into more compact 2D implicit network to eliminate redundancy and boost real-time performance. However, simply replacing explicit 3D networks with 2D implicit networks causes issues that can lead to performance degradation, including the loss of structural information, the quality decline of inter-image information, as well as the inaccurate regression caused by low-level features. To address these issues, we first integrate intra-image information to fuse with inter-image information, facilitating propagation guided by structural cues. Subsequently, we introduce the Fast Multi-scale Score Volume (FMSV) and Confidence Based Filtering (CBF) to efficiently acquire accurate multi-scale, noise-free inter-image information. Furthermore, combined with the Residual Context-aware Upsampler (RCU), our Intra-Inter Fusing network is meticulously designed to enhance information transmission on both feature-level and disparity-level, thereby enabling accurate and robust regression. Experimental results affirm the superiority of our network in terms of both speed and accuracy compared to all other fast methods.

Effective classification of alzheimer disease based on image tractography framework utilizing GM-ABC-NN

A progressive neurological disorder that leads to memory loss as well as cognitive decline is called Alzheimer’s Disease (AD). Grounded on various factors like symptoms, biomarkers, or disease stages, the classification of AD is done. Yet, the prevailing techniques pose challenges in capturing accurate AD, which results in misdiagnosis or delayed treatment. To overcome these issues, an effective framework for the classification of AD based on image tractography using several algorithms is proposed in this article. Primarily, the dataset comprising AD images undergoes image transformation. Then, to enhance the MRI image quality, the transformed images are pre-processed using Block Matching 3D Filtering (BM3D) and Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithms. Afterward, image tractography followed by brain tissue segmentation is done utilizing the Cauchy-Steiner Weighted Fuzzy C Means (CauSt-FCM) algorithm. Then, by utilizing the Cosine Non-Negative Kernel Regression (CosNNK) algorithm, graph construction is done. Lastly, for the feature selection and validation process, the Premature Singer Archerfish Hunting Optimizer (PreS-AHO) algorithm and Analysis of Variance (ANOVA) analysis are used. Thereafter, the AD stages are classified by using the Gaussian Mixture Alpha-Beta Convolutional Neural Network (GM-ABC-NN) algorithm, The proposed system attains superior accuracy (97.56%), precision (97.55%), and recall (97.57%).

Multi-view 3D reconstruction based on deep learning: A survey and comparison of methods

An important objective in computer vision is to analyze multiple images and subsequently reconstruct the shape and structure in 3D. Traditional multi-view 3D reconstruction techniques extract and match key features from images with known camera parameters. However, this approach is inefficient and fails to fully exploit the advantages of multi-view information. Advancements in deep learning have revolutionized multi-view 3D reconstruction by enabling end-to-end 3D shape inferencing without the need for sequential feature matching typically found in conventional algorithms. Recent rapid progress in this field necessitates a thorough review of current algorithms and provide insight into method of improving 3D reconstruction performance. This review classifies reconstruction algorithms according to their resultant model, including depth map, voxel, point cloud, mesh, and implicit surface. Additionally, this review encompasses the inclusion of frequently employed network training loss functions for network training, assessment metrics, and the incorporation of 3D datasets. Experimental results are also presented to assess the performance of different algorithms. Finally, the paper concludes with a summary, discussion of challenges, and potential future directions.

Resource allocation in vehicular network based on sparrow search algorithm and hyper-graph in the presence of multiple cellular users

PurposeSince the performance of vehicular users and cellular users (CUE) in Vehicular networks is highly affected by the allocated resources to them. The purpose of this paper is to investigate the resource allocation for vehicular communications when multiple V2V links and a V2I link share spectrum with CUE in uplink communication under different Quality of Service (QoS).Design/methodology/approachAn optimization model to maximize the V2I capacity is established based on slowly varying large-scale fading channel information. Multiple V2V links are clustered based on sparrow search algorithm (SSA) to reduce interference. Then, a weighted tripartite graph is constructed by jointly optimizing the power of CUE, V2I and V2V clusters. Finally, spectrum resources are allocated based on a weighted 3D matching algorithm.FindingsThe performance of the proposed algorithm is tested. Simulation results show that the proposed algorithm can maximize the channel capacity of V2I while ensuring the reliability of V2V and the quality of service of CUE.Originality/valueThere is a lack of research on resource allocation algorithms of CUE, V2I and multiple V2V in different QoS. To solve the problem, one new resource allocation algorithm is proposed in this paper. Firstly, multiple V2V links are clustered using SSA to reduce interference. Secondly, the power allocation of CUE, V2I and V2V is jointly optimized. Finally, the weighted 3D matching algorithm is used to allocate spectrum resources.

Robust HDR reconstruction using 3D patch based on two-scale decomposition

In this paper, we propose an effective 3D patch-based match and fusion method by taking account of dynamic or multi-view scenes in a multi-exposure image sequence using two-scale decomposition. As opposed to most multi-exposure image fusion methods, the proposed method does not require a pre-alignment step to reduce ghosting artifacts. Considering that pixel values are affected by multi-view or multi-exposure scenes, we use a uniform matching approach to match and find similar patches in different exposure images and then fuse them at each scale. By searching all similar patches (instead of the optimal patch) in the searching window to form the 3D patch when facing limited image information, we can fully use the complementary information of the multi-exposure images to preserve information about moving objects and scene details. The experimental results show that the proposed method not only performs well on dynamic scenes but also consistently generates high-quality fused images in multi-view scenes.

Enhancing Image Denoising Efficiency: Dynamic Transformations in BM3D Algorithm

This research aims to enhance the performance of image-denoising algorithms, particularly in the context of Block Matching 3D (BM3D) usage, focusing on improving image quality and retaining important information in noisy images. The novelty of this research lies in developing more effective and efficient image-denoising techniques by considering the characteristics of image blocks to improve denoising results. The method employed in this research involves the development of a new approach enabling the application of adaptive 2D and 3D transformations depending on the characteristics of the image block being processed. The research develops a new approach enabling the application of adaptive 2D and 3D transformations depending on the characteristics of the image block being processed. The results of this research indicate that the proposed adaptive approach in the BM3D image denoising algorithm can significantly improve denoising performance. Experimental results show that performing 2D transformations on blocks that do not have sufficiently similar blocks can yield better denoising results, especially at high noise levels.

Parallel Complementary Virtual Arrays Algorithm for Direction of Arrival (DOA) Estimation

This Paper discusses the challenges faced by previous method 2D Direction of Arrival (DOA) systems, such as low degrees of freedom, poor resolution, and significant estimation errors in scenarios with small snapshots. In response to these issues, the present method proposes a low-complexity 2D Direction of Arrival (DOA) estimation algorithm based on a parallel complementary virtual array.&#x0D; The algorithm utilizes two mutually parallel complementary linear arrays to generate a virtual array, addressing the limitations of traditional parallel arrays. It constructs an extended matrix with enhanced 2D angular degrees of freedom using covariance and cross-covariance matrices. The final step involves obtaining automatic matching 2D angle estimates through Singular Value Decomposition (SVD) and Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT).&#x0D; In comparison to traditional 2D DOA estimation methods, the proposed algorithm better exploits the information from the array's received data. It can identify more incoming signals, offering high resolution without the need for 2D linear search or angle parameter matching. Importantly, it demonstrates effective estimation even in scenarios with low Signal-to-Noise Ratio (SNR) and small snapshots. Experimental simulation results validate the effectiveness and reliability of the proposed algorithm.

Interface-Engineered 2D Heterojunction with Photoelectric Dual Gain: Mxene@MOF-Enhanced SPR Spectroscopy for Direct Sensing of Exosomes.

MXene is widely used in the construction of optoelectronic interfaces due to its excellent properties. However, the hydrophilicity and metastable surface of MXene lead to its oxidation behavior, resulting in the degradation of its various properties, which seriously limits its practical application. In this work, a 2D metal-organic framework (2D MOF) with matching 2D morphology, excellent stability performance, and outstanding optoelectronic performance is grown in situ on the MXene surface through heterojunction engineering to suppress the direct contact between reactive molecules and the inner layer material without affecting the original advantages of MXene. The photoelectric dual gain MXene@MOF heterojunction is confirmed. As a photoelectric material, its properties are highly suitable for the demand of interface sensitization layer materials of surface plasmon resonance (SPR). Therefore, using SPR as a platform for the application of this interface material, the performance of MXene@MOF and its potential mechanism to enhance SPR are analyzed in depth using experiments combined with simulation calculations (FDTD/DFT). Finally, the MXene@MOF/peptides-SPR sensor is constructed for rapid and sensitive detection of the cancer marker exosomes to explore its potential in practical applications. This work offers a forward-looking strategy for the design of interface materials with excellent photoelectric performance.

3D Vehicle Object Tracking Algorithm Based on Bounding Box Similarity Measurement

Effectively extracting features from a discrete point cloud is necessary for three-dimensional (3D) vehicle tracking. However, a point cloud data set is large and sparsely distributed, hindering the success of vehicle-tracking algorithms. To solve this problem, this paper proposes a 3D vehicle object tracking algorithm based on bounding box similarity measurement. The algorithm includes state prediction, temporal association, trajectory management, state update, and other processes. Also incorporated is a vehicle object temporal association method based on a siamese encoder. The bounding box is encoded into a high-dimensional space, the feature distance is calculated as the time series association cost, and triplet loss was introduced to urge the encoder to learn the geometric similarity of the truth matching box. A 3D Kalman filter and greedy matching are used to effectuate 3D vehicle object tracking algorithm. The experimental results using a KITTI multi-object tracking dataset show that the proposed algorithm can achieve good vehicle tracking performance. In the case of frame loss of point cloud in the frequency reduction simulation, compared with the benchmark method AB3DMOT, the average multi-object tracking accuracy ( AMOTA) and the average multi-object tracking accuracy ( AMOTP) of the improved method are increased by 2.62% and 1.41% respectively, and the performance is better in the frame loss scene.

A Hybrid Medical Image Denoising Based on Block Matching 3D Collaborative Filtering

A Hybrid Medical Image Denoising Based on Block Matching 3D Collaborative Filtering