Object Matching Research Articles

Neural Thermodynamic Integration: Free Energies from Energy-Based Diffusion Models.

Thermodynamic integration (TI) offers a rigorous method for estimating free-energy differences by integrating over a sequence of interpolating conformational ensembles. However, TI calculations are computationally expensive and typically limited to coupling a small number of degrees of freedom due to the need to sample numerous intermediate ensembles with sufficient conformational-space overlap. In this work, we propose to perform TI along an alchemical pathway represented by a trainable neural network, which we term Neural TI. Critically, we parametrize a time-dependent Hamiltonian interpolating between the interacting and noninteracting systems and optimize its gradient using a score matching objective. The ability of the resulting energy-based diffusion model to sample all intermediate ensembles allows us to perform TI from a single reference calculation. We apply our method to Lennard-Jones fluids, where we report accurate calculations of the excess chemical potential, demonstrating that Neural TI reproduces the underlying changes in free energy without the need for simulations at interpolating Hamiltonians.

When Reality Meets Perception: Well-Being Effects of Objective and Subjective Person-Culture Matches in Religiosity

Previous research found that religious individuals report higher well-being when they live in a religious country—that is, when there is an objective match—or when they perceive their country as religious—that is, when there is a subjective match. Objective and subjective person-culture matches are typically considered different operationalizations of the same phenomenon. The present research, in contrast, suggests that objective and subjective matches are two conceptually distinct phenomena, each independently contributing to higher well-being: Across 24 countries ( N = 10,195), individuals tended to experience higher global, psychological, and physical well-being when both objective and subjective matches were high. Notably, only objective (but not subjective) matches were related to higher social well-being and life satisfaction, whereas only subjective matches were related to positive affect. Thus, jointly investigating objective and subjective matches is crucial to avoid incomplete or even incorrect conclusions about person-culture match effects on well-being.

Unified Sampling and Ranking for Protein Docking with DFMDock.

Diffusion models have shown promise in addressing the protein docking problem. Traditionally, these models are used solely for sampling docked poses, with a separate confidence model for ranking. We introduce DFMDock (Denoising Force Matching Dock), a diffusion model that unifies sampling and ranking within a single framework. DFMDock features two output heads: one for predicting forces and the other for predicting energies. The forces are trained using a denoising force matching objective, while the energy gradients are trained to align with the forces. This design enables our model to sample using the predicted forces and rank poses using the predicted energies, thereby eliminating the need for an additional confidence model. Our approach outperforms the previous diffusion model for protein docking, DiffDock-PP, with a sampling success rate of 44% compared to its 8%, and a Top- 1 ranking success rate of 16% compared to 0% on the Docking Benchmark 5.5 test set. In successful decoy cases, the DFMDock Energy forms a binding funnel similar to the physics-based Rosetta Energy, suggesting that DFMDock can capture the underlying energy landscape.

Object matching of visible–infrared image based on attention mechanism and feature fusion

Object matching can be viewed as an image patch matching problem, which is widely employed in image fusion, image retrieval and other computer vision fields. In this paper, we regard image matching as a regression and classification task and propose a visible and infrared image matching network named AMFFNet. AMFFNet adopts a Siamese network structure and utilizes a residual network with an attention mechanism to extract features from the input images to obtain feature maps, and fuses these feature maps. AMFFNet then performs classification and regression on the fused feature maps to achieve matching. The classification operation identifies whether the predicted area is the object, while the regression operation determines the coordinates and size of the predicted box. To improve the matching performance of the network, we utilize the center-ness branch in the network and use Generalized Intersection over Union (GIoU) loss during training. We rearrange the existing dataset to provide a sufficient visible–infrared image matching dataset. Experimental results demonstrate that the proposed method achieves superior matching mean Average Precision (mAP) compared to other methods.

Large-scale integration of remotely sensed and GIS road networks: A full image-vector conflation approach based on optimization and deep learning

Road networks play an important role in the sustainable development of human society. Conventionally, there are two sources of road data acquisition: road extraction from Remote Sensing (RS) imagery and GIS based map production. Each method has its limitations. The RS road extraction methods are primarily raster-based and the extracted roads are not directly usable in GIS due to their fragmented and noisy nature, while vector-based methods cannot utilize rich raster information. Further more, the vector and raster data can have discrepancies for various reasons. Efficient road data production requires an image-vector conflation process that can match and combine raster and vector-based road data automatically.In this study, we propose a full image-vector conflation framework that directly integrates image and vector road data by appropriately transforming extracted roads from imagery and establishing a match relation between these roads and a credible target GIS road dataset. Based on analyzing these match relations, we propose new metrics for measuring the degree of agreement between the raster and vector road data. The proposed framework combines state-of-the-art deep learning methods for image segmentation and optimization-based models for object matching. We prepared a large-scale high-resolution road dataset covering two counties in Kansas, US. Using trained models from one of the two counties, we were able to extract road segments in the other county and match them to the TIGER/Line roads.Our experiments show that conventional performance metrics for road extraction (e.g. IoU) are insufficient for measuring the degree of agreement between image and vector roads as they are pixel-based and are too sensitive to spatial displacement. Instead, the newly defined vector-based agreement metrics are needed for image-vector conflation purposes. Experiments show that, by the vector-based metrics, nearly 90% of GIS road lengths in the study area were extracted and over 90% of extracted roads matched the target GIS roads. The new framework streamlines raster-vector conflation of roads and can potentially expedite relevant geospatial analyses regarding change detection, disaster monitoring and GIS data production, among others.

Correction: Yang et al. Maritime Electro-Optical Image Object Matching Based on Improved YOLOv9. Electronics 2024, 13, 2774

In the original publication [...]

Aerial Map-Based Navigation by Ground Object Pattern Matching

This paper proposes a novel approach to map-based navigation for unmanned aircraft. The proposed approach employs pattern matching of ground objects, not feature-to-feature or image-to-image matching, between an aerial image and a map database. Deep learning-based object detection converts the ground objects into labeled points, and the objects’ configuration is used to find the corresponding location in the map database. Using the deep learning technique as a tool for extracting high-level features reduces the image-based localization problem to a pattern-matching problem. The pattern-matching algorithm proposed in this paper does not require altitude information or a camera model to estimate the horizontal geographical coordinates of the vehicle. Moreover, it requires significantly less storage because the map database is represented as a set of tuples, each consisting of a label, latitude, and longitude. Probabilistic data fusion with the inertial measurements by the Kalman filter is incorporated to deliver a comprehensive navigational solution. Flight experiments demonstrate the effectiveness of the proposed system in real-world environments. The map-based navigation system successfully provides the position estimates with RMSEs within 3.5 m at heights over 90 m without the aid of the GNSS.

Intuitive network topology.

Topology is the branch of mathematics that seeks to understand and describe spatial relations. A number of studies have examined the human perception of topology-in particular, whether adults and young children perceive and differentiate objects based on features like closure, boundedness, and emptiness. Topology is about more than "wholes and holes," however; it also offers an efficient language for representing network structure. Topological maps, common for subway systems across the world, are an example of how effective this language can be. Inspired by this idea, here we examine "intuitive network topology." We first show that people readily differentiate objects based on several different features of topological networks. We then show that people both remember and match objects in accordance with their topology, over and above substantial variation in their surface features. These results demonstrate that humans possess an intuitive understanding for the basic topological features of networks, and hint at the possibility that topology may serve as a format for representing relations in the mind. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Remote refocusing for multi-scale imaging.

The technique of remote focusing (RF) has attracted considerable attention among microscopists due to its ability to quickly adjust focus across different planes, thus facilitating quicker volumetric imaging. However, the difficulty in changing objectives to align with a matching objective in a remote setting while upholding key requirements remains a challenge. We aim to propose a customized yet straightforward technique to align multiple objectives with a remote objective, employing an identical set of optical elements to ensure meeting the criteria of remote focusing. We propose a simple optical approach for aligning multiple objectives with a singular remote objective to achieve a perfect imaging system. This method utilizes readily accessible, commercial optical components to meet the fundamental requirements of remote focusing. Our experimental observations indicate that the proposed RF technique offers at least comparable, if not superior, performance over a significant axial depth compared with the conventional RF technique based on commercial lenses while offering the flexibility to switch the objective for multi-scale imaging. The proposed technique addresses various microscopy challenges, particularly in the realm of multi-resolution imaging. We have experimentally demonstrated the efficacy of this technique by capturing images of focal volumes generated by two distinct objectives in a water medium.

Noise-robust re-identification with triple-consistency perception

Traditional re-identification (ReID) methods heavily rely on clean and accurately annotated training data, rendering them susceptible to label noise in real-world scenarios. Although some noise-robust learning methods have been proposed and achieved promising recognition performance, however, most of these methods are designed for the image classification task and they are not suitable in ReID (engaging in the association and matching of objects rather than solely focusing on their identification). To address this problem, in this paper, we propose a Triple-consistency Perception based Noise-robust Re-identification Model (TcP-ReID), in which we make the model mine and focus more on the clean samples and reliable relationships among samples from different perspectives. Specifically, the self-consistency strategy guides the model to emphasize and prioritize clean samples, thereby preventing overfitting to noise labels during the initial stages of model training. Rather than focusing solely on individual samples, the context-consistency loss exploits similarities between samples in the feature space, promoting predictions for each sample to align with those of its nearest neighbors. Moreover, to further enforce the robustness of our model, a Jensen-Shannon divergence based cross-view consistency loss is introduced by encouraging the consistency of samples across different views. Extensive experiments demonstrate the superiority of the proposed TcP-ReID over the competing methods under instance-dependent noise and instance-independent noise. For instance, on the Market1501 dataset, our method achieves 85.8% in rank-1 accuracy and 56.3% in mAP score (5.6% and 8.7% improvements) under instance-independent noise with noise ratio 50%, and similarly 5.7% and 1.4% under instance-dependent label noise.

Maritime Electro-Optical Image Object Matching Based on Improved YOLOv9

The offshore environment is complex during automatic target annotation at sea, and the difference between the focal lengths of visible and infrared sensors is large, thereby causing difficulties in matching multitarget electro-optical images at sea. This study proposes a target-matching method for visible and infrared images at sea based on decision-level topological relations. First, YOLOv9 is used to detect targets. To obtain markedly accurate target positions to establish accurate topological relations, the YOLOv9 model is improved for its poor accuracy for small targets, high computational complexity, and difficulty in deployment. To improve the detection accuracy of small targets, an additional small target detection head is added to detect shallow feature maps. From the perspective of reducing network size and achieving lightweight deployment, the Conv module in the model is replaced with DWConv, and the RepNCSPELAN4 module in the backbone network is replaced with the C3Ghost module. The replacements significantly reduce the number of parameters and computation volume of the model while retaining the feature extraction capability of the backbone network. Experimental results of the photovoltaic dataset show that the proposed method improves detection accuracy by 8%, while the computation and number of parameters of the model are reduced by 5.7% and 44.1%, respectively. Lastly, topological relationships are established for the target results, and targets in visible and infrared images are matched based on topological similarity.

3D Deformable Object Matching Using Graph Neural Networks

Considering the current advancements in computer vision it can be observed that most of it is focused on two-dimensional imagery. This includes problems such as classification, regression, and the lesser-known object matching problem. While object matching ca be viewed as a solved problem in a two-dimensional space, for a three-dimensional space there is a long way to go, especially for non-rigid objects. The problem is focused on matching a given object to a target object. We propose a solution based on Graph Neural Networks that tries to generalize over multiple objects at once, based on self-attention and cross-attention blocks for the network. To test our solution, we utilised five convolutional operators for the layers of the model. The convolutional operators we compared included GCNConv, ChebConv, SAGEConv, TAGConv, and FeaStConv. This paper aims to find the best operators for our architecture and the task. Our approach obtained favourable results for predicting the barycentric weights for the model, while struggling to predict the triangle indexes. The best results were obtained for the models using GCNConv, for the triangles index prediction and FeaStConv for the barycentric coordinates prediction. Keywords: graph neural networks, object matching, 3D objects, deformable objects.

An element perception and state recognition method oriented to complex assembled product

Quality inspection during assembly process has an important impact on the performance and service life of the final product. Automated inspection solutions based on machine vision and artificial intelligence greatly improve the efficiency of inspection. However, the current mainstream large-scene inspection solutions, such as mobile vision platforms, are difficult to apply widely due to cost issues. Therefore, portable vision inspection solutions have gradually gained attention. However, the bottleneck technologies that limit this solution are full-element perception and occlusion judgment under free viewing angle, robust object matching method. To address the above problems, This paper proposes a free-viewpoint-based element perception and state recognition scheme. An improved structure estimation model is adopted to establish a bridge between virtual and actual mapping under the support of small-sample data, and all elements are located and occlusion analyzed by a 3D model-constrained view evolution algorithm. In addition, a hierarchy matching and optimization method is established to complete visual analysis. Experimental results show that this scheme performs well in structure estimation, element localization, occlusion judgment, state recognition, and work efficiency, demonstrating high application value.

Detours in Shared Rides

Detours are considered key for the efficient operation of a shared rides service, but they are also a major pain point for consumers of such services. This paper studies the relationship between the value generated by shared rides and the detours they create for riders. We establish a limit on the sum of value and detour, and we prove that this leads to a tight bound on the Pareto frontier of values and detours in a general setting with an arbitrary number of requests. We explicitly compute the Pareto frontier for one family of city topologies and construct it via simulation for several more networks, including one based on ride-sharing data from commute hours in Manhattan. We find that average detours are usually small, even in low-demand-density settings. We also find that by carefully choosing the match objective, detours can be reduced with a relatively small impact on values and that the density of ride requests is far more important than detours for the effective operations of a shared rides service. In response, we propose that platforms implement a two-product version of shared rides and limit the worst-case detours of its users. This paper was accepted by Hamid Nazerzadeh, data science. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2020.03125 .

Sentinel 1a-2a Incorporating an Object-Based Image Analysis Method for Flood Mapping and Extent Assessment

Abstract. This study presents flood extent extraction and mapping from Sentinel images. Here we suggest an algorithm for extracting flooded areas from object-based image analysis (OBIA) using Sentinel-1A and Sentinel-2A images to map and assess the flood extent from the beginning to one week after the event. This study used multi-scale parameters in OBIA for image segmentation. First, we identified the flooded regions by applying our proposed algorithm on the Sentinel-1A. Then, to evaluate the effects of the flood on each land-use/land cover (LULC) class, Sentinel-2A images is classified using the OBIA after the event. Besides, we also used the threshold method to compare the proposed algorithm applying OBIA to determine the efficiency in computing parameters for change detection and flood extent mapping. The findings revealed the best performance for the segmentation process with an Object Fitness Index (OFI) is 0.92 when the scale parameter of 60 is applied. The results also show that 2099.4 km2 of the study area is flooded at the beginning of the flood. Furthermore, we found that the most flooded LULC classes are agricultural land and orchards with 695.28km2 (32.4%) and 708.63 km2 (33.7%), respectively. In comparison, about 33.9% of the remaining flooded area has occurred in other classes (i.e., fish farm, built-up, bare land and water bodies). The resulting object of each scale parameter was evaluated by Object Pureness Index (OPI), Object Matching Index (OMI), and OFI. Finally, our Overall Accuracy (OA) method incorporated field data using the Global Positioning System (GPS) shows 93%, 90%, and 89% for LULC, flood map (i.e., using our proposed algorithm), and threshold method, respectively.

Automated tariff design for energy supply–demand matching based on Bayesian optimization: Technical framework and policy implications

With the emergence of renewable energy sources, designing tariffs becomes crucial to match unstable energy supply with varying energy demand. However, traditional tariff design frameworks depend on a certain type of tariff and the demand response of customers, which makes designing tariffs rigid and hard to automate. To overcome the challenges, in this paper, we propose an automated tariff design framework for energy supply–demand matching based on Bayesian optimization. The proposed framework allows us to design an optimized tariff for any given objective of energy matching automatically. This automation can provide flexibility in designing tariffs for energy management. Moreover, we propose a novel matching evaluation metric focusing on the mismatch of actual energy supply–demand patterns related to the stability and reliability of energy management systems. Adopting it along with traditional economic utility, actual energy pattern matching and economic values can be deliberately balanced in the automated tariff design. Through simulations with real datasets, we demonstrate that the proposed framework can effectively design tariffs in the real world such as time-of-use and demand charge tariffs, considering energy patterns and economic costs separately. Finally, we provide potential policy implications resulting from the automated tariff design.

A Fast Detection Algorithm for Change Detection in National Forestland “One Map” Based on NLNE Quad-Tree

The National Forestland “One Map” applies the boundaries and attributes of sub-elements to mountain plots by means of spatial data to achieve digital management of forest resources. The change detection and analysis of forest space and property is the key to determining the change characteristics, evolution trend and management effectiveness of forest land. The existing spatial overlay method, rasterization method, object matching method, etc., cannot meet the requirements of high efficiency and high precision at the same time. In this paper, we investigate a fast algorithm for the detection of changes in “One Map”, taking Sichuan Province as an example. The key spatial characteristic extraction method is used to uniquely determine the sub-compartments. We construct an unbalanced quadtree based on the number of maximum leaf node elements (NLNE Quad-Tree) to narrow down the query range of the target sub-compartments and quickly locate the sub-compartments. Based on NLNE Quad-Tree, we establish a change detection model for “One Map” (NQT-FCDM). The results show that the spatial feature combination of barycentric coordinates and area can ensure the spatial uniqueness of 44.45 million sub-compartments in Sichuan Province with 1 m~0.000001 m precision. The NQT-FCDM constructed with 1000–6000 as the maximum number of leaf nodes has the best retrieval efficiency in the range of 100,000–500,000 sub-compartments. The NQT-FCDM shortens the time by about 75% compared with the traditional spatial union analysis method, shortens the time by about 50% compared with the normal quadtree and effectively solves the problem of generating a large amount of intermediate data in the spatial union analysis method. The NQT-FCDM proposed in this paper improves the efficiency of change detection in “One Map” and can be generalized to other industries applying geographic information systems to carry out change detection, providing a basis for the detection of changes in vector spatial data.

Using Content Relevance and Representativeness Indices in Instrument Revision

ABSTRACT Combining methods from earlier content validity and more contemporary content alignment studies may allow a more complete evaluation of the meaning of test scores than if either set of methods is used on its own. This article distinguishes item relevance indices in the content validity literature from test representativeness indices in the subsequent content alignment literature. We then demonstrate that setting minimum thresholds for both types of indices in a sequential process can affect conclusions about test content representativeness, which are integral to validity evaluation. We illustrate this outcome using test item-to-target domain objective match data from subject-matter expert panelists who reviewed sixteen US state achievement tests. Although the illustration incorporates educational achievement test data, the process has broader applicability to development of survey questionnaires, workplace tests, and other instruments.

A wide drive frequency matching method for harmonic suppression and voltage stabilization of ultrasonic motors.

To reduce harmonic components, balance system impedance, and stabilize driving voltage, an additional matching circuit is required for ultrasonic motors (USMs) driver. However, the performance of inductor or capacitor matching can be seriously weakened with changes in driving frequency. Therefore, this paper presents a simple and effective LC matching method against driving frequency adjustment for USMs. First, the driving scheme of the USM is proposed and the electromechanical coupling model is analyzed. Subsequently, the output characteristics of the full-bridge inverter are derived theoretically when the driving frequency deviates from the mechanical resonant frequency. Then, the impedance circular transform method is proposed, which can intuitively analyze the effect of matching parameters on the voltage amplitude. A matching objective function is established that can consider both the voltage stabilization and harmonic suppression. The matching parameters are solved using random weight particle swarm optimization. Simulations and experiments demonstrate that within the operating frequency of the USM, the proposed matching method can effectively prevent overvoltage and suppress harmonic components. Furthermore, compared with the existing resonant matching method, the proposed matching method can realize more stable driving capability at different frequencies. The proposed method could be useful for USMs' variable-frequency driver design.

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.