Matching Algorithm Research Articles

EMC+GD_C: circle-based enhanced motion consistency and guided diffusion feature matching for 3D reconstruction

AbstractRobust matching, especially the number, precision and distribution of feature point matching, directly affects the effect of 3D reconstruction. However, the existing methods rarely consider these three aspects comprehensively to improve the quality of feature matching, which in turn affects the effect of 3D reconstruction. Therefore, to effectively improve the quality of 3D reconstruction, we propose a circle-based enhanced motion consistency and guided diffusion feature matching algorithm for 3D reconstruction named EMC+GD_C. Firstly, a circle-based neighborhood division method is proposed, which increases the number of initial matching points. Secondly, to improve the precision of feature point matching, on the one hand, we put forward the idea of enhancing motion consistency, reducing the mismatch of high similarity feature points by enhancing the judgment conditions of true and false matching points; on the other hand, we combine the RANSAC optimization method to filter out the outliers and further improve the precision of feature point matching. Finally, a novel guided diffusion idea combining guided matching and motion consistency is proposed, which expands the distribution range of feature point matching and improves the stability of 3D models. Experiments on 8 sets of 908 pairs of images in the public 3D reconstruction datasets demonstrate that our method can achieve better matching performance and show stronger stability in 3D reconstruction. Specifically, EMC+GD_C achieves an average improvement of 24.07% compared to SIFT-based ratio test, 9.18% to GMS and 1.94% to EMC+GD_G in feature matching precision.

Comparative assessment and policy analysis of forecasting quarterly renewable energy demand: Fresh evidence from an innovative seasonal approach with superior matching algorithms

In the pursuit of sustainable development, accurate renewable energy demand forecasting holds great significance for climate change mitigation and promoting sustainability. However, renewable energy forecasting has been consistently challenged by seasonality and nonlinearity. Identifying the periodic and nonlinear characteristics concealed within renewable energy sources accurately is still an unexplored problem. Consequently, an innovative nonlinear discrete seasonal grey model is proposed for renewable energy forecasting, which incorporates seasonal dummy variables and a power exponent term for handling the seasonality and nonlinear patterns in time series. Furthermore, an intelligent algorithm matching framework is proposed to augment the flexibility of the newly developed model. For practical purposes, the new methodology is contrasted against a range of benchmarks encompassing statistical, machine-learning, and traditional grey models in forecasting the quarterly total renewable energy consumption in the United States. The proposed model exhibits over 27% improvement rates over its counterparts, achieving the most superior predictive accuracies of 1.45%, 39.27, and 0.79 in MAPEP, RMSEP, and MASEP metrics, respectively. Furthermore, the probability density and sample size analyses are conducted to validate the robustness of the new model, confirming its adaptability and stability towards algorithm randomness and historical information volume. Consequently, the novel model is employed to forecast the short-to-long-terms renewable energy consumption in the U.S., showcasing an upward trend and seasonal fluctuations of the consumption for the forthcoming 24 quarters from 2023Q4 to 2029Q3. These insights can offer valuable implications to the stakeholders such as energy suppliers, utility managers, and policy advocates, highlighting actionable strategies for optimizing renewable energy consumption forecasting and aiding sustainable development initiatives.

Matching UAV-Based Simulated High-Resolution SAR and Real SAR Images

Abstract. Our experiment focuses on generating simulated high-resolution (HR) Synthetic Aperture Radar (SAR) images from the photogrammetric 3D model derived from Unmanned Aerial Vehicle (UAV) imagery. The goal is to match these simulated images with real SAR data of complex scenes containing vegetation, buildings, roads, and the construction of receiving antennas. Simulated SAR reflectivity map was based on ray tracing techniques.Two feature matching algorithms, namely Scale-Invariant Feature Transform (SIFT) and Oriented FAST and Rotated BRIEF (ORB) were employed for co-registration of the simulated to the real SAR images. The effectiveness of these algorithms is evaluated by their matching rates. The experiment results of the matching process highlight the superior performance of ORB over SIFT, attributed to its robustness against rotation variations and noise. ORB emerges as a versatile choice for high-resolution real and simulated SAR registration tasks.

Building Height Extraction Based on Satellite GF-7 High-Resolution Stereo Image

Abstract. High-resolution remote sensing images can distinguish objects of smaller size, so as to more clearly express the texture features and structural information of objects, and provide a data source for the development of large-scale mapping, high-precision stereo measurement and other fields. The purpose of this paper is to complete the height estimation of the buildings by analyzing the stereoscopic observation formed by the front and rear view images of the Gaofen-7 line array CCD. After determining the roof profile of the building on the rear-view image, assuming a series of object elevations of the building, that is, searching for elevations with a certain step distance within a certain elevation search range, adopt the object-based image matching VLL algorithm, Through the RFM imaging model of the Gaofen-7 sensor, the rear-view contour is projected to the front-view image, and then the PSNR is selected as the similarity measure of the window, and the similarity of the image block area corresponding to the front- and rear-view contour is calculated. Corresponds to the hypothetical object elevation as the estimated height of the building. Under the technical route of this paper, the height of buildings on high-resolution images can be estimated to a level within 3 meters of accuracy.

Hybrid topic modeling method based on dirichlet multinomial mixture and fuzzy match algorithm for short text clustering

Topic modeling methods proved to be effective for inferring latent topics from short texts. Dealing with short texts is challenging yet helpful for many real-world applications, due to the sparse terms in the text and the high dimensionality representation. Most of the topic modeling methods require the number of topics to be defined earlier. Similarly, methods based on Dirichlet Multinomial Mixture (DMM) involve the maximum possible number of topics before execution which is hard to determine due to topic uncertainty, and many noises exist in the dataset. Hence, a new approach called the Topic Clustering algorithm based on Levenshtein Distance (TCLD) is introduced in this paper, TCLD combines DMM models and the Fuzzy matching algorithm to address two key challenges in topic modeling: (a) The outlier problem in topic modeling methods. (b) The problem of determining the optimal number of topics. TCLD uses the initial clustered topics generated by DMM models and then evaluates the semantic relationships between documents using Levenshtein Distance. Subsequently, it determines whether to keep the document in the same cluster, relocate it to another cluster, or mark it as an outlier. The results demonstrate the efficiency of the proposed approach across six English benchmark datasets, in comparison to seven topic modeling approaches, with 83% improvement in purity and 67% enhancement in Normalized Mutual Information (NMI) across all datasets. The proposed method was also applied to a collected Arabic tweet and the results showed that only 12% of the Arabic short texts were incorrectly clustered, according to human inspection.

Traffic trajectory data analysis technology based on HMM model map matching algorithm.

The rapid growth of traffic trajectory data and the development of positioning technology have driven the demand for its analysis. However, in the current application scenarios, there are some problems such as the deviation between positioning data and real roads and low accuracy of existing trajectory data traffic prediction models. Therefore, a map matching algorithm based on hidden Markov models is proposed in this study. The algorithm starts from the global route, selects K nearest candidate paths, and identifies candidate points through the candidate paths. It uses changes in speed, angle, and other information to generate a state transition matrix that match trajectory points to the actual route. When processing trajectory data in the experiment, K = 5 is selected as the optimal value, the algorithm takes 51 ms and the accuracy is 95.3%. The algorithm performed well in a variety of road conditions, especially in parallel and mixed road sections, with an accuracy rate of more than 96%. Although the time loss of this algorithm is slightly increased compared with the traditional algorithm, its accuracy is stable. Under different road conditions, the accuracy of the algorithm is 98.3%, 97.5%, 94.8% and 96%, respectively. The accuracy of the algorithm based on traditional hidden Markov models is 95.9%, 95.7%, 95.4% and 94.6%, respectively. It can be seen that the accuracy of the algorithm designed has higher precision. The experiment proves that the map matching algorithms based on hidden Markov models is superior to other algorithms in terms of matching accuracy. This study makes the processing of traffic trajectory data more accurate.

Application of image rendering based on improved neural networks and sensors in landscape design

Landscape planning and design is an indispensable part of modern urban construction. In landscape design, rendering technology can transform designers' imagination into actual images, allowing people to better understand the design intent. In order to solve the problem of insufficient precision in image rendering in landscape design, this paper proposes a landscape design image rendering technology based on improved neural network technology. This technology combines stereo matching algorithms, which evaluate the corrected stereo images to calculate the differences between the real scene and the underlying model. The stereo matching algorithm is based on the basic model and obtains accurate depth information from a wide range image pair, making the rendered landscape image more realistic and precise. The experiment demonstrates the effectiveness of the landscape design image rendering system through two important aspects: processing speed and parallax accuracy. The development of image rendering technology in landscape design cannot be separated from the mutual promotion of theory and practice. The technology proposed in this article has certain practical value and hopes to make more positive contributions to urban construction and cultural inheritance.

Maritime distress target detection algorithm based on YOLOv5s‐EFOE network

AbstractTraditional maritime search and rescue methods mainly rely on manual operation, which takes a long time to identify and results in poor search and rescue results. This paper applies computer vision technology to the field of maritime distress object detection and proposes an improved object detection algorithm YOLOv5s‐EFOE based on the YOLOv5s algorithm. Firstly, the authors change the detection head of the YOLOv5s algorithm to use a mixed channel strategy to build a more efficient decoupling head, reducing the number of 3 × 3 convolutional layers in the middle of the decoupling head to only one. The width of the head is scaled by the width multipliers of Backbone and Neck. Secondly, in conjunction with the SimOTA matching algorithm, the positive samples of the target to be tested are dynamically allocated to improve the recognition ability of different targets. Finally, considering the low pixel value of the maritime distress target from the perspective of unmanned aerial vehicles (UAV), the loss function CIoU in the original YOLOv5s is changed to EIoU. EIoU not only considers the distance and aspect ratio of the centre point, but also considers the true difference between the width and height of the prediction box and the real box, which improves the prediction accuracy of the anchor box. Experiments are conducted on a subset of the public dataset SeaDronesSee. The of the YOLOv5s‐EFOE model proposed in this paper reached 75.9%, reached 79.9%, and reached 44.5%. These indicators are superior to the original YOLOv5s model, YOLOv7 series models, and YOLOv8 series models. Compared with the original model, the YOLOv5s‐EFOE model has increased the by 5.4%, by 5.6%, and by 4.6%, improving the difficult to detect and missed detection situations. This model can be deployed on UAVs and can effectively identify maritime distress target, achieving the purpose of search and rescue.

An improvement in block matching algorithms using a dissimilarity measure in frequency domain transform

In this work, the image block matching based on dissimilarity measure is investigated. Moreover, an unsupervised approach is implemented to yield that the algorithms have low complexity (in numbers of operations) compared to the full search algorithm. The state-of-the-art experiments only use discrete cosine transform as a domain transform. In addition, some images were tested to evaluate the algorithms. However, these images were not evaluated according to specific characteristics. So, in this paper, an improved version is presented to tackle the problem of dissimilarity measure in block matching with a noisy environment, using another’s domain transforms or low-pass filters to obtain a better result in block matching implementing a quantitive measure with an average accuracy margin of ± 0.05 is obtained. The theoretical analysis indicates that the complexity of these algorithms is still accurate, so implementing Hadamard spectral coefficients and Fourier filters can easily be adjusted to obtain a better accuracy of the matched block group.

Adopting the euro: A synthetic control approach

We investigate whether joining the European Monetary Union and losing the ability to set monetary policy affected the economic growth of Eurozone countries. We use the synthetic control approach to create a counterfactual scenario for how each Eurozone country would have evolved without adopting the euro. We let this matching algorithm determine which combination of other developed economies best resembles the pre-euro path of twelve Eurozone economies. Our estimates suggest that most countries’ economic growth was not significantly affected. There were some mild losers (France, Germany, Italy, and Portugal) and a clear winner (Ireland). The drivers of these economic gains and losses are heterogeneous. First, we find that Ireland’s economic gains are more modest when excluding profits and income earned by foreigners. Second, our results show that adopting the euro spurred government consumption and trade and deterred private consumption and investment, on average.

Pattern matching algorithms in blockchain for network fees reduction

AbstractBlockchain received a vast amount of attention in recent years and is still growing. The second generation of blockchain, such as Ethereum, allows execution of almost any program in EVM, making it a global protocol for distributed applications. The code deployment and each operation performed in EVM cost the network fee called gas, whose price varies and can be significant. That is why code optimization and well-chosen algorithms are crucial in programming on the blockchain. This paper evaluates the gas usage of several exact pattern matching algorithms on the EVM. We also propose an efficient implementation of the algorithms in the Solidity/YUL language. We evaluate the gas fees of all the algorithms for different parameters (such as pattern length, alphabet size, and text size). We show a significant gas fee and execution time reduction with up to 22-fold lower gas usage and 55-fold speed-up compared to StringUtils (a popular Solidity string library).

Semantic matching in GUI test reuse

Reusing test cases across apps that share similar functionalities reduces both the effort required to produce useful test cases and the time to offer reliable apps to the market. The main approaches to reuse test cases across apps combine different semantic matching and test generation algorithms to migrate test cases across Android apps. In this paper we define a general framework to evaluate the impact and effectiveness of different choices of semantic matching with Test Reuse approaches on migrating test cases across Android apps. We offer a thorough comparative evaluation of the many possible choices for the components of test migration processes. We propose an approach that combines the most effective choices for each component of the test migration process to obtain an effective approach. We report the results of an experimental evaluation on 8,099 GUI events from 337 test configurations. The results attest the prominent impact of semantic matching on test reuse. They indicate that sentence level perform better than word level embedding techniques. They surprisingly suggest a negligible impact of the corpus of documents used for building the word embedding model for the Semantic Matching Algorithm. They provide evidence that semantic matching of events of selected types perform better than semantic matching of events of all types. They show that the effectiveness of overall Test Reuse approach depends on the characteristics of the test suites and apps. The replication package that we make publicly available online (https://star.inf.usi.ch/#/software-data/11) allows researchers and practitioners to refine the results with additional experiments and evaluate other choices for test reuse components.

Multi-source domain adaptive network based on local kernelized higher-order moment matching for rotating machinery fault diagnosis

Unsupervised domain adaptation has been extensively researched in rotating-machinery cross-domain fault diagnosis. A multi-source domain adaptive network based on local kernelized higher-order moment matching is constructed in this research for rotating-machinery fault diagnosis. Firstly, a multi-branch network is designed to map each source-target pair to a domain-specific shared space and to extract domain-invariant features using domain adversarial thought. Then, a local kernelized higher-order moment matching algorithm is proposed to perform fine-grained matching in shared category subspace. Finally, a feature fusion strategy based on the local domain distribution deviation is applied to synthesize the output features of multiple classifiers to obtain diagnostic results. The experimental validation of two-branch and three-branch networks on two public datasets is carried out and average diagnostic accuracies both exceed 99%. The results demonstrate the effectiveness and superiority of the approach.

PnP-UGCSuperGlue: deep learning drone image matching algorithm for visual localization

PnP-UGCSuperGlue: deep learning drone image matching algorithm for visual localization

Homomorphic encrypted Yara rules evaluation

Malware signatures represent a powerful tool for malware detection and classification, widely used by security researchers and security solution providers. Yara rules describe malware based on string patterns that are evaluated on targeted files. Generally, the security provider sends signatures to the client endpoints and the rule evaluation is performed locally, such that the scanned files do not leave the client machines. However, if a zero-day vulnerability is discovered and the security provider exposes the corresponding signature, there is a considerable risk to also disclose the unpatched vulnerability. A solution is represented by homomorphic encrypted Yara rules, which can be evaluated on targeted files without being decrypted. In this article, we propose a homomorphic Yara rules evaluation method and do a comparative analysis with the HENFA method (Homomorphic Encryption for Finite Automata (Genise et al., 2019)). For our method, we propose a fully homomorphic exact string matching algorithm based on the TFHE scheme (Fully Homomorphic Encryption over the Torus (Chillotti et al., 2020)). In order to determine how suitable is the exact string matching approach in practice, we analyze a public Yara rules repository and generate various statistics about the patterns used in the Yara rules. For the two homomorphic Yara rules matching methods, both theoretical and experimental comparative evaluations are presented. Our proposed method implies ciphertexts with smaller sizes and has better efficiency in the average testing scenarios compared to the HENFA method.

Outcomes with General Anesthesia Compared to Conscious Sedation for Endovascular Treatment of Medium Vessel Occlusions: Results of an International Multicentric Study.

Optimal anesthetic strategy for the endovascular treatment of stroke is still under debate. Despite scarce data concerning anesthetic management for medium and distal vessel occlusions (MeVOs) some centers empirically support ageneral anesthesia (GA) strategy in these patients. We conducted an international retrospective study of MeVO cases. Apropensity score matching algorithm was used to mitigate potential differences across patients undergoing GA and conscious sedation (CS). Comparisons in clinical and safety outcomes were performed between the two study groups GA and CS. The favourable outcome was defined as amodified Rankin Scale (mRS) 0-2 at 90days. Safety outcomes were 90-days mortality and symptomatic intracranial hemorrhage (sICH). Predictors of afavourable outcome and sICH were evaluated with backward logistic regression. After propensity score matching 668 patients were included in the CS and 264 patients in the GA group. In the matched cohort, either strategy CS or GA resulted in similar rates of good functional outcomes (50.1% vs. 48.4%), and successful recanalization (89.4% vs. 90.2%). The GA group had higher rates of 90-day mortality (22.6% vs. 16.5%, p < 0.041) and sICH (4.2% vs. 0.9%, p = 0.001) compared to the CS group. Backward logistic regression did not identify GA vs CS as apredictor of good functional outcome (OR for GA vs CS = 0.95 (0.67-1.35)), but GA remained asignificant predictor of sICH (OR = 5.32, 95% CI 1.92-14.72). Anaesthetic strategy in MeVOs does not influence favorable outcomes or final successful recanalization rates, however, GA may be associated with an increased risk of sICH and mortality.

Zerohunger Link-Bridging Food Waste and Hunger Gaps-An Integrated Approach

The Food and Agriculture Organisation (FAO) of the UN estimates that 1.3 billion tonnes, or one-third, of the food produced for human consumption is lost or wasted, making food waste a major global issue. About 40% of food produced in India is wasted, and homes, restaurants, hotels, weddings, and other gathering places are major contributors to this problem. In India, a large number of nonprofit organisations are actively trying to collect excess food and provide it to people in need in order to alleviate hunger, malnutrition, and food waste. The objective of this proposed project is to manage a prepared food supply chain through the development of an innovative application, with the goal of addressing urban excess food waste and hunger-related fatalities. This programme will make it easier to connect surplus and deficient food resources within a community network by using an ERP paradigm. Administrators will be able to monitor donor and recipient information, assess hotels according on their contributions and input, and efficiently manage logistics thanks to the technology. Trusts and beneficiaries will be able to register, use a matching algorithm to find local food resources, choose and request food, communicate directly with donors, and offer insightful feedback. Donors will have the ability to sign up, enter the information of the food that is provided, reply to requests, and get in touch with recipients and trusts. Additionally, staff members and delivery persons will be equipped with the necessary tools to register, oversee pickups and deliveries, and send out timely alerts when they are finished. In the end, this all-encompassing strategy seeks to reduce food waste and lessen hunger in communities by establishing an effective and transparent method for the redistribution of excess food resources

Three 1-bit Speckle-embedded pulse-width modulation patterns for robust absolute 3D measurement

Abstract In 3D shape measurement techniques using structured light, 1-bit pulse-width modulation (PWM) patterns and 1-bit speckle patterns can be projected at high speed. However, when combining PWM and speckle patterns to integrate their advantages, the decoupling problem is insurmountable. In this work, a novel 1-bit speckle-embedded pulse-width-modulation (SP-PWM) patterns was proposed to achieve absolute 3D shape measurement using only three binary patterns. Our method consists of three main steps: First, a sinusoidal pattern reconstruction network (SPRNet) was proposed to eliminate the high-order harmonics and speckle patterns in the SP-PWM patterns and obtain high-quality sinusoidal patterns. Second, a multi-temporal spatial correlation matching algorithm (MTSCMA) was proposed to obtain a coarse disparity map from the three SP-PWM patterns. Third, the high-accuracy wrapped phase map is used as an additional constraint for refining the coarse disparity map to obtain the final high-accuracy disparity map for absolute 3D measurement without phase unwrapping. Our method combines the advantages of fringe projection profilometry techniques for high-precision wrapped phase retrieval and speckle correlation matching algorithms for robust and unambiguous disparity map calculation. The experimental results demonstrated that our method could realize high-precision absolute 3D shape measurement with an accuracy of 0.057mm using only three 1-bit SP-PWM patterns. Furthermore, different simulation noises were used to demonstrate the robustness of the proposed method.

Urban Building Height Extraction from Gaofen-7 Stereo Satellite Images Enhanced by Contour Matching

The traditional method for extracting the heights of urban buildings involves utilizing dense matching algorithms on stereo images to generate a digital surface model (DSM). However, for urban buildings, the disparity discontinuity issue that troubles the dense matching algorithm makes the elevations of high-rise buildings and the surrounding areas inaccurate. The occlusion caused by trees in greenbelts makes it difficult to accurately extract the ground elevation around the building. To tackle these problems, a method for building height extraction from Gaofen-7 (GF-7) stereo images enhanced by contour matching is presented. Firstly, a contour matching algorithm was proposed to extract accurate building roof elevation from GF-7 images. Secondly, a ground filtering algorithm was employed on the DSM to generate a digital elevation model (DEM), and ground elevation can be extracted from this DEM. The difference between the rooftop elevation and the ground elevation represents the building height. The presented method was verified in Yingde, Guangzhou, Guangdong Province, and Xi’an, Shaanxi Province. The experimental results demonstrate that our proposed method outperforms existing methods in building height extraction concerning accuracy.

A Smart Food Donation System using Mobile Technology to Reduce Food Waste

Food wastage is a significant global challenge, with approximately one third of food produced for human consumption, amounting to 1.3 billion tonnes, being lost or wasted according to the Food and Agriculture Organization (FAO) of the UN. In India, around 40% of food produced goes to waste, with sources such as weddings, events, restaurants, hostels, and households contributing substantially to this issue. Numerous non-profit organizations in India are actively working to combat hunger, malnutrition, and food wastage by collecting surplus food and distributing it to those in need. This proposed project aims to tackle urban excess food waste and hunger-related deaths by developing an innovative application designed to manage a cooked food supply chain. Using an ERP model, this application will facilitate the connection between surplus and deficient food resources within a community network. The system will empower administrators to oversee donor and recipient details, evaluate hotels based on their contributions and feedback, and coordinate logistics effectively. Trusts and recipients will have the ability to register, locate nearby food resources through a matching algorithm, select and request food, engage in direct communication with donors, and provide valuable feedback. Donors will be able to register, input available food details, respond to requests, and communicate with trusts and recipients. Furthermore, employees and delivery personnel will have the tools to register, manage pickups and deliveries, and provide timely notifications upon completion. This comprehensive approach aims to establish an efficient and transparent solution for the redistribution of surplus food resources, ultimately contributing to the reduction of food waste and alleviation of hunger in communities