Feature Matching Method Research Articles

Fake Currency Detection Using ORB Algorithm

Abstract: This paper presents the technique to detect fake currency using the technique of image processing. Fake currency is produced and circulated in the market by antisocial people or group of people in the society. Fake currency affects the economic status of the country and also brings disturbance in the economic development. Various methods of image processing techniques that can be used to process and detect fake currency are- image segmentation, image acquisition, edge detection, grey scale conversion, feature extraction and feature matching. The system presented in the paper deals with feature extraction and feature matching method of image processing in python language. Result will be displayed in terms of accuracy value depicting whether the currency note is original or fake.

Automatic Anomaly Monitoring Research of Business English Literature Translation Based on Decision Tree Intelligent Analysis

As an important language tool, literature in business English defines rights and obligations in business activities from the perspective of literature translation. This article discusses business English from the perspective of literature translation, which should not only preserve the characteristics of literature, but also ensure the smooth and correct language. In order to improve the accuracy of the automatic translation of business English literature and optimize the design of the teaching platform for business English literature translation, a design method of the teaching platform for business English literature translation based on the decision tree logistic model is proposed. The platform design consists of two modules: automatic translation algorithm design and software development of the platform. Using the decision tree logistics model to analyze the semantic features of business English translation and context feature matching and adaptive semantic variable optimization method to analyze automation lexical features of business English translation and to extract the correlation between vocabulary and vocabulary characteristics, in the context of a specific business translation difference correction, the accuracy of English translation is improved. The software design of the platform is carried out under the decision tree logistics model. The platform construction is mainly divided into vocabulary database module, English information processing module, network interface module, and human-computer interaction interface module. B/S framework protocol is used for integrated development and the design of translation platform. According to the characteristics of the data business application and the particularity of data security risk monitoring, from business English requirement analysis, the study of business English translation behavior monitoring ability and analysis in the process of abnormal behavior monitoring techniques and methods, including data access, data processing, experience in engine, and model engine puts forward the future research direction. The platform test results show that this method has good accuracy and strong automatic translation ability in business English literature translation.

Multi-temporal orthophoto and digital surface model registration produced from UAV imagery over an agricultural field

Correcting the three-dimensional geometric error is essential to effectively use the multi-temporal unmanned aerial vehicle (UAV) orthophoto and digital surface model (DSM) acquired from the agricultural field. Although ground control points (GCPs) obtained through field surveys are usually used to calibrate geometrical errors establishing/maintaining GCPs and surveying them in the field are time-consuming and inefficient. Therefore, we propose a simple and efficient methodology to improve the geometric registration of multi-temporal orthophotos and DSMs without GCPs. In the proposed method, coarse to fine image registration is performed first, which corrects severe to slight errors by sequential feature and area-based matching methods. Subsequently, we extract height-invariant regions in multi-temporal DSM pairs, called elevation invariant feature (EIF), using the EIFs to register DSMs by estimating a linear regression model. Various experiments were conducted to analyze the absolute and relative accuracies using ten multi-temporal orthophotos and DSMs, and the robustness of the proposed method was evaluated using data obtained from another site. The experimental results demonstrate that the geometric quality of registered orthophotos and DSMs was significantly improved.

Reflective texture-less object registration using multiple edge features for augmented reality assembly

Augmented reality assembly technology has developed rapidly in recent years. As one of the key technologies in augmented reality assembly, object registration is to match the target’s location and posture between virtual objects and entities. However, the existing methods cannot effectively register reflective texture-less objects such as metal parts. In this paper, an object registration method based on multiple edge features is proposed, which includes a sparse uniform template sampling method and a multiple edge feature matching method. At the same time, we also used two oil pumps to carry out augmented reality assembly experiments and verify the accuracy of object registration. The experimental results show that this method can achieve high accuracy which is in line with the requirements of the augmented reality assembly in the industry.

Comparing Feature Matching Methods to Identify Persian Writers

Comparing Feature Matching Methods to Identify Persian Writers

LiDAR Odometry and Mapping Based on Neighborhood Information Constraints for Rugged Terrain

The simultaneous localization and mapping (SLAM) method estimates vehicles’ pose and builds maps established on the collection of environmental information primarily through sensors such as LiDAR and cameras. Compared to the camera-based SLAM, the LiDAR-based SLAM is more geared to complicated environments and is not susceptible to weather and illumination, which has increasingly become a hot topic in autonomous driving. However, there has been relatively little research on the LiDAR-based SLAM algorithm in rugged scenes. The following two issues remain unsolved: on the one hand, the small overlap area of two adjacent point clouds results in insufficient valuable features that can be extracted; on the other hand, the conventional feature matching method does not take point cloud pitching into account, which frequently results in matching failure. Hence, a LiDAR SLAM algorithm based on neighborhood information constraints (LoNiC) for rugged terrain is proposed in this study. Firstly, we obtain the feature points with surface information using the distribution of the normal vector angles in the neighborhood and extract features with discrimination through the local surface information of the point cloud, to improve the describing ability of feature points in rugged scenes. Secondly, we provide a multi-scale constraint description based on point cloud curvature, normal vector angle, and Euclidean distance to enhance the algorithm’s discrimination of the differences between feature points and prevent mis-registration. Subsequently, in order to lessen the impact of the initial pose value on the precision of point cloud registration, we introduce the dynamic iteration factor to the registration process and modify the corresponding relationship of the matching point pairs by adjusting the distance and angle thresholds. Finally, the verification based on the KITTI and JLU campus datasets verifies that the proposed algorithm significantly improves the accuracy of mapping. Specifically in rugged scenes, the mean relative translation error is 0.0173%, and the mean relative rotation error is 2.8744°/m, reaching the current level of the state of the art (SOTA) method.

Indoor–Outdoor Point Cloud Alignment Using Semantic–Geometric Descriptor

Aligning indoor and outdoor point clouds is a challenging problem since the overlapping area is usually limited, thus resulting in a lack of correspondence features. The windows and doors can be observed from both sides and are usually utilized as shared features to make connections between indoor and outdoor models. However, the registration performance using the geometric features of windows and doors is limited due to the considerable number of extracted features and the mismatch of similar features. This paper proposed an indoor/outdoor alignment framework with a semantic feature matching method to solve the problem. After identifying the 3D window and door instances from the point clouds, a novel semantic–geometric descriptor (SGD) is proposed to describe the semantic information and the spatial distribution pattern of the instances. The best object match is identified with an improved Hungarian algorithm using indoor and outdoor SGDs. The matching method is effective even when the numbers of objects are not equal in the indoor and outdoor models, which is robust to measurement occlusions and feature outliers. The experimental results conducted in the collected dataset and the public dataset demonstrated that the proposed method could identify accurate object matches under complicated conditions, and the alignment accuracy reached the centimeter level.

Artificial intelligence scale-invariant feature transform algorithm-based system to improve the calculation accuracy of Ki-67 index in invasive breast cancer: a multicenter retrospective study.

Ki-67 is a key indicator of the proliferation activity of tumors. However, no standardized criterion has been established for Ki-67 index calculation. Scale-invariant feature transform (SIFT) algorithm can identify the robust invariant features to rotation, translation, scaling and linear intensity changes for matching and registration in computer vision. Thus, this study aimed to develop a SIFT-based computer-aided system for Ki-67 calculation in breast cancer. Hematoxylin and eosin (HE)-stained and Ki-67-stained slides were scanned and whole slide images (WSIs) were obtained. The regions of breast cancer (BC) tissues and non-BC tissues were labeled by experienced pathologists. All the labeled WSIs were randomly divided into the training set, verification set, and test set according to a fixed ratio of 7:2:1. The algorithm for identification of cancerous regions was developed by a ResNet network. The registration process between paired consecutive HE-stained WSIs and Ki-67-stained WSIs was based on a pyramid model using the feature matching method of SIFT. After registration, we counted the nuclear-stained Ki-67-positive cells in each identified invasive cancerous region using color deconvolution. To assess the accuracy, the AI-assisted result for each slice was compared with the manual diagnosis result of pathologists. If the difference of the two positive rate values is not greater than 10%, it was a consistent result; otherwise, it was an inconsistent result. The accuracy of the AI-based algorithm in identifying breast cancer tissues in HE-stained slides was 93%, with an area under the curve (AUC) of 0.98. After registration, we succeeded in identifying Ki-67-positive cells among cancerous cells across the entire WSIs and calculated the Ki-67 index, with an accuracy rate of 91.5%, compared to the gold standard pathological reports. Using this system, it took about 1 hour to complete the evaluation of all the tested 771 pairs of HE- and Ki-67-stained slides. Each Ki-67 result took less than 2 seconds. Using a pyramid model and the SIFT feature matching method, we developed an AI-based automatic cancer identification and Ki-67 index calculation system, which could improve the accuracy of Ki-67 index calculation and make the data repeatable among different hospitals and centers.

A Coupled Visual and Inertial Measurement Units Method for Locating and Mapping in Coal Mine Tunnel.

Mobile robots moving fast or in scenes with poor lighting conditions often cause the loss of visual feature tracking. In coal mine tunnels, the ground is often bumpy and the lighting is uneven. During the movement of the mobile robot in this scene, there will be violent bumps. The localization technology through visual features is greatly affected by the illumination and the speed of the camera movement. To solve the localization and mapping problem in an environment similar to underground coal mine tunnels, we improve a localization and mapping algorithm based on a monocular camera and an Inertial Measurement Unit (IMU). A feature-matching method that combines point and line features is designed to improve the robustness of the algorithm in the presence of degraded scene structure and insufficient illumination. The tightly coupled method is used to establish visual feature constraints and IMU pre-integration constraints. A keyframe nonlinear optimization algorithm based on sliding windows is used to accomplish state estimation. Extensive simulations and practical environment verification show that the improved simultaneous localization and mapping (SLAM) system with a monocular camera and IMU fusion can achieve accurate autonomous localization and map construction in scenes with insufficient light such as coal mine tunnels.

RI-LPOH: Rotation-Invariant Local Phase Orientation Histogram for Multi-Modal Image Matching

To better cope with the significant nonlinear radiation distortions (NRD) and severe rotational distortions in multi-modal remote sensing image matching, this paper introduces a rotationally robust feature-matching method based on the maximum index map (MIM) and 2D matrix, which is called the rotation-invariant local phase orientation histogram (RI-LPOH). First, feature detection is performed based on the weighted moment equation. Then, a 2D feature matrix based on MIM and a modified gradient location orientation histogram (GLOH) is constructed and rotational invariance is achieved by cyclic shifting in both the column and row directions without estimating the principal orientation separately. Each part of the sensed image’s 2D feature matrix is additionally flipped up and down to obtain another 2D matrix to avoid intensity inversion, and all the 2D matrices are concatenated by rows to form the final 1D feature vector. Finally, the RFM-LC algorithm is introduced to screen the obtained initial matches to reduce the negative effect caused by the high proportion of outliers. On this basis, the remaining outliers are removed by the fast sample consensus (FSC) method to obtain optimal transformation parameters. We validate the RI-LPOH method on six different types of multi-modal image datasets and compare it with four state-of-the-art methods: PSO-SIFT, MS-HLMO, CoFSM, and RI-ALGH. The experimental results show that our proposed method has obvious advantages in the success rate (SR) and the number of correct matches (NCM). Compared with PSO-SIFT, MS-HLMO, CoFSM, and RI-ALGH, the mean SR of RI-LPOH is 170.3%, 279.8%, 81.6%, and 25.4% higher, respectively, and the mean NCM is 13.27, 20.14, 1.39, and 2.42 times that of the aforementioned four methods.

Dual-View Stereovision-Guided Automatic Inspection System for Overhead Transmission Line Corridor

Overhead transmission line corridor detection is important to ensure the safety of power facilities. Frequent and uncertain changes in the transmission line corridor environment requires an efficient and autonomous UAV inspection system, whereas the existing UAV-based inspection systems has some shortcomings in control model and ground clearance measurement. For one thing, the existing manual control model has the risk of striking power lines because it is difficult for manipulators to judge the distance between the UAV fuselage and power lines accurately. For another, the ground clearance methods based on UAV usually depend on LiDAR (Light Detection and Ranging) or single-view visual repeat scanning, with which it is difficult to balance efficiency and accuracy. Aiming at addressing the challenging issues above, a novel UAV inspection system is developed, which can sense 3D information of transmission line corridor by the cooperation of the dual-view stereovision module and an advanced embedded NVIDIA platform. In addition, a series of advanced algorithms are embedded in the system to realize autonomous control of UAVs and ground clearance measurement. Firstly, an edge-assisted power line detection method is proposed to locate the power line accurately. Then, 3D reconstruction of the power line is achieved based on binocular vision, and the target flight points are generated in the world coordinate system one-by-one to guide the UAVs movement along power lines autonomously. In order to correctly detect whether the ground clearances are in the range of safety, we propose an aerial image classification based on a light-weight semantic segmentation network to provide auxiliary information categories of ground objects. Then, the 3D points of ground objects are reconstructed according to the matching points set obtained by an efficient feature matching method, and concatenated with 3D points of power lines. Finally, the ground clearance can be measured and detected according to the generated 3D points of the transmission line corridor. Tests on both corresponding datasets and practical 220-kV transmission line corridors are conducted. The experimental results of different modules reveal that our proposed system can be applied in practical inspection environments and has good performance.

Hyperspectral Panoramic Image Stitching Using Robust Matching and Adaptive Bundle Adjustment

Remote-sensing developments such as UAVs heighten the need for hyperspectral image stitching techniques that can obtain information on a large area through various parts of the same scene. State-of-the-art approaches often suffer from accumulation errors and high computational costs for large-scale hyperspectral remote-sensing images. In this study, we aim to generate high-precision hyperspectral panoramas with less spatial and spectral distortion. We introduce a new stitching strategy and apply it to hyperspectral images. The stitching framework was built as follows: First, a single band obtained by signal-to-noise ratio estimation was chosen as the reference band. Then, a feature-matching method combining the SuperPoint and LAF algorithms was adopted to strengthen the reliability of feature correspondences. Adaptive bundle adjustment was also designed to eliminate misaligned artifact areas and occasional accumulation errors. Lastly, a spectral correction method using covariance correspondences is proposed to ensure spectral consistency. Extensive feature-matching and image-stitching experiments on several hyperspectral datasets demonstrate the superiority of our approach over the state of the art.

Robust template feature matching method using motion-constrained DCF designed for visual navigation in asteroid landing

Robust template feature matching method using motion-constrained DCF designed for visual navigation in asteroid landing

Improved GAN with fact forcing for mobility prediction

Mobility prediction based on users movement history is a prerequisite for proactive mobility and several other management operations in wireless networks. Recent Deep Learning (DL) studies for mobility prediction have shown improved accuracies using variety of different models that have high complexity and massive training data requirements. These limitations restrict the viability of mobility prediction driven proactive management of wireless networks. This paper overcomes the mentioned limitations by proposing Improved Generative Adversarial Network with Fact Forcing (iGAN-FF) that enhances the existing improved GAN (iGAN). iGAN-FF architecture consists of generator and discriminator neural networks, where generator predicts mobility (next Point of Attachment (PoA)) and discriminator classifies between the predicted PoA and the ground truth in adversarial learning. The adversarial learning reduces the needed amount of training data and enables use of simplistic models, and novel compounded fact forcing and feature matching method achieves higher accuracy. Moreover, the effects of data representation on performance are evaluated by transforming the Campus Mobility Dataset (CMD) and Operator Mobility Dataset (OMD) to One-hot, Binary, and Embedded vectors representations. The results confirm the merits of iGAN-FF as it outperforms iGAN, GAN-FF, GAN, LSTM based Next PoA (LNP), and Transformer based Next PoA (TNP). In particular, the highest accuracies of 99.57% and 83.3% are achieved with CMD One-hot and OMD Embedded representations, respectively. Notably, iGAN-FF shows robustness under data scarcity and secures ∼98% accuracy with only 20% of CMD data.

Automatic compact camera module solder joint inspection method based on machine vision

Because of the contradiction between the production requirements of compact camera modules (CCMs) to achieve high efficiency and quality and the low efficiency and poor accuracy of traditional solder joint inspection methods, an automatic inspection method of CCM solder joint based on machine vision is proposed. After optimizing the imaging parameters according to the CCM inspection process, the region of interest is dynamically identified based on feature matching and image enhancement methods to remove background interference. On this basis, an improved adaptive particle swarm optimization is used to optimize the kernel extreme learning machine to automatically classify the solder joint defects. Experimental results showed that with its low latency, high precision and robustness, the CCM surface solder joint defect detection and classification method based on machine vision can effectively solve the problem of low efficiency and high cost of the current CCM solder joint defect detection technology.

Color Image Feature Matching Method Based on the Improved Firework Algorithm

An improved ORB feature point purification method has been proposed to address the problem of large feature point matching error and low image registration rate in the oriented FAST and rotated BRIEF (ORB) feature point color image matching algorithm. Pure virtual quaternions were used to represent the color image pixels in this method, and an improved FAST algorithm was used to detect the color feature points at first. The firework algorithm has been used to divide the detected feature points into key areas, auxiliary areas, and small influence areas, depending on the degree of attachment. The feature points of the key area and the subsidiary area are the required feature points. In order to further improve the efficiency of matching the color image feature points, the firework explosion radius formula and the explosion number formula in the firework algorithm have been improved, and an improved firework algorithm is proposed. This improved algorithm purifies the quaternion-represented color image feature points. For feature matching, the hamming distance was used. Experiments show that, when compared to the traditional ORB algorithm, the improved algorithm retains the key feature points of the color image with high accuracy, removes a large number of irrelevant feature points and noise points, and provides significantly higher accuracy and efficiency of color image matching.

Analysis of Different Image Enhancement and Feature Extraction Methods

This paper describes an image enhancement method for reliable image feature matching. Image features such as SIFT and SURF have been widely used in various computer vision tasks such as image registration and object recognition. However, the reliable extraction of such features is difficult in poorly illuminated scenes. One promising approach is to apply an image enhancement method before feature extraction, which preserves the original characteristics of the scene. We thus propose to use the Multi-Scale Retinex algorithm, which is aimed to emulate the human visual system and it provides more information of a poorly illuminated scene. We experimentally assessed various combinations of image enhancement (MSR, Gamma correction, Histogram Equalization and Sharpening) and feature extraction methods (SIFT, SURF, ORB, AKAZE) using images of a large variety of scenes, demonstrating that the combination of the Multi-Scale Retinex and SIFT provides the best results in terms of the number of reliable feature matches.

Leveraging Local Planar Motion Property for Robust Visual Matching and Localization

One primary difficulty preventing the visual localization for service robots is the robustness against changes, including environmental changes and perspective changes. In recent years, learning-based feature matching methods have been widely studied and effectively verified in practical applications. Learning-based feature matching effectively solves the problem of environmental changes, including illumination changes and man-made changes. However, there is still room for improvement dealing with large perspective changes. In this letter, we leverage local planar motion property to simplify the affine transform and propose an augmentation-based feature matching method that greatly enhances the robustness to perspective changes. The proposed feature matching approach maintains low matching costs as the augmentation is performed on the simplified affine matrix space. Combined with the motion property aided minimal solution for pose estimation, an end-to-end robust visual localization system is proposed which is shown to bring 67% improvement in localization performance under large perspective changes in publicly available OpenLORIS dataset, while increasing computational cost by only 20% by using batch processing techniques with a single GPU. In addition, a guide for map frame selection is presented to support robust localization with very sparse map frames in storage. Experiments on the classified dataset with environmental changes and perspective changes validate the effectiveness of the proposed system.

IIPA-Net: Joint Illumination-Invariant and Pose-Aligned Feature Learning for Person Reidentification

Person reidentification (re-id) has gained significant progress and aroused great interest in computer vision. However, due to the effect of weak illumination and poor alignment, person re-id is still a challenging task. Many previous works focus on either illumination enhancement methods or pose estimation. However, those methods are difficult to apply in real-world scenarios, which usually contain various interference factors. To improve the performance of re-id, we propose an Illumination-Invariant and Pose-Aligned Network (IIPA-Net). The illumination change is handled by a retinex decompose network, and the pose variation problem is solved by a local feature matching method. Based on the multimodal nature of a person, we propose a part attention module to optimize the global feature. Finally, a data-driven training strategy is proposed to train the proposed architecture effectively. Experiments show that the proposed framework outperforms other state-of-the-art approaches on both normal- and low-light datasets.

Application of Visual Communication Combined with Electrochemistry in Ceramic Carving Product Design

In order to improve the design of ceramic carving products, the author proposes a visual communication, combined with electrochemistry, laser engraving technology, using superpixel imaging technology, carry out laser engraving ceramic reticulation imaging processing, build a binary laser engraving image model, using image edge feature matching method, carry out the edge contour feature detection of the laser engraved ceramic reticulated image, and extract the local information feature quantity of the laser engraved ceramic mesh image. The fuzzy adaptive segmentation method is used for the extracted features and to perform grid area segmentation. The experimental results show that the greater the laser output power, the deeper the engraving depth. As the laser output power increases, the depth of the hole formed by a single pulse laser is enlarged, the etched area formed by the dense hole group also deepens. However, the experiment found that after the laser power is increased to a certain value (17 A), the temperature of the light spot rises sharply, reach the melting point of ceramics, and produce slag that is not easy to remove on the engraved surface, decrease the effective engraving depth. It proves that visual communication, combined with electrochemistry, can effectively realize the design of ceramic carving products.