High Matching Accuracy Research Articles

An Efficient Automatic Fingerprint Authentication System Using a Template Protection Technique

The disclosure of biometric template data is one of the possible weaknesses in a biometric system, posing major security and privacy risks. The majority of template protection methods on the market fall short of meeting all the necessary specifications for a workable biometric system, including high matching accuracy, security, privacy, and revocability. Research on automated fingerprint-based identification began in the early 1960s since fingerprints have been a vital tool for forensics and law enforcement for more than a century. Our proposal includes a system that uses extraction of minutiae approach to verify fingerprints and an automated system that takes attendance. Unimodal fingerprint biometric systems, which analyze distinctive sequences to protect authentication information, deal with issues including noisy data, non-universality, intra-class deviation, and susceptibility to spoof attacks. Multimodal fingerprint biometric systems overcome these issues by compensating for the shortcomings of different biometric sources. Our test findings demonstrate that, while maintaining template security, the suggested multi-biometric template protection strategy outperforms its unibiometric equivalents regarding verification outcomes.

A Novel Map Matching Method Based on Improved Hidden Markov and Conditional Random Fields Model

ABSTRACT Aiming at the inherent “labeling bias” problem of Hidden Markov Models (HMMs) in the process of low-frequency sampling rate GPS trajectory map matching, this paper proposes a map matching method that combines HMMs and Conditional Random Field Models (CRFs). Various features including vehicle driving direction, direction change between candidate roads, shortest path, and spatial localization accuracy of trajectory points are integrated to optimize the state transfer process in the HMM model. The Viterbi algorithm was then employed to calculate the joint probability values of all candidate paths and the path with maximum probability value was selected as the matching path. The proposed map matching method was validated using publicly available Global Positioning System (GPS) vehicle trajectories from Guangzhou, Dongguan, and field-collected vehicle trajectory data from Ganzhou, China. The results demonstrated that the proposed method achieves high matching accuracy while maintaining efficient matching efficiency. The percentage of correct matching of GPS sampling points remains above 95%, and the average calculation time for each candidate road segment is kept within 50 ms. These advancements can greatly benefit location-based services that rely on map matching technology.

Vehicle Target Detection Using the Improved YOLOv5s Algorithm

This paper explores the application of the YOLOv5s algorithm integrated with the DeepSORT tracking detection algorithm in vehicle target detection, leveraging its advantages in data processing, loss function, network structure, and training strategy. Regarding detection frame regression, adopting Focal-EIOU can improve vehicle detection accuracy by precisely measuring overlap and better handle complex scenarios, enhancing the overall performance. The CoordConv convolution layer with more spatial position information is employed to enhance the original network structure’s convolution layer and improve vehicle positioning accuracy. The principle and effectiveness of the Shuffle Attention mechanism are analyzed and added to the YOLOv5s network structure to enhance training, improve detection accuracy and running speed. And the DeepSORT tracking detection algorithm is designed to achieve high-speed operation and high-accuracy matching in target tracking, enabling efficient and reliable tracking of objects. Simultaneously, the network structure is optimized to enhance algorithmic speed and performance. To meet the requirements of vehicle detection in practical transportation systems, real-world vehicle images are collected as a dataset for model training to achieve accurate vehicle detection. The results show that the accuracy rate P of the improved YOLOv5s algorithm is increased by 0.484%, and mAP_0.5:0.95 reaches 92.221%, with an increase of 1.747%.

Efficient gridless wideband sparse array synthesis with tapped delay-lines

In this paper, we propose a new wideband sparse array synthesis method based on gridless compressed sensing to solve the basis mismatch problem for discrete grids. Considering the tapped delay-lines (TDL) structure for space-time domain processing, and using successive frequency-varying atoms for sparse representation of wideband signals, an arbitrary sampling-atomic norm minimization is introduced to model the group sparsity-constrained wideband arrays in which the positions and the excitation values of array element are obtained with a high freedom. The above nonconvex problem is then transformed into a convex relaxation, which is solved using the Prolate Spheroidal Wave Functions (PSWFs). The experimental results show that the proposed sparse array design has higher matching accuracy and sparsity, compared with the discretized wideband sparse array design, which verifies the effectiveness and efficiency of this method.

Production of Annual Nighttime Light Based on De-Difference Smoothing Algorithm

Nighttime light (NTL) remote sensing has emerged as a powerful tool in various fields such as urban expansion, socio-economic estimation, light pollution, and energy domains. However, current annual NTL products suffer from several critical limitations, including poor consistency, severe background noise, and limited comparability. These issues have significantly interfered with the research of long-term NTL trends and diminished the accuracy of related findings. Therefore, this study developed a de-difference smoothing algorithm for producing high-quality annual NTL products based on monthly National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data. It enabled the construction of a continuous global high-quality NTL dataset, named the De-Difference Smoothed Nighttime Light (DDSNL), covering the period from 2012 to 2023. Comparative analyses were conducted to validate the accuracy and availability of the DDSNL product against the benchmark EOG NPP-VIIRS and NPP-VIIRS-like NTL datasets. The results showed that DDSNL products had strong correlation with the NTL distribution of EOG NPP-VIIRS, but little correlation with NPP-VIIRS-like. Notably, DDSNL demonstrated better background noise reduction and higher separability between NTL and non-NTL areas compared to EOG NPP-VIIRS NTL. In contrast to the complete exclusion of background in NPP-VIIRS-Like, the retention of background values in DDSNL leads to more reasonable representation in the urban fringes. In the analysis of NTL changes matching impervious surface changes, the DDSNL product demonstrated the least interference from noise, resulting in the smallest segmentation threshold and the highest matching accuracy. This indirectly demonstrates the spatial and temporal consistency of the annual DDSNL product, ensuring its reliability in change detection-related studies. The annual DDSNL product developed in this research exhibits high fidelity, strong consistency, and improved comparability, and can provide reliable data reference for applications in electrification and urban studies.

Optimization of Network Security Intelligent Early Warning System Based on Image Matching Technology of Partial Differential Equation

In order to effectively avoid network information leakage and computer network paralysis, and improve the ability of computer network security early warning, it is necessary to design a computer network security intelligent early warning system based on network behavior. Security warning is considered as the second defense mechanism behind the firewall. It can monitor and warn the network without affecting the network performance, so as to provide real-time protection for external attacks, internal attacks and misoperations, and improve the network security. This paper designs an intelligent early warning system for network security based on partial differential equation image matching technology. The system adopts B/S development mode, and the server and browser are located in the campus network. Data fusion technology is used to assess network security, predict potential threats, and add new intrusion features to the feature database for subsequent use. In this paper, a target matching algorithm based on partial differential equation is proposed by using partial differential equation. The algorithm calculates the phase difference through the algebraic combination of orthogonal filter outputs. For scenes with continuous disparity changes, the error matching rate of this algorithm is lower than that of Michael Bleyer algorithm and vertical constraint algorithm. In general, the disparity map generated by this algorithm has high matching accuracy. The results show that the distribution of alarm information is reasonable and in line with the actual situation.

High-Matching and Low-Cost Realization of the FHN Neuron Model on Reconfigurable FPGA Board.

The main objectives of neuromorphic engineering are the research, modeling, and implementation of neural functioning in the human brain. We provide a hardware solution that can replicate such a nature-inspired system by merging multiple scientific domains and is based on neural cell processes. This work provides a modified version of the original Fitz-Hugh Nagumo (FHN) neuron using a simple 2V term called Hybrid Piece-Wised Base-2 Model (HPWBM), which accurately reproduces numerous patterns of the original neuron model. With reduced terms, we suggest modifying the original nonlinear term to achieve high matching accuracy and little computing error. Time domain and phase portraits are used to validate the proposed model, which shows that it can reproduce all of the FHN model's properties with high accuracy and little mistake. We provide an effective digital hardware approach for large-scale neuron implementations based on resource-sharing and pipelining strategies. The Hardware Description Language (HDL) is used to construct the hardware on an FPGA as a proof of concept. The recommended model hardly uses 0.48 percent of the resources on a Virtex 4 FPGA board, according to the results of the hardware implementation. The circuit can run at a maximum frequency of 448.236 MHz, according to the static timing study.

Underwater terrain-matching algorithm based on improved iterative closest contour point algorithm

Although an autonomous underwater vehicle (AUV) is noted for its good autonomy, concealment and anti-interference ability, errors in its inertial navigation system (INS) inevitably increase over time, leading to positional failure during long-term voyages. Terrain-assisted navigation can help the INS to correct its position. The traditional iterative closest contour point (ICCP) achieves high matching accuracy when the initial position error of the INS is small, but is prone to mismatching when the initial error is large. This study combines ICCP with particle swarm optimization (PSO) to overcome this problem. First, the global optimization ability of PSO is improved by changing the acceleration factor and introducing an artificial bee colony (ABC) onlooker bee greedy search (ABC- ωAPSO). Second, the Euclidean distance of ICCP is replaced by the Mahalanobis distance to abate the influence of system error on the matching accuracy. Finally, the initial position error is reduced by rough matching using the ABC- ωAPSO, which has global optimization capability. Next, fine matching is performed by ICCP. This two-step process resolves the sensitivity problem of ICCP to the initial position error. The experimental results revealed a good matching effect after the double-matching procedure. When the initial INS errors were 0.55′ to the east and 0.55′ to the north, the matching error was reduced to 89.3 m, suggesting that the approach can realize autonomous passive navigation of AUVs.

MSGM: An Advanced Deep Multi-Size Guiding Matching Network for Whole Slide Histopathology Images Addressing Staining Variation and Low Visibility Challenges.

Matching whole slide histopathology images to provide comprehensive information on homologous tissues is beneficial for cancer diagnosis. However, the challenge arises with the Giga-pixel whole slide images (WSIs) when aiming for high-accuracy matching. Learning-based methods are difficult to generalize well with large-size WSIs, necessitating the integration of traditional matching methods to enhance accuracy as the size increases. In this paper, we propose a multi-size guiding matching method applicable high-accuracy requirements. Specifically, we design learning multiscale texture to train deep descriptors, called TDescNet, that trains 64 × 64 × 256 and 256 × 256 × 128 size convolution layer as C64 and C256 descriptors to overcome staining variation and low visibility challenges. Furthermore, we develop the 3D-ring descriptor using sparse keypoints to support the description of large-size WSIs. Finally, we employ C64, C256, and 3D-ring descriptors to progressively guide refined local matching, utilizing geometric consistency to identify correct matching results. Experiments show that when matching WSIs of size 4096 × 4096 pixels, our average matching error is 123.48 μm and the success rate is 93.02 % in 43 cases. Notably, our method achieves an average improvement of 65.52 μm in matching accuracy compared to recent state-of-the-art methods, with enhancements ranging from 36.27 μm to 131.66 μm. Therefore, we achieve high-fidelity whole-slice image matching, and overcome staining variation and low visibility challenges, enabling assistance in comprehensive cancer diagnosis through matched WSIs.

Efficient and accurate registration with BWPH descriptor for low-quality point clouds.

Point cloud registration based on local descriptors plays a crucial role in 3D computer vision applications. However, existing methods often suffer from limitations such as low accuracy, a large memory footprint, and slow speed, particularly when dealing with 3D point clouds from low-cost sensors. To overcome these challenges, we propose an efficient local descriptor called Binary Weighted Projection-point Height (BWPH) for point cloud registration. The core idea behind the BWPH descriptor is the integration of Gaussian kernel density estimation with weighted height characteristics and binarization components to encode distinctive information for the local surface. Through extensive experiments and rigorous comparisons with state-of-the-art methods, we demonstrate that the BWPH descriptor achieves high matching accuracy, strong compactness, and feasibility across contexts. Moreover, the proposed BWPH-based point cloud registration successfully registers real datasets acquired by low-cost sensors with small errors, enabling accurate initial alignment positions.

Research on hierarchical network security situation awareness data fusion method in big data environment

ABSTRACT With the increase in the number of network users, data information has become more abundant, and query speed and coverage have greatly improved. In this era, the problem of information leakage is relatively serious, and some malicious software has invaded the network system of user devices, increasing the threat to network security. In order to improve the analysis ability of network security situation awareness, this study designs a hierarchical network security situation awareness data fusion method under the big data environment. On the basis of data fusion technology and network security situation awareness technology, the hierarchical network security situation information is collected. Then, its features are extracted, and the network security situation awareness process is constructed based on RBF neural. By using this process, hierarchical network security situation awareness data can be obtained. The data is first sorted out and analyzed, and then the data is filtered. Finally, the matrix three decomposition data fusion algorithm is selected as the blueprint of the data fusion method. It can complete the hierarchical network security situation awareness data fusion. Experimental results show that the proposed method has high matching accuracy, little influence on the data fusion time due to the increase of data volume and low energy consumption in the fusion process.

GeoTransformer: Fast and Robust Point Cloud Registration With Geometric Transformer.

We study the problem of extracting accurate correspondences for point cloud registration. Recent keypoint-free methods have shown great potential through bypassing the detection of repeatable keypoints which is difficult to do especially in low-overlap scenarios. They seek correspondences over downsampled superpoints, which are then propagated to dense points. Superpoints are matched based on whether their neighboring patches overlap. Such sparse and loose matching requires contextual features capturing the geometric structure of the point clouds. We propose Geometric Transformer, or GeoTransformer for short, to learn geometric feature for robust superpoint matching. It encodes pair-wise distances and triplet-wise angles, making it invariant to rigid transformation and robust in low-overlap cases. The simplistic design attains surprisingly high matching accuracy such that no RANSAC is required in the estimation of alignment transformation, leading to 100 times acceleration. Extensive experiments on rich benchmarks encompassing indoor, outdoor, synthetic, multiway and non-rigid demonstrate the efficacy of GeoTransformer. Notably, our method improves the inlier ratio by 18∼31 percentage points and the registration recall by over 7 points on the challenging 3DLoMatch benchmark.

Hash Indexing-Based Image Matching for 3D Reconstruction

Image matching is a basic task in three-dimensional reconstruction, which, in recent years, has attracted extensive attention in academic and industrial circles. However, when dealing with large-scale image datasets, these methods have low accuracy and slow speeds. To improve the effectiveness of modern image matching methods, this paper proposes an image matching method for 3D reconstruction. The proposed method can obtain high matching accuracy through hash index in a very short amount of time. The core of hash matching includes two parts: creating the hash table and hash index. The former is used to encode local feature descriptors into hash codes, and the latter is used to search candidates for query feature points. In addition, the proposed method is extremely robust to image scaling and transformation by using various verifications. A comprehensive experiment was carried out using several challenging datasets to evaluate the performance of hash matching. Experimental results show that the HashMatch presents excellent results compared to the state-of-the-art methods in both computational efficiency and matching accuracy.

Partial discharge signal compression reconstruction method based on transfer sparse representation and dual residual ratio threshold

AbstractPartial discharge (PD) signals have a large amount of data and a low energy proportion of pulse signals, resulting in difficult data transmission and poor reconstruction efficiency. To this end, a PD signal compression reconstruction method based on transfer sparse representation and dual residual ratio threshold (TSR‐DRRT) is proposed. TSR‐DRRT is centred on the sparse representation (SR) and accurate reconstruction of noisy signals. The intrinsic pulse of PD signals is extracted by signal decomposition, and jointly trained with different types of signals to establish the transfer SR dictionary. The compressed signal accurately retains the essential characteristics of the pulse information by improving the match between the dictionary atoms and the polymorphic PD pulses. To match the transfer SR dictionary, the inner and outer DRRT iteration termination conditions are set adaptively during the reconstruction process based on the correlation difference between the dictionary and signal frames. Independent control of PD pulse recognition and reconstruction accuracy is achieved, and its performance under noisy signals is improved. The results show that the method can achieve high ratio compression and efficient reconstruction of noisy signals. Different types of PD signals can also have high matching accuracy. This method can meet the demand for PD signals compression and transmission to the terminal for accurate reconstruction.

Parallax image stitching combining improved feature optimization with optimal seam estimation

In the process of image stitching, many problems will inevitably arise, such as misalignment, artifacts and local structure distortion in the overlapping regions. A parallax image stitching algorithm combining improved feature optimization with an innovative iterative seam estimation is proposed. First, the point features and line features of input images are detected. To optimize point features, the histogram statistical approach is proposed to remove false matching points combined with RANSAC algorithm. Second, the mesh warp is optimized by minimizing the sparse quadratic total energy function so as to achieve accurate alignment in the overlapping regions. Finally, we propose an iterative seam estimation method using an improved quality evaluation strategy. Experimental results show that our method has higher matching accuracy and visually better image stitching performance than other methods.

The first patients treated with MR-CBCT soft-tissue matching in a MR-only prostate radiotherapy pathway

IntroductionMagnetic Resonance (MR)-only radiotherapy for prostate cancer has previously been reported using fiducial markers for on-treatment verification. MR-Cone Beam Computed Tomography (CBCT) soft-tissue matching does not require invasive fiducial markers and enables MR-only treatments to other pelvic cancers. This study evaluated the first clinical implementation of MR-only prostate radiotherapy using MR-CBCT soft-tissue matching. MethodsTwenty prostate patients were treated with MR-only radiotherapy using a synthetic (s)CT-optimised plan with MR-CBCT soft-tissue matching. Two MR sequences were acquired: small Field Of View (FOV) for target delineation and large FOV for organs at risk delineation, sCT generation and on-treatment verification. Patients also received a CT for validation. The prostate was independently contoured on the small FOV MR, copied to the registered CT and modified if there were MR-CT soft-tissue alignment differences (MR-CT volume). This was compared to the MR-only volume with a paired t-test. The treatment plan was recalculated on CT and the doses compared. Independent offline CT-CBCT matches for 5/20 fractions were performed by three therapeutic radiographers using the MR-only contours and compared to the online MR-CBCT matches using two one-sided paired t-tests for equivalence within ±1 mm. ResultsThe MR-only volumes were significantly smaller than MR-CT (p = 0.003), with a volume ratio 0.92 ± 0.02 (mean ± standard error). The sCT isocentre dose difference to CT was 0.2 ± 0.1%. MR-CBCT soft-tissue matching was equivalent to CT-CBCT (p < 0.001), with differences of 0.1 ± 0.2 mm (vertical), −0.1 ± 0.2 mm (longitudinal) and 0.0 ± 0.1 mm (lateral). ConclusionsMR-only radiotherapy with soft-tissue matching has been successfully clinically implemented. It produced significantly smaller target volumes with high dosimetric and on-treatment matching accuracy. Implications for practiceMR-only prostate radiotherapy can be safely delivered without using invasive fiducial markers. This enables MR-only radiotherapy to be extended to other pelvic cancers where fiducial markers cannot be used.

Semi-Global Stereo Matching Algorithm Based on Multi-Scale Information Fusion

Semi-global matching (SGM) has been widely used in binocular vision. In spite of its good efficiency, SGM still has difficulties in dealing with low-texture regions. In this paper, an SGM algorithm based on multi-scale information fusion (MSIF), named SGM-MSIF, is proposed by combining multi-path cost aggregation and cross-scale cost aggregation (CSCA). Firstly, the stereo pairs at different scales are obtained by Gaussian pyramid down-sampling. The initial matching cost volumes at different scales are computed by combining census transform and color information. Then, the multi-path cost aggregation in SGM is introduced into the cost aggregation at each scale and the aggregated cost volumes are fused by CSCA. Thirdly, the disparity map is optimized by internal left-right consistency check and median filter. Finally, experiments are conducted on Middlebury datasets to evaluate the proposed algorithm. Experimental results show that the average error matching rate (EMR) of the proposed SGM-MSIF algorithm reduced by 1.96% compared with SGM. Compared with classical cross-scale stereo matching algorithm, the average EMR of SGM-MSIF algorithm reduced by 0.92%, while the processing efficiency increased by 58.7%. In terms of overall performance, the proposed algorithm outperforms the classic SGM and CSCA algorithms. It can achieve high matching accuracy and high processing efficiency for binocular vision applications, especially for those with low-texture regions.

Soft Tissue Feature Tracking Based on Deep Matching Network

Research in the field of medical image is an important part of the medical robot to operate human organs. A medical robot is the intersection of multi-disciplinary research fields, in which medical image is an important direction and has achieved fruitful results. In this paper, a method of soft tissue surface feature tracking based on a depth matching network is proposed. This method is described based on the triangular matching algorithm. First, we construct a self-made sample set for training the depth matching network from the first N frames of speckle matching data obtained by the triangle matching algorithm. The depth matching network is pre-trained on the ORL face data set and then trained on the self-made training set. After the training, the speckle matching is carried out in the subsequent frames to obtain the speckle matching matrix between the subsequent frames and the first frame. From this matrix, the inter-frame feature matching results can be obtained. In this way, the inter-frame speckle tracking is completed. On this basis, the results of this method are compared with the matching results based on the convolutional neural network. The experimental results show that the proposed method has higher matching accuracy. In particular, the accuracy of the MNIST handwritten data set has reached more than 90%.

Digital surface model generation from aerial imagery using bridge probability relaxation matching

Optical image dense matching is a crucial step in the process of generating digital surface models (DSMs). Many existing dense matching methods have adopted pixelwise matching strategy and have achieved precise matching results; however, the methods are time consuming and have limited efficiency in high surveying and mapping production. We introduce a bridge probability relaxation matching method for automatic DSM generation. The method adopts a coarse-to-fine hierarchical strategy and achieves high matching accuracy and fast processing speed simultaneously. This method builds a self-adaptive disparity surface model in a local area and constrains the disparity surface using the spatial relationship between feature points and adjacent pixels. Finally, the disparity is optimized by calculating the increment of the relaxation iteration probability. Experiments are based on different areas with different textures and terrain types. Compared with the DSMs derived from semi-global matching, our proposed approach achieves high levels of accuracy and efficiency in automatic DSM generation.

Game-theoretic hypergraph matching with density enhancement

Feature matching plays a fundamental role in computer vision and pattern recognition. As straightforward comparison of feature descriptors is not enough to provide reliable matching results in many situations, graph matching makes use of the pairwise relationship between features to improve matching accuracy. Hypergraph matching further employs the relationship among multiple features to provide more invariance between feature correspondences. Existing hypergraph matching algorithms usually solve an assignment problem, where outliers may result in a large number of false matches. In this paper we cast the hypergraph matching problem as a non-cooperative multi-player game, and obtain the matches by extracting the evolutionary stable strategies. Our algorithm exerts a strong constraint on the consistency of obtained matches, and false matches are excluded effectively. In order to increase the number of matches without increasing the computation load evidently, we present a density enhancement method to improve the matching results. We further propose two methods to enforce the one-to-one constraint, thereby removing false matches and maintaining a high matching accuracy. Experiments with both synthetic and real datasets validate the effectiveness of our algorithm.