Feature Matching Research Articles

Detection of counterfeit images using SIFT and PNN

Manipulation of digital images presents a significant challenge, primarily due to the continuous advancement and sophistication of image enhancement tools. As a consequence, image feature extraction and matching have become pivotal areas of research in the field of image processing. Among the various techniques developed, the Scale-Invariant Feature Transform algorithm has gained widespread recognition for its robustness and superior performance in feature matching tasks when compared to other existing methods. In this context, our proposed method, which integrates the Scale-Invariant Feature Transform algorithm with a probabilistic neural network, offers a novel approach to digital image classification specifically aimed at counterfeit image detection. This integrated system not only enhances the accuracy of feature extraction but also leverages the probabilistic neural network to improve classification efficacy. Empirical results indicate that this hybrid approach achieves a remarkable relative error rate of just 0.666%, underscoring its potential as a reliable tool for combating digital image forgery. Such advancements are crucial for maintaining the integrity of digital media in various applications, from forensic analysis to digital content authentication.

Facile synthesis of hierarchical Fe–S co-doped carbon-based composites with superior microwave absorption

Difficulty of multi-component coupling and hierarchical pore structure preparation make it challenging to manufacture highly efficient microwave absorption materials (MAMs). Herein, hierarchical porous carbon composites anchored with various types of iron sulfides are successfully fabricated using a simply feasible heating-induced self-assembly approach. Controllable electromagnetic parameters and impedance matching features of FexSy/AC (amorphous carbon) composites are obtained by varying carbonization temperature. Besides, AC and FexSy provide dielectric and magnetic losses, while defect-rich AC can effectively disperse FexSy nanoparticles, exhibiting high sintering resistance and high-density interfaces, which contributes to polarization losses. Moreover, the unique hierarchical porous structure not only offers better impedance matching, but also enhances microwave loss. The thorough investigation indicates that FexSy/AC nanocomposites exhibit outstanding microwave absorption performance by the joint effect of unique hierarchical porous structure and coupling loss of multi-components. The optimal sample (FSC-700) shows a strongest reflection loss value of up to −78.96 dB with a matching thickness of 2.85 mm, an effective absorption bandwidth (EAB, RL<−10 dB) of 6.98 GHz (11.02–18.0 GHz, spanning Ku-band), and a radar cross section (RCS) reduction value of 21.75 dBm2. In addition, the EAB can reach 13.8 GHz with the simulated thickness varies from 1.0 to 5.5 mm, covering 86 % of the measured frequency range. The in-depth research of the multi-component system with hierarchical porous structure offers an innovative and practical method for high-performance MAMs.

Vision-aided inertial navigation for planetary landing without feature extraction and matching

In this paper, a vision-aided inertial navigation method for planetary landing based on the direct method is proposed. This approach requires only a monocular camera and an inertial measurement unit (IMU) to be equipped on the lander. Unlike traditional feature-based visual-aided inertial navigation algorithms, which entail extracting and matching image features and thus incurring high computational costs, the proposed method utilizes the direct method that tracks any pixel with a sufficient intensity gradient. By jointly optimizing photometric and inertial errors, the proposed algorithm estimates the camera motion and sparse map of the scene. Simulation results demonstrate that the proposed method reduces computation by nearly 65 % compared to the feature point method, with a slight decrease in navigation accuracy.

Improved mirror-assisted multi-view digital image correlation for accuracy-enhanced dual-surface profile and deformation measurement

Accurate measurement of dual-surface kinematic fields is essential for characterizing the mechanical behavior and anisotropic parameters of sheet specimens. To this end, a cost-efficient and simple-to-implement mirror-assisted multi-view digital image correlation (MV-DIC) was recently proposed and has been proven to be effective. Nevertheless, this method conventionally relies on reflection transformation calibration to map the measured virtual surfaces to their actual positions. The positional transformation for each surface is mutually independent and lacks constraint relationships. This work proposes an improved mirror-assisted MV-DIC method for accuracy-enhanced dual-surface deformation measurement by utilizing the overlapped region of the virtual surfaces instead of reflection transformation calibration. Through preliminary feature matching and precise matching of subsets for corresponding points in the overlapped regions, the relative positional transformations between the measured surfaces of the specimen can be accurately determined. As the accuracy of positional transformations primarily depends on the matching accuracy of corresponding points rather than the accuracy of multiple plane fitting, this method provides better relative positional relationships between the two surfaces compared to conventional methods. The results of several real experiments well validated the practicality and accuracy of the proposed method.

Data-driven hierarchical learning approach for multi-point servo control of Pan–Tilt–Zoom cameras

Pan–Tilt–Zoom (PTZ) cameras, with their significant features of free rotation and zoom, are widely used in areas such as border security, ecological conservation, emergency management, and the military. PTZ cameras can achieve automatic monitoring of a selected area through multiple servo operations, known as multi-point servo control. However, due to the deficiencies in the servo control Software Development Kit (SDK), hardware wear, and interference from complex external environments, the multi-point servo control process generates significant errors. This paper proposes a precise multi-point servo control framework based on Deep Reinforcement Learning (DRL) to address this issue. The complexity of real-world environments necessitates a reward function design that fully considers various factors, for which we propose the directional gravity reward function. Due to the instability during the training process, prolonged trial-and-error interactions between the agent and the equipment can cause irreversible damage to the devices. This framework employs a phased training approach, where the agent learns sequentially from offline, off-policy, and on-policy data, reducing direct interaction with the equipment while enhancing the agent’s overall performance. Additionally, real-time and accurate device status can be obtained by performing feature matching on images from adjacent time frames, which is crucial for the system’s operation. Evaluation results indicate that our proposed precise multi-point servo control framework significantly outperforms other methods in 4-point servo control tasks in both virtual and real-world scenarios. Additionally, the operational process fully considers safety issues.

Underwater Mapping and Optimization Based on Multibeam Echo Sounders

Multibeam echo sounders (MBESs) enable extensive underwater environment exploration. However, due to weak correlation between adjacent multibeam sonar data and difficulties in inter-frame feature matching, the resulting underwater mapping accuracy frequently falls short of the desired level. To address this issue, this study presents the development of a multibeam data processing system, which includes functionalities for sonar parameter configuration, data storage, and point cloud conversion. Subsequently, an Iterative Extended Kalman Filter (iEKF) algorithm is employed for odometry estimation, facilitating the initial construction of the point cloud map. To further enhance mapping accuracy, we utilize the Generalized Iterative Closest Point (GICP) algorithm for point cloud registration, effectively merging point cloud data collected at different times from the same location. Finally, real-world lake experiments demonstrate that our method achieves an Absolute Trajectory Error (ATE) of 15.10 m and an average local point cloud registration error of 0.97 m. Furthermore, we conduct measurements on various types of artificial targets. The experimental results indicate that the average location error of the targets calculated by our method is 4.62 m, which meets the accuracy requirements for underwater target exploration.

Close-range imaging for green roofs: Feature detection, band matching, and image registration for mixed plant communities

Green roofs offer ecological benefits but harsh rooftop environment stressors like heat, water scarcity, and wind can limit optimal plant growth. Monitoring plant health is crucial for optimizing green roof performance and remote sensing provides a non-destructive approach, yet challenges persist in tight, urban settings. This allows for close-range ground monitoring as a viable solution in these emerging environments. Recent studies explored automatic image registration techniques, showing promise in homogeneous settings, but faced uncertainties in mixed species systems or early plant growth stages. Feature detection techniques such as Speeded up Robust Features (SURF) using fixed and moving image spaces have been proposed for alignment. This study aimed to assess feature detection, matching, and image registration techniques in mixed species plant communities on green roofs throughout the growing season. This study developed a sophisticated monitoring system using close-range multispectral sensors for aligning bands in mixed species plant communities, contributing to enhanced green roof management and sustainability. The results of the study showed no significant differences in band alignment accuracy between growth stages for mixed species bush bean/sedum and single species sedum modules, but significant differences were found in single species bush bean systems. Additionally, mixed species modules showed better accuracy compared to single species bush bean modules, indicating that heterogenous systems provide better alignment accuracy due to more diverse feature extractions.

A Robust AR-DSNet Tracking Registration Method in Complex Scenarios

A robust AR-DSNet (Augmented Reality method based on DSST and SiamFC networks) tracking registration method in complex scenarios is proposed to improve the ability of AR (Augmented Reality) tracking registration to distinguish target foreground and semantic interference background, and to address the issue of registration failure caused by similar target drift when obtaining scale information based on predicted target positions. Firstly, the pre-trained network in SiamFC (Siamese Fully-Convolutional) is utilized to obtain the response map of a larger search area and set a threshold to filter out the initial possible positions of the target; Then, combining the advantage of the DSST (Discriminative Scale Space Tracking) filter tracker to update the template online, a new scale filter is trained after collecting multi-scale images at the initial possible position of target to reason the target scale change. And linear interpolation is used to update the correlation coefficient to determine the final position of target tracking based on the difference between two frames. Finally, ORB (Oriented FAST and Rotated BRIEF) feature detection and matching are performed on the accurate target position image, and the registration matrix is calculated through matching relationships to overlay the virtual model onto the real scene, achieving enhancement of the real world. Simulation experiments show that in complex scenarios such as similar interference, target occlusion, and local deformation, the proposed AR-DSNet method can complete the registration of the target in AR 3D tracking, ensuring real-time performance while improving the robustness of the AR tracking registration algorithm.

Physics-driven feature alignment combined with dynamic distribution adaptation for three-cylinder drilling pump cross-speed fault diagnosis

Monitoring the health of drilling pumps and diagnosing faults is crucial for the smooth operation of oil drilling activities. However, existing deep learning algorithms struggle with varying speed conditions. To address this, we propose a method that combines physics-driven feature alignment with Dynamic Distribution Adaptation (DDA) for diagnosing drilling pump faults across varying speeds. Our physics-driven feature alignment method reduces discrepancies between samples by aligning amplitude values, angular sampling frequencies, and impulse phases of signals. DDA further improves cross-domain feature matching by dynamically adjusting marginal and conditional distributions during domain adaptation. Experiments on a three-cylinder drilling pump platform under varying speed conditions demonstrate the superior performance of our method compared to state-of-the-art approaches.

Multi-scale occlusion suppression network for occluded person re-identification

In practical application scenarios, the occlusion caused by various obstacles greatly undermines the accuracy of person re-identification. Most existing methods for occluded person re-identification focus on inferring visible parts of the body through auxiliary models, resulting in inaccurate feature matching of parts and ignoring the problem of insufficient occluded samples, which seriously affects the accuracy of occluded person re-identification. To address the above issues, we propose a multi-scale occlusion suppression network (MSOSNet) for occluded person re-identification. Specifically, we first propose a dual occlusion augmentation module (DOAM), which combines random occlusion with our proposed novel cross occlusion to generate more diverse occlusion data. Meanwhile, we design a novel occluded-aware spatial attention module (OSAM) to enable the network to focus on non-occluded areas of pedestrian images and effectively extract discriminative features. Ultimately, we propose a part feature matching module (PFMM) that utilizes graph matching algorithms to match non-occluded body parts of pedestrians. Extensive experimental results on both occluded and holistic datasets validate the effectiveness of our method.

Low-light image enhancement based on cell vibration energy model and lightness difference

Low-light image enhancement algorithms play a crucial role in revealing details obscured by darkness in images and substantially improving overall image quality. However, existing methods often suffer from issues like color or lightness distortion and possess limited scalability. In response to these challenges, we introduce a novel low-light image enhancement algorithm leveraging a cell vibration energy model and lightness difference. Initially, a new low-light image enhancement framework is proposed, building upon a comprehensive understanding and analysis of the cell vibration energy model and its statistical properties. Subsequently, to achieve pixel-level multi-lightness difference adjustment and exert control over the lightness level of each pixel independently, a lightness difference adjustment strategy is introduced utilizing Weibull distribution and linear mapping. Furthermore, to expand the adaptive range of the algorithm, we consider the disparities between HSV space and RGB space. Two enhanced image output modes are designed, accompanied by a thorough analysis and deduction of the relevant image layer mapping formulas. Finally, to enhance the reliability of experimental results, certain image faults in the SICE database are rectified using the feature matching method. Experimental results showcase the superiority of the proposed algorithm over twelve state-of-the-art algorithms. The resource code of this article will be released at https://github.com/leixiaozhou/CDEGmethod.

Adherent Peanut Image Segmentation Based on Multi-Modal Fusion.

Aiming at the problem of the difficult segmentation of adherent images due to the not fully convex shape of peanut pods, their complex surface texture, and their diverse structures, a multimodal fusion algorithm is proposed to achieve a 2D segmentation of adherent peanut images with the assistance of 3D point clouds. Firstly, the point cloud of a running peanut is captured line by line using a line structured light imaging system, and its three-dimensional shape is obtained through splicing and combining it with a local surface-fitting algorithm to calculate a normal vector and curvature. Seed points are selected based on the principle of minimum curvature, and neighboring points are searched using the KD-Tree algorithm. The point cloud is filtered and segmented according to the normal angle and the curvature threshold until achieving the completion of the point cloud segmentation of the individual peanut, and then the two-dimensional contour of the individual peanut model is extracted by using the rolling method. The search template is established, multiscale feature matching is implemented on the adherent image to achieve the region localization, and finally, the segmentation region is optimized by an opening operation. The experimental results show that the algorithm improves the segmentation accuracy, and the segmentation accuracy reaches 96.8%.

Copy-move forgery detection using Regional Density Center clustering

Copy-move forgery detection is a common image tampering detection technology. In this paper, a novel copy-move forgery detection scheme is proposed. The proposed scheme is based on Regional Density Center (RDC) clustering and Refined Length Homogeneity Filtering (RLHF) policy. First, to obtain an adequate number of keypoints in smooth or small areas of the image, the proposed scheme employs scale normalization and adjustment of the contrast threshold of the input image. Subsequently, to speed up the feature matching process, a matching algorithm based on gray value grouping is used to match the keypoints. RLHF policy is applied to filter the mismatched pairs. To guarantee a good estimation of the affine transformation, the RDC clustering algorithm is proposed to group the matched pairs. Finally, the correlation coefficients are computed to precisely locate the tampered regions. The proposed copy-move forgery detection scheme based on RDC and RLHF can effectively identify duplicated regions of digital images. It demonstrates the effectiveness and robustness of the proposed scheme over many state-of-the-art schemes on public datasets.

Semantic Visual SLAM Algorithm Based on Improved DeepLabV3+ Model and LK Optical Flow

Aiming at the problem that dynamic targets in indoor environments lead to low accuracy and large errors in the localization and position estimation of visual SLAM systems and the inability to build maps containing semantic information, a semantic visual SLAM algorithm based on the semantic segmentation network DeepLabV3+ and LK optical flow is proposed based on the ORB-SLAM2 system. First, the dynamic target feature points are detected and rejected based on the lightweight semantic segmentation network DeepLabV3+ and LK optical flow method. Second, the static environment occluded by the dynamic target is repaired using the time-weighted multi-frame fusion background repair technique. Lastly, the filtered static feature points are used for feature matching and position calculation. Meanwhile, the semantic labeling information of static objects obtained based on the lightweight semantic segmentation network DeepLabV3+ is fused with the static environment information after background repair to generate dense point cloud maps containing semantic information, and the semantic dense point cloud maps are transformed into semantic octree maps using the octree spatial segmentation data structure. The localization accuracy of the visual SLAM system and the construction of the semantic maps are verified using the widely used TUM RGB-D dataset and real scene data, respectively. The experimental results show that the proposed semantic visual SLAM algorithm can effectively reduce the influence of dynamic targets on the system, and compared with other advanced algorithms, such as DynaSLAM, it has the highest performance in indoor dynamic environments in terms of localization accuracy and time consumption. In addition, semantic maps can be constructed so that the robot can better understand and adapt to the indoor dynamic environment.

Stitching method for panoramic nail fold images based on capillary contour enhancement.

Nail fold capillaroscopy is an important means of monitoring human health. Panoramic nail fold images improve the efficiency and accuracy of examinations. However, the acquisition of panoramic nail fold images is seldom studied and the problem manifests of few matching feature points when image stitching is used for such images. Therefore, this paper presents a method for panoramic nail fold image stitching based on vascular contour enhancement, which first solves the problem of few matching feature points by pre-processing the image with contrast-constrained adaptive histogram equalization (CLAHE), bilateral filtering (BF), and sharpening algorithms. The panoramic images of the nail fold blood vessels are then successfully stitched using the fast robust feature (SURF), fast library of approximate nearest neighbors (FLANN) and random sample agreement (RANSAC) algorithms. The experimental results show that the panoramic image stitched by this paper's algorithm has a field of view width of 7.43 mm, which improves the efficiency and accuracy of diagnosis.

One-Shot Action Recognition via Multi-Scale Spatial-Temporal Skeleton Matching.

One-shot skeleton action recognition, which aims to learn a skeleton action recognition model with a single training sample, has attracted increasing interest due to the challenge of collecting and annotating large-scale skeleton action data. However, most existing studies match skeleton sequences by comparing their feature vectors directly which neglects spatial structures and temporal orders of skeleton data. This paper presents a novel one-shot skeleton action recognition technique that handles skeleton action recognition via multi-scale spatial-temporal feature matching. We represent skeleton data at multiple spatial and temporal scales and achieve optimal feature matching from two perspectives. The first is multi-scale matching which captures the scale-wise semantic relevance of skeleton data at multiple spatial and temporal scales simultaneously. The second is cross-scale matching which handles different motion magnitudes and speeds by capturing sample-wise relevance across multiple scales. Extensive experiments over three large-scale datasets (NTU RGB+D, NTU RGB+D 120, and PKU-MMD) show that our method achieves superior one-shot skeleton action recognition, and outperforms SOTA consistently by large margins.

Shape-Scale Co-Awareness Network for 3D Brain Tumor Segmentation.

The accurate segmentation of brain tumor is significant in clinical practice. Convolutional Neural Network (CNN)-based methods have made great progress in brain tumor segmentation due to powerful local modeling ability. However, brain tumors are frequently pattern-agnostic, i.e. variable in shape, size and location, which can not be effectively matched by traditional CNN-based methods with local and regular receptive fields. To address the above issues, we propose a shape-scale co-awareness network (S2CA-Net) for brain tumor segmentation, which can efficiently learn shape-aware and scale-aware features simultaneously to enhance pattern-agnostic representations. Primarily, three key components are proposed to accomplish the co-awareness of shape and scale. The Local-Global Scale Mixer (LGSM) decouples the extraction of local and global context by adopting the CNN-Former parallel structure, which contributes to obtaining finer hierarchical features. The Multi-level Context Aggregator (MCA) enriches the scale diversity of input patches by modeling global features across multiple receptive fields. The Multi-Scale Attentive Deformable Convolution (MS-ADC) learns the target deformation based on the multiscale inputs, which motivates the network to enforce feature constraints both in terms of scale and shape for optimal feature matching. Overall, LGSM and MCA focus on enhancing the scale-awareness of the network to cope with the size and location variations, while MS-ADC focuses on capturing deformation information for optimal shape matching. Finally, their effective integration prompts the network to perceive variations in shape and scale simultaneously, which can robustly tackle the variations in patterns of brain tumors. The experimental results on BraTS 2019, BraTS 2020, MSD BTS Task and BraTS2023-MEN show that S2CA-Net has superior overall performance in accuracy and efficiency compared to other state-of-the-art methods. Code: https://github.com/jiangyu945/S2CA-Net.

MSGA-Net: Progressive Feature Matching via Multi-Layer Sparse Graph Attention

MSGA-Net: Progressive Feature Matching via Multi-Layer Sparse Graph Attention

Data processing algorithm of cone-cylinder forgings process based on spectral graph theory and Hungarian matching

This paper presents a novel data processing algorithm. This algorithm is used to solve the problem of incomplete and misaligned of point cloud data due to the complexity of nuclear power containment cone-cylinder forgings and the limitation of laser scanner. Based on spectral graph theory and Hungarian matching, this paper first introduces the lazy random walk, and point cloud state vector is calculated during the walk to judge the local information, thereby eliminate the influence of noise. Then, characteristic edges are extracted using spectral graph theory. Additionally, the feature descriptors are calculated and the cost matrix is constructed using the feature descriptors. The Hungarian algorithm is applied for feature matching, facilitating a coarse registration of the point clouds. Finally, the improved point-to-plane iteration closest point is used for fine registration to ensure accurate alignment between point clouds. The experimental results demonstrate the algorithm's effectiveness in the registration of point clouds for nuclear power containment cone-cylinder forgings.

A Federated Learning-Based Industrial Health Prognostics for Heterogeneous Edge Devices Using Matched Feature Extraction

Data-driven industrial health prognostics require rich training data to develop accurate and reliable predictive models. However, stringent data privacy laws and the abundance of edge industrial data necessitate decentralized data utilization. Thus, the industrial health prognostics field is well suited to significantly benefit from federated learning (FL), a decentralized and privacy-preserving learning technique. However, FL-based health prognostics tasks have hardly been investigated due to the complexities of meaningfully aggregating model parameters trained from heterogeneous data to form a high performing federated model. Specifically, data heterogeneity among edge devices, stemming from dissimilar degradation mechanisms and unequal dataset sizes, poses a critical statistical challenge for developing accurate federated models. We propose a pioneering FL-based health prognostic model with a feature similarity-matched parameter aggregation algorithm to discriminatingly learn from heterogeneous edge data. The algorithm searches across the heterogeneous locally trained models and matches neurons with probabilistically similar feature extraction functions first, before selectively averaging them to form the federated model parameters. As the algorithm only averages similar neurons, as opposed to conventional naive averaging of coordinate-wise neurons, the distinct feature extractors of local models are carried over with less dilution to the resultant federated model. Using both cyclic degradation data of Li-ion batteries and non-cyclic data of turbofan engines, we demonstrate that the proposed method yields accuracy improvements as high as 44.5\% and 39.3\% for state-of-health estimation and remaining useful life estimation, respectively.