Final Matching Research Articles

Suppressing the non-switching contribution in BiFeO3-Bi4Ti3O12 based thin film composites to produce room-temperature multiferroic behavior

Exploiting the exceptional multiferroic characteristics of BiFeO3-based systems depends largely on controlling the high leakage currents that often constrain the ferroelectric response of this material. This limiting circumstance is even more restrictive in the film geometry, where the high area/volume ratio can add complications related to the uncontrolled loss of a particularly volatile element such as bismuth. In this work we address the suppression of such non-switching contribution by preparing BFO-BiT thin film composites and using a low-temperature processing protocol in a fully aqueous medium. With an adequate and systematic doping of both oxides, the produced composites show both magnetic and ferroelectric response at room temperature, in a process that is also related to the fine matching between the two crystal lattices involved. The results obtained further indicate the possibility of applying a simple, sustainable protocol with high scalability prospects to fabricate exploitable multiferroic systems, i.e. with no need for large energy inputs nor sophisticated equipment.

Design of Sports Training Simulation System for Children Based on Improved Deep Neural Network.

With the development of AI technology, human-computer interaction technology is no longer the traditional mouse and keyboard interaction. AI and VR have been widely used in early childhood education. In the process of the slow development and application of voice interaction, visual interaction, action interaction, and other technologies, multimodal interaction technology system has become a research hotspot. In this paper, dynamic image capture and recognition technology is integrated into early childhood physical education for intelligent interaction. According to the basic movement process and final node matching in children's sports training to judge children's physical behavior ability, attention is paid to identify the accuracy and safety of movement. The input images and questions are from the abstract clipart dataset of dynamic image recognition and the self-made 3D dataset of Web3D dynamic motion scene with the same style, which is similar to the action content in the actual preschool training teaching. Therefore, according to the idea of process capture and target recognition, on the basis of the original conditions of the recognition model, a new recognition model is developed through Zheng's target detector. The modified model is characterized by higher accuracy. Weapons need to combine process recognition and result recognition. The experimental results show that the improved model has the obvious advantages of high precision and fast speed, which provides a new research idea for the development of children's physical training simulation.

A Multi-Scale Residential Areas Matching Method Considering Spatial Neighborhood Features

Residential areas is one of the basic geographical elements on the map and an important content of the map representation. Multi-scale residential areas matching refers to the process of identifying and associating entities with the same name in different data sources, which can be widely used in map compilation, data fusion, change detection and update. A matching method considering spatial neighborhood features is proposed to solve the complex matching problem of multi-scale residential areas. The method uses Delaunay triangulation to divide complex matching entities in different scales into closed domains through spatial neighborhood clusters, which can obtain many-to-many matching candidate feature sets. At the same time, the geometric features and topological features of the residential areas are fully considered, and the Relief-F algorithm is used to determine the weight values of different similarity features. Then the similarity and spatial neighborhood similarity of the polygon residential areas are calculated, after which the final matching results are obtained. The experimental results show that the accuracy rate, recall rate and F value of the matching method are all above 90%, which has a high matching accuracy. It can identify a variety of matching relationships and overcome the influence of certain positional deviations on matching results. The proposed method can not only take account of the spatial neighborhood characteristics of residential areas, but also identify complex matching relationships in multi-scale residential areas accurately with a good matching effect.

JOINT BUNDLE ADJUSTMENT OF THERMAL INFRA-RED AND OPTICAL IMAGES BASED ON MULTIMODAL MATCHING

Abstract. Despite the fact that Thermal Infrared (TIR) cameras have been in use for decades, processing of TIR images poses a variety of challenges when compared to optical images, which are captured in the visible part of the electromagnetic spectrum. The estimation of the exterior orientation of TIR cameras by bundle adjustment is a difficult task due to the limited geometric resolution of a TIR camera and the low image quality in terms of contrast and texture compared to optical images. Optical images have a potential to increase TIR external orientation accuracy by incorporating them into a joint bundle adjustment. However, because the modality gap between those two image types is large, classical point matching algorithms typically fail to find matches, making processing both image types in the joint bundle challenging. In order to locate matching points in both modalities, this study suggests using the Edge Histogram Descriptor (EHD) in the frequency domain representation of the images based on phase congruency. To properly allocate edges from the phase congruency, which are then employed in EHD, non-maximum suppression and hysteresis thresholding are used. Considering that both sensors are fixed rigidly to a single platform, the search region for the matching point candidate of the TIR image is determined based on stereo calibration of a thermal/optical stereo setup combined with geometric constraints. The final matching is based on the cosine distance, while RANdom SAmple Consensus (RANSAC) is used in order to eliminate outliers. The findings of this study show that using a joint bundle adjustment with optical images versus a bundle adjustment only with TIR images improves TIR image orientation, which is supported by the increased accuracy of the adjusted Ground Control Point (GCP) coordinates.

Infrared and Visible Image Registration Based on Automatic Robust Algorithm

Image registration is the base of subsequent image processing and has been widely utilized in computer vision. Aiming at the differences in the resolution, spectrum, and viewpoint of infrared and visible images, and in order to accurately register infrared and visible images, an automatic robust infrared and visible image registration algorithm, based on a deep convolutional network, was proposed. In order to precisely search and locate the feature points, a deep convolutional network is introduced, which solves the problem that a large number of feature points can still be extracted when the pixels of the infrared image are not clear. Then, in order to achieve accurate feature point matching, a rough-to-fine matching algorithm is designed. The rough matching is obtained by location orientation scale transform Euclidean distance, and then, the fine matching is performed based on the update global optimization, and finally, the image registration is realized. Experimental results show that the proposed algorithm has better robustness and accuracy than several advanced registration algorithms.

SAR Image Matching Based on Local Feature Detection and Description Using Convolutional Neural Network

Feature detection is a vital step for the image registration process whose target is the misalignment correction among images to increase the convergency level. Deep learning (DL) in remote sensing has become a worldwide sensation. Despite its huge potential, DL has not reached its intended target concerning the applications of Synthetic Aperture Radar (SAR) images. In this study, we focus on matching SAR images using a Convolutional Neural Network. The big challenge in this study is how to modify a pretrained Visual Geometry Group model based on the multispectral dataset to act as a SAR image feature detector where it does not require any prior knowledge about the nature of the SAR feature. Since SAR images have different characteristics from optical images such as SAR dynamic range and imaging geometry, some problems arise and should be considered during the matching process. Despite all these difficulties, results demonstrate the robustness of the registration process where it can provide descriptors that preserve the localization data of features. Also, the proposed approach provides reasonable results compared to the state-of-the-art methods and outperforms the correlation approach and ORB descriptor under scaling. In addition, it may be considered an end-to-end image matching tool for SAR images, although the calculations of fine matching parameters are included.

Multi-phase matching mechanism for stable and optimal resource allocation in cloud manufacturing platforms Using IF-VIKOR method and deferred acceptance algorithm

ABSTRACT Public platforms are one of the most important Cloud Manufacturing modes. Public platforms enable an environment for manufacturers and demanders to freely and directly connect with each other. Exploiting the high potentials of public platforms depends on final matching. This paper has developed a multi-phase matching mechanism for stable and optimal resource allocation in public platform. The proposed mechanism grades the demanders using an intuitionistic fuzzy VIKOR method using three measures of quality, time, and sustainability. Then, the mechanism clusters the demanders based on these three measures; and finally, allocates the clusters using the Deferred Acceptance (DA) algorithm to the manufacturers. The mechanism is examined using a case study from the Iranian automotive industry. The paper has extended and examined the model under three directions of: the grading method impact, clustering analysis impact, and the platform mode impact. Based on the experiments, the intuitionistic fuzzy TOPSIS-DA results on average 4.34% better outcome rather than the intuitionistic fuzzy VIKOR-DA. The proposed heuristic and optimized clustering method results on average 1.09% better solution rather than the KM clustering method. Also, the analysis of the PoS reveals that a private platform yields on average 10% better utility rather than a public platform.

DreamLoc: A Deep Relevance Matching-Based Framework for bug Localization

To improve the software debugging efficiency, bug localization techniques have been developed to automatically locate buggy files based on bug reports. Traditional information retrieval-based bug localization cannot deal with the lexical mismatch, thus its performance is limited. In recent years, some deep learning models have been proposed to learn the semantics of bug reports and source files to bridge the lexical gap. However, their accuracy is still limited as building accurate semantic representations of bug reports and source files is very challenging. Recently, relevance matching was proposed to identify whether a document is relevant to a given query by considering both local matching and global matching. In this work, we propose a novel framework DreamLoc, which utilizes a relevance matching model to locate buggy files. Specifically, DreamLoc conducts the local matching by employing an attention-based mechanism to calculate the matching scores between bug report terms and code snippets. It also conducts the global matching by employing a gating mechanism to aggregate results of local matching and obtain the final matching score between a bug report and a source file. Since the local matching considers the relevance between each word and the global matching differentiates the importance of words, DreamLoc can effectively model the characteristics of bug reports and source files. Experimental results on five benchmark datasets show that DreamLoc outperforms five state-of-the-art models. For example, compared with DeepLoc, a recently proposed approach, the evaluation measures Accuracy@10, MAP, and MRR are improved by 6.4%, 7.4%, and 7.2%, respectively.

A Fingerprint-Based Verification Framework Using Harris and SURF Feature Detection Algorithms

Amongst all biometric-based personal authentication systems, a fingerprint that gives each person a unique identity is the most commonly used parameter for personal identification. In this paper, we present an automatic fingerprint-based authentication framework by means of fingerprint enhancement, feature extraction, and matching techniques. Initially, a variant of adaptive histogram equalization called CLAHE (contrast limited adaptive histogram equalization) along with a combination of FFT (fast Fourier transform), and Gabor filters are applied to enhance the contrast of fingerprint images. The fingerprint is then authenticated by picking a small amount of information from some local interest points called minutiae point features. These features are extracted from the thinned binary fingerprint image with a hybrid combination of Harris and SURF feature detectors to render significantly improved detection results. For fingerprint matching, the Euclidean distance between the corresponding Harris-SURF feature vectors of two feature points is used as a feature matching similarity measure of two fingerprint images. Moreover, an iterative algorithm called RANSAC (RANdom SAmple Consensus) is applied for fine matching and to automatically eliminate false matches and incorrect match points. Quantitative experimental results achieved on FVC2002 DB1 and FVC2000 DB1 public domain fingerprint databases demonstrate the good performance and feasibility of the proposed framework in terms of achieving average recognition rates of 95% and 92.5% for FVC2002 DB1 and FVC2000 DB1 databases, respectively.

Heterogeneous self-supervised interest point matching for multi-modal remote sensing image registration

ABSTRACT The implementation of multi-modal remote sensing image registration is still a challenge in various applications. In this paper, a novel framework called Heterogeneous SuperPoint Network is proposed for multi-modal remote sensing image registration. Heterogeneous SuperPoint Network operates on full-sized images and produces multi-modal interest points accompanied with fixed length descriptors in a single forward pass. Moreover, we develop a strategy for training a base detector that used in conjunction with heterogeneous homographic adaptation to generate pseudo-ground truth interest point labels for unlabeled images in a self-supervised fashion. Then, the interest points and point correspondences obtained by Heterogeneous SuperPoint Network are used to do the final matching with the nearest neighbor (NN) matching and Random sample consensus (RANSAC). Various image sets have been considered in this paper for the evaluation of the proposed approach. Experimental results demonstrate the superior performance of the proposed method over the existing design for the whole data set.

Dual Hashing Index Cancellable Finger Vein Template Based on Gaussian Random Mapping

In the existing cancellable finger vein template protection schemes, the original biometric features cannot be well protected, which results in poor security. In addition, the performance of matching recognition performances after generating a cancellable template is poor. Therefore, a dual hashing index cancellable finger vein template protection based on Gaussian random mapping is proposed in this study. The scheme is divided into an enrollment stage and a verification stage. In the two stages, symmetric data encryption technology was used to generate encryption templates for matching. In the enrollment stage, first, the extracted finger vein features were duplicated to obtain an extended feature vector; then, this extended vector was uniformly and randomly permuted to obtain a permutation feature vector. The above two vectors were combined into a two-dimensional feature matrix. The extended and permuted feature vector made full use of the original biometric features and further enhanced the non-invertibility. Second, a random Gaussian projection vector with m×q dimensions was generated, and a random orthogonal projection matrix was generated by the Schmidt orthogonalization of the previously generated random vector. This approach accurately transferred the characteristics of the biometric features to another feature space and ensured that the biological template is revocable. Finally, the inner product of the two-dimensional feature vector and random orthogonal projection matrix was obtained and superimposed into a row. The dual index values of the largest and second largest values were repeated m times to obtain a hash code for matching. The secondary maximum value index was introduced to adjust the error generated by the random matrix, which improved the recognition rate of the algorithm. In the verification stage, another hash code for matching was generated based on symmetric data encryption technology, and then the two hash codes were cross matched to obtain the final matching result. The experimental results show that this scheme attains good recognition performance with the PolyU and SDUMLA-FV databases, that it meets the design standard for cancellable biometric identification, and that it is robust to security and privacy attacks.

A FAST assembly simulation analysis method for hull blocks with engineering constraints

In order to effectively improve the assembly efficiency for hull blocks, an assembly simulation analysis method considering engineering constraints is proposed in this paper, and an integrated system of shipbuilding accuracy analysis and assembly analysis considering multi-constraints is developed. The method is divided into pre-matching model and fine matching model. In the pre-matching model, an Improved Coherent Point Drift (ICPD) algorithm is used to obtain more accurate initial matching values. The fine matching model firstly uses the Analytic Hierarchy Process (AHP) algorithm to automatically obtain the constrained weights, then the weights vector is used to add the assembly constraints for hull blocks such as straightness, hard point constraints etc. into the multi-objective optimization function. By solving the function, the optimum positioning location and the most reasonable adjustment scheme are obtained. This method shortens the occupancy time of the equipment used to build the hull, reduces the workload of the staff, and improves the efficiency and quality of shipbuilding. The integrated system adds engineering constraints analysis module and the function of automatically finding and eliminating error measurement points. Through the verification of the examples, the integrated system realizes the automation and intellectualization of the assembly for hull blocks.

3MRS: An Effective Coarse-to-Fine Matching Method for Multimodal Remote Sensing Imagery

The fusion of image data from multiple sensors is crucial for many applications. However, there are significant nonlinear intensity deformations between images from different kinds of sensors, leading to matching failure. To address this need, this paper proposes an effective coarse-to-fine matching method for multimodal remote sensing images (3MRS). In the coarse matching stage, feature points are first detected on a maximum moment map calculated with a phase congruency model. Then, feature description is conducted using an index map constructed by finding the index of the maximum value in all orientations of convolved images obtained using a set of log-Gabor filters. At last, several matches are built through image matching and outlier removal, which can be used to estimate a reliable affine transformation model between the images. In the stage of fine matching, we develop a novel template matching method based on the log-Gabor convolution image sequence and match the template features with a 3D phase correlation matching strategy, given that the initial correspondences are achieved with the estimated transformation. Results show that compared with SIFT, and three state-of-the-art methods designed for multimodal image matching, PSO-SIFT, HAPCG, and RIFT, only 3MRS successfully matched all six types of multimodal remote sensing image pairs: optical–optical, optical–infrared, optical–depth, optical–map, optical–SAR, and day–night, with each including ten different image pairs. On average, the number of correct matches (NCM) of 3MRS was 164.47, 123.91, 4.88, and 4.33 times that of SIFT, PSO-SIFT, HAPCG, and RIFT for the successfully matched image pairs of each method. In terms of accuracy, the root-mean-square error of correct matches for 3MRS, SIFT, PSO-SIFT, HAPCG, and RIFT are 1.47, 1.98, 1.79, 2.83, and 2.45 pixels, respectively, revealing that 3MRS got the highest accuracy. Even though the total running time of 3MRS was the longest, the efficiency for obtaining one correct match is the highest considering the most significant number of matches. The source code of 3MRS and the experimental datasets and detailed results are publicly available.

Multi-Angle Optical Image Automatic Registration by Combining Point and Line Features.

Image registration is an important basis of image processing, which is of great significance in image mosaicking, target recognition, and change detection. Aiming at the automatic registration problem of multi-angle optical images for ground scenes, a registration method combining point features and line features to register images is proposed. Firstly, the LSD (Line Segment Detector) algorithm is used to extract line features of images. The obtained line segments whose length are less than a given threshold are eliminated by a visual significant algorithm. Then, an affine transform model obtained by estimating a Gaussian mixture model (GMM) is applied to the image to be matched. Lastly, Harris point features are utilized in fine matching to overcome shortages of methods based on line features. In experiments, the proposed algorithm is compared with popular feature-based registration algorithms. The results indicate that the proposed algorithm in this work has obvious advantages in terms of registration accuracy and reliability for optical images acquired at different angles.

Adversarial Cross-domain Community Question Retrieval

Community Q&A forum is a special type of social media that provides a platform to raise questions and to answer them (both by forum participants), to facilitate online information sharing. Currently, community Q&A forums in professional domains have attracted a large number of users by offering professional knowledge. To support information access and save users’ efforts of raising new questions, they usually come with a question retrieval function, which retrieves similar existing questions (and their answers) to a user’s query. However, it can be difficult for community Q&A forums to cover all domains, especially those emerging lately with little labeled data but great discrepancy from existing domains. We refer to this scenario as cross-domain question retrieval. To handle the unique challenges of cross-domain question retrieval, we design a model based on adversarial training, namely, X-QR , which consists of two modules—a domain discriminator and a sentence matcher. The domain discriminator aims at aligning the source and target data distributions and unifying the feature space by domain-adversarial training. With the assistance of the domain discriminator, the sentence matcher is able to learn domain-consistent knowledge for the final matching prediction. To the best of our knowledge, this work is among the first to investigate the domain adaption problem of sentence matching for community Q&A forums question retrieval. The experiment results suggest that the proposed X-QR model offers better performance than conventional sentence matching methods in accomplishing cross-domain community Q&A tasks.

Biometric Recognition of Finger Knuckle Print Based on the Fusion of Global Features and Local Features.

Compared with the most traditional fingerprint identification, knuckle print and hand shape are more stable, not easy to abrase, forge, and pilfer; in aspect of image acquisition, the requirement of acquisition equipment and environment are not high; and the noncontact acquisition method also greatly improves the users' satisfaction; therefore, finger knuckle print and hand shape of single-mode identification system have attracted extensive attention both at home and abroad. A large number of studies show that multibiometric fusion can greatly improve the recognition rate, antiattack, and robustness of the biometric recognition system. A method combining global features and local features was designed for the recognition of finger knuckle print images. On the one hand, principal component analysis (PCA) was used as the global feature for rapid recognition. On the other hand, the local binary pattern (LBP) operator was taken as the local feature in order to extract the texture features that can reflect details. A two-layer serial fusion strategy is proposed in the combination of global and local features. Firstly, the sample library scope was narrowed according to the global matching result. Secondly, the matching result was further determined by fine matching. By combining the fast speed of global coarse matching and the high accuracy of local refined matching, the designed method can improve the recognition rate and the recognition speed.

A Self-paced Learning based Transfer Model for Hypergraph Matching

Determination of correspondences between vertexes of two graphs is one of essential tasks in the computer vision fields. Despite the graph matching problem is NP-hard, hypergraph matching is well used in many matching methods from the perspective of higher order geometric information. However, it is still a challenge to learn graph models from observed samples of graph matching. In this paper, we present an effective scheme to parameterize a graph model through self-paced learning algorithm. Consequently, each iteration heuristically selects smaller loss samples in a data-driven manner, the learning and matching problems are aligned to learn a new transfer hypergraph model for constructing its high-order structural attributes for visual object matching. For the final matching task between two graphs, we develop the transfer matching method through the assignment matrix decomposition to achieve it. Several experiments on Willow-Object datasets and some other datasets indicate the good performance of our method.

Locality-Guided Global-Preserving Optimization for Robust Feature Matching.

Feature matching is a fundamental problem in many computer vision tasks. This paper proposes a novel effective framework for mismatch removal, named LOcality-guided Global-preserving Optimization (LOGO). To identify inliers from a putative matching set generated by feature descriptor similarity, we introduce a fixed-point progressive approach to optimize a graph-based objective, which represents a two-class assignment problem regarding an affinity matrix containing global structures. We introduce a strategy that a small initial set with a high inlier ratio exploits the topology of the affinity matrix to elicit other inliers based on their reliable geometry, which enhances the robustness to outliers. Geometrically, we provide a locality-guided matching strategy, i.e., using local topology consensus as a criterion to determine the initial set, thus expanding to yield the final feature matching set. In addition, we apply local affine transformations based on reference points to determine the local consensus and similarity scores of nodes and edges, ensuring the validity and generality for various scenarios including complex nonrigid transformations. Extensive experiments demonstrate the effectiveness and robustness of the proposed LOGO, which is competitive with the current state-of-the-art methods. It also exhibits favorable potential for high-level vision tasks, such as essential and fundamental matrix estimation, image registration and loop closure detection.

Intelligent Matching Method for Heterogeneous Remote Sensing Images Based on Style Transfer

Intelligent matching of heterogeneous remote sensing images is a common basic problem in the field of intelligent remote sensing image processing. Aiming at the difficulty of matching satellite-aerial remote sensing images, this paper proposes an intelligent matching method for heterogeneous remote sensing images based on style transfer. First, based on the idea of image style transfer of a generative adversarial networks, this method improves the conversion effect of the model on heterogeneous images by constructing a new generative network loss function and converts satellite images into aerial images. Then, the advanced deep learning-based matching Algorithms D2-Net and LoFTR are used to achieve matching between the generated aerial image and the original aerial image. Finally, this transformation relationship is mapped to the corresponding satellite-aerial image pair to obtain the final matching result. The image style transfer experiments and the matching experiments we carry out under different test datasets show that the smooth cycle-consistent generative adversarial networks proposed in this paper can effectively reduce the complexity of the algorithm and improve the quality of image generation. In addition, combining it with deep learning-based feature matching methods can effectively improve the accuracy and robustness of the matching algorithm. Our code and data can be found at: <uri>https://gitee.com/AZQZ/intelligent-matching</uri>.

Robust sclera recognition based on a local spherical structure

The sclera is the opaque white tissue that covers the sphere of the human eye, and this causes a complex nonlinear deformation of the vessel pattern in the sclera as the eyes move. Over the last decade, although the blood vessels of the sclera have been investigated for use as a biometric modality, very little research has been undertaken on feature extraction and matching based on this anatomical structure. To address this problem, we propose a new local spherical structure (LSS) that incorporates a vessel shape feature (VSF) to represent the geometry and texture feature of the vessel pattern. The key ideas in this work are the designs of the VSF and the LSS, which are based on the fact that the vessels lie below a sphere-shaped sclera. To create the proposed VSF, a Harris corner detector is used to extract interest points, each of which is encoded into a normalized polar histogram that represents the shape of a vessel. These points are then projected onto the surface of a sphere, and we design an LSS to represent the topological relationships between each of the points, derived from the arc distance. In the matching step, we adopt a coarse-to-fine procedure that enables a fast similarity comparison between two point pairs using the VSF and the LSS. The final matching score is obtained by combining the number of matched pairs, the total number of interest points, and the similarity. Various experiments were conducted on the UBIRIS.v1 and UTIRIS databases, the latter of which was more challenging. The proposed method showed promising performance, as it yielded an error rate equal to those of other feature extraction and matching methods. Our findings suggest that the proposed approach not only outperforms the state-of-the-art methods of sclera recognition but also illustrates how to address the issue of nonlinear deformation of the vessel pattern based on the anatomical structure of the human eye.