Template Matching Algorithm Research Articles

IRAL: Robust and versatile UAV localization using infrared vision and altitude sensor fusion

Supplementing or replacing the Global Satellite Navigation System (GNSS) for robust UAV localization remains a challenge. In this work, we propose an infrared vision and altitude sensor fusion method called IRAL, which mainly includes on-board near-infrared (NIR) beacons, off-board narrowband-pass vision sensors with the same wavelength as the beacon and strapdown high precision altitude sensors. The beacon with a high signal-to-noise ratio as a cooperative target provides robust features, thereby facilitating beacon recognition through the designed gradient-based sequential frame template matching (GSFTM) algorithm. The proposed method measures the altitude difference between the UAV and the vision sensor through the altitude sensor to accomplish depth estimation. After obtaining the beacon’s pixel coordinates and depth, combined with the intrinsics and extrinsics of the vision sensor, the observation equation can be set up to solve the UAV’s spatial position. Real-world experiments under various scenarios demonstrate that the proposed method stably achieves high accuracy.

Research on Stay Cable Tension Estimation Based on Vision-Based Monitoring System

The cables in cable-stayed bridges are the primary load-bearing components, making the accurate monitoring of cable tension crucial for assessing the service reliability of the cables and the overall safety of the bridge. This study presents the development of a non-contact, vision-based monitoring system utilizing computer vision technology for monitoring cable tension in cable-stayed bridges. The monitoring system comprises an industrial camera equipped with an infrared filter and an infrared target lamp, employing a sub-pixel template matching algorithm to achieve high-precision cable tension measurement under long-distance and multi-frequency motion conditions. Model experiments were conducted to validate the accuracy and stability of the vision-based monitoring system. Results from long-distance model experiments indicate that the system can accurately measure frequencies across various distances, with a maximum frequency error of only 0.05%. Additionally, results from multi-frequency model experiments demonstrate that the system can accurately measure multiple vibration frequencies, with a maximum frequency error of just 0.4%.

GazeLink: A multi-language low-cost mobile eye-gesture communication system with large language models for people with amyotrophic lateral sclerosis

Amyotrophic Lateral Sclerosis (ALS) patients who have severe motor and speech impairments mostly rely on their eyes and assistive technology to communicate. However, existing high-tech products are expensive and hard to access, while low-tech products are inefficient and restrictive. To mitigate the limitations, this research proposes GazeLink, a multi-language low-cost mobile application for ALS patients to communicate efficiently with only eye movements. First, the system recognizes user eye gestures like left or up with machine learning and a template-matching algorithm. Then, it converts the eye gestures to words through a keyboard that supports English, Spanish, and Chinese. For efficiency, the system employs Large Language Models (LLMs) to generate a suitable sentence with words typed by the user and the context. Finally, the system provides text-to-speech and social media post services for both verbal and digital eye-gesture communication. Simulations conclude that sentence generation with LLMs can reduce user keystrokes by 81% while maintaining 90% of semantic similarity. Usability studies with 30 participants show that GazeLink can recognize eye gestures with 94.1% accuracy in varying lighting. After rapidly learning the user interface in under 10 attempts, first-time participants typed sentences of various lengths with their eyes at 15.1 words per minute, which is 7.2x faster than the common low-tech solution E-Tran. Experiments demonstrate GazeLinks efficiency, learnability, and accuracy in eye-gesture text entry. The system is extremely affordable (less than $0.1 a month), portable, and easily accessible online. It also supports different users, lighting, smartphones, and languages. Product testing with ALS patients and personalized LLM models will be the next step.

Spindle Detection Based on Elastic Time Window and Spatial Pyramid Pooling.

Sleep spindles have emerged as valuable biomarkers for assessing cognitive abilities and related disorders, underscoring the importance of their detection in clinical research. However, template matching-based algorithms using fixed templates may not be able to fully adapt to spindles of different durations. Moreover, inspired by the multiscale feature extraction of images, the use of multiscale feature extraction methods can be used to better adapt to spindles of different frequencies and durations. Therefore, this study proposes a novel automatic spindle detection algorithm based on elastic time windows and spatial pyramid pooling (SPP) for extracting multiscale features. The algorithm utilizes elastic time windows to segment electroencephalogram (EEG) signals, enabling the extraction of features across multiple scales. This approach accommodates significant variations in spindle duration and polarization positioning during different EEG epochs. Additionally, spatial pyramid pooling is integrated into a depthwise separable convolutional (DSC) network to perform multiscale pooling on the segmented spindle signal features at different scales. Compared with existing template matching algorithms, this algorithm's spindle wave polarization positioning is more consistent with the real situation. Experimental results conducted on the public dataset DREAMS show that the average accuracy of this algorithm reaches 95.75%, with an average negative predictive value (NPV) of 96.55%, indicating its advanced performance. The effectiveness of each module was verified through thorough ablation experiments. More importantly, the algorithm shows strong robustness when faced with changes in different experimental subjects. This feature makes the algorithm more accurate at identifying sleep spindles and is expected to help experts automatically detect spindles in sleep EEG signals, reduce the workload and time of manual detection, and improve efficiency.

Noise analysis of six pulsars and a limit on the gravitational wave background

The direct detection of ultra-low frequency gravitational waves (GWs, 10−9 − 10−6 Hz) using pulsar timing arrays (PTAs) is a significant application of millisecond pulsars. The initial detection of GWs in the nano-Hertz range will probably be an isotropic stochastic GW background (GWB) emitted by the combination of merging super-massive black hole binaries (SMBHBs). In this study, we utilize data from six pulsars collected with four European PTA (EPTA) telescopes to place an upper limit on the GWB amplitude A=2.34−1.32+0.75×10−15 (95%) as a function of the spectral slope α = − 2/3, corresponding to a GWB generated by inspiraling SMBHBs. The posterior for the amplitude that we obtained from the six brightest EPTA pulsars is strongly consistent with previously published upper limits set by the three PTAs. We also compare the sensitivity of our reprocessed pulsar pulse times of arrival (ToAs) determined by the superior ToA creation method, which utilizes a combination of the best template generation algorithm and template-matching algorithm, with the ToAs determined by the EPTA groups. Our analysis shows that the reprocessed ToAs obtained with the superior ToA creation method yield narrower posteriors, indicating an overall increased sensitivity to GW.

Direct observation on the phase separation of polymer membrane embryos confined by microfluidics chips

In this paper, the growth of finger-like droplets inside various polymer membrane embryos induced by non-solvent phase separation (NIPS) is studied in details by using a standardized optical microscopic observation method. The boundary conditions of the phase separation are well-defined by microfluidics technology. The growth of finger-like droplets and Marangoni convection inside them are tracked and recorded by high-speed camera of sharp resolutions. Effects of the geometry of the membrane embryos (thickness/shape/size), humidity of the atmosphere and the viscosity of the embryos on the growth of finger-like droplets are systematically studied. Except the qualitative observations that are consistent to previous relative reports, more quantitative data are provided in this paper. The diffusing speed of the interface of the developing large droplets is measured by template matching algorithm. Strikingly, the time-dependent diffusing speed of the developing large droplets shows a wave-like declining pattern, which indicates that fluctuating boundary conditions are surrounding the membrane embryos. The Reynolds number of the flow inside the developing large droplets is estimated at the order of 10−5, the magnitude of which is 2 order smaller than that of a swimming sperm.

A Wearable Dual-Mode Probe for Image-Guided Closed-Loop Ultrasound Neuromodulation.

Low-intensity focused ultrasound (FUS) is an emerging non-invasive and spatially/temporally precise method for modulating the firing rates and patterns of peripheral nerves. This paper describes an image-guided platform for chronic and patient-specific FUS neuromodulation. The system uses custom wearable probes containing separate ultrasound imaging and modulation transducer arrays realized using piezoelectric transducers assembled on a flexible printed circuit board (PCB). Dual-mode probes operating around 4 MHz (imaging) and 1.3 MHz (modulation) were fabricated and tested on tissue phantoms. The resulting B-mode images were analyzed using a template-matching algorithm to estimate the location of the target nerve and then direct the modulation beam toward the target. The ultrasound transmit voltage used to excite the modulation array was optimized in real-time by automatically regulating functional feedback signals (the average rates of emulated muscle twitches detected by an on-board motion sensor) through a proportional and integral (PI) controller, thus providing robustness to inter-subject variability and probe positioning errors. The proposed closed-loop neuromodulation paradigm was experimentally demonstrated in vitro using an active tissue phantom that integrates models of the posterior tibial nerve and nearby blood vessels together with embedded sensors and actuators.

Stellar spectral template library construction based on generative adversarial networks

Stellar spectral template libraries play an important role in the automated analysis of stellar spectra. Synthetic template libraries cover a very large parameter space but suffer from poor matching with observed spectra. In this study, we propose a synthetic-to-observed spectral translation (SOST) method based on generative adversarial networks. The SOST method is able to calibrate synthetic spectra by converting them to the corresponding observed spectra. We applied this method to Kurucz synthetic spectra and observed spectra data from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). After that, we constructed a stellar spectral library with uniform and broad parameter distributions using the SOST-corrected Kurucz synthetic spectra. Our stellar spectral template library contains 2431 spectra spanning a parameter space of 3500–8000 K for effective temperature (Teff), 0.0–5.0 dex for surface gravity (log g), and −2.0–0.5 dex for metallicity ([Fe/H]). The spectra in the library have a resolution of R ∼ 1800 and cover the wavelength range 3900–8700 Å. In order to verify the accuracy of this template library, we used the template library and the template-matching algorithm to derive the parameters of the PASTEL database. Compared to measurements using the original synthetic template library, the accuracies of the three parameters, Teff, log g, and [Fe/H], are improved, from 140 K, 0.31 dex, and 0.21 dex to 121 K, 0.26 dex, and 0.13 dex, respectively. In addition, we re-parameterised more than six million stellar spectra released by LAMOST DR8.

Modeling of fabric sewing break detection based on U-Net network

To solve the problem of false positives and false negatives in the manual detection of fabric sewing breaks, a method of fabric sewing break detection based on the U-Net network is proposed. By detecting the adjacent distance between the characteristic contours of adjacent sewing stitches, the distribution uniformity of sewing stitches in sewing patterns is calculated, and the abnormal detection and traceability of fabric sewing broken threads are realized. First, the U-Net network sewing feature extraction model was trained using sewing images and their corresponding stitching feature annotation maps. Then, the trained network model was used to process sewing image samples to obtain binary stitching feature maps. Second, the stitching feature maps were processed using a closing operation to eliminate residual image noise. On this basis, the template matching algorithm was used to extract the stitching feature contours. Finally, according to the distance between adjacent feature contours, the fabric sewing break detection and abnormality tracing model was constructed. The model is validated by examples, and the results show that the abnormal samples of stitching lines are detected, and the corresponding break positions are given. The overall detection accuracy of the model is 95.75%, indicating that the constructed fabric sewing break detection model is effective.

Flexible thin parts multi‐target positioning method of multi‐level feature fusion

AbstractIn new energy battery manufacturing, machine vision is widely used in automated assembly scenarios for key parts. To improve the accuracy and real‐time multi‐target positioning recognition of flexible thin parts, this paper proposes a multi‐level feature fusion template matching algorithm based on the Gaussian pyramid. Firstly, the algorithm constructs a Gaussian pyramid by multi‐scale image construction. Secondly, considering the image features of each layer of the pyramid, this paper uses the grey‐based Fast Normalized Matching algorithm to obtain coarse positioning coordinates on the upper layer, and the improved Linemod‐2D algorithm is applied to the bottom layer image to get accurate positioning coordinates. Finally, the positioning coordinates returned from each layer are fused to obtain the final positioning coordinate. The experimental results show that the proposed algorithm achieves excellent performance in nickel plate positioning and recognition. It exhibits satisfactory performance in nickel sheet localization and recognition. In terms of angular error, repeat accuracy, and matching speed, it competes favourably with Halcon, VisionMaster, and SCISmart. Its positioning error closely approximates that of Halcon, effectively meeting the practical production demands for high‐speed feeding and high‐precision positioning.

Estimation of Oil Palm Tree Quantity Using Oil Palm Ecognition and SAGA GIS Based on UAV Imagery (Case Study: Dumai, Riau)

Abstract Oil palm is a plantation commodity in Indonesia that drives the growth of the national economy. The growth of oil palm is rapidly expanding every year, thus requiring efficient land monitoring strategies. Tree counting is one technique used to monitor current conditions in vast plantation areas. Utilizing UAV technology in agricultural mapping is the best option as a tool for capturing high-resolution aerial photos. The objective of this research is to automatically count the number of oil palm trees by comparing two methods and algorithms. GEOBIA (Geography-Based Image Analysis) and Deep Learning classification are methods used for automatic object counting. In this study, high-resolution aerial photo data was utilized, making object detection much more feasible. The research applied the Template Matching and Watershed Segmentation algorithm models in a sample area of 32.5 hectares divided into two blocks: Unknown-2 and E-1. It can be concluded that both methods, with their respective algorithm models, are sufficiently relevant for use in the automatic counting process of oil palm trees. This is evidenced by the accuracy test error values, which are not more than 15%, indicating that the counted oil palm tree results can be used for further data analysis.

Quantum pathways for charged track finding in high-energy collisions.

In high-energy particle collisions, charged track finding is a complex yet crucial endeavor. We propose a quantum algorithm, specifically quantum template matching, to enhance the accuracy and efficiency of track finding. Abstracting the Quantum Amplitude Amplification routine by introducing a data register, and utilizing a novel oracle construction, allows data to be parsed to the circuit and matched with a hit-pattern template, without prior knowledge of the input data. Furthermore, we address the challenges posed by missing hit data, demonstrating the ability of the quantum template matching algorithm to successfully identify charged-particle tracks from hit patterns with missing hits. Our findings therefore propose quantum methodologies tailored for real-world applications and underline the potential of quantum computing in collider physics.

Multipoint ECG Signal Extraction on Palms and Figures for Human Identification System

The ECG signal contain vital information for cardiac disease which is one of main cause of catastrophic mortality. In conventional technique, the signals are extracted through different points located on the limbs and surface of the chest. The biological impedance of human body eliminates any practical means of making fake copies of the relevant physiological traits. Moreover, this method needs the complex data acquisition system and apparatus as well as inconvenience way of acquisition of signal at the chest. Hence, there is a demand to find out the promising alternative technique to extract the ECG signals in more convenient way with high accuracy. In this paper, the real-time ECG monitor has been developed to extract the signals from the multipoint on the hands and fingers. Placement of the electrodes are done based on the acupressure point on palms and fingers. A total of 120 volunteers were investigated to develop the ECG database as the development dataset. This work uses the template matching algorithm and distance classification method to analyze and to find the best similarities between the developed dataset of ECG biometric signals to improve the identification rate. In order to lessen the noise that was recorded with the ECG signals, signal averaging was used to create ECG databases and templates. The recognition rate rose to 98% accuracy on the development dataset when the prescreening procedure was introduced to create a combined system model. ECG biometric model was created by combining the two models and using the development dataset's results. The algorithm was applied on the entire developed ECG dataset and 96% of accuracy rate was achieved in identification.

HD-TMA: A New Fast Template Matching Algorithm Implementation for Linear DAS Array Data and Its Optimization Strategies

Abstract Distributed acoustic sensing (DAS) technology, combined with existing telecom fiber-optic cable, has shown great potential in earthquake monitoring. The template matching algorithm (TMA) shows good detection capabilities but depends on heavy computational cost and diverse template events. We developed a program named HD-TMA (high-efficiency DAS template matching algorithm), which accelerates computation by 40 times on the central processing unit platform and 2 times on the graphic processing unit platform. For linear DAS array data, we introduced a fast arrival-picking algorithm based on the Hough transform to pick the time window of template waveform. The HD-TMA was successfully applied to the 2022 Ms 6.9 Menyuan earthquake aftershock sequence recorded by a DAS array, and the DAS data result was compared with a collocated short-period seismometer data’s result. Two optimization strategies were discussed based on this data set. (1) Using signal-to-noise ratio in choosing the location and aperture of the subarray and the time window of the template waveform. (2) Considering the decrease in template events’ marginal utility, we proposed applying a neural network to build a template event library, followed by the HD-TMA scanning. Such strategies can effectively reduce computational cost and improve detection capability.

Rapid Production Method of Massive Thematic Maps Based on Geospatial Knowledge Extraction

<p>Geospatial knowledge in massive academic papers can provide knowledge services such as location-based research hotspot analysis, spatio-temporal data aggregation, research results recommendation, etc. However, geospatial knowledge often exists implicitly in literature resources in unstructured form, which is difficult to be directly accessed and mined and utilized for rapid production of massive thematic maps. In this paper, we take the geospatial knowledge of the area studied as an example and introduce its extraction method in detail. An integrated feature template matching and random forest classification algorithm is proposed for accurately identifying research areas from the abstract texts of academic papers and producing thematic maps. Firstly, the precise recognition of geographical names is achieved step by step based on BiLSTM-CRF algorithm and improved heuristic disambiguation method; then, the area studied is extracted by the designed integrated feature recognition template of area studied using random forest classification algorithm, and a fast thematic map is designed for the knowledge of area studied, topic and literature. The experimental results show that the area studied recognition accuracy can reach 97%, the F-value is 96%, and the recall rate reaches 96%, achieving high accuracy and high efficiency of area studied extraction in text. Based on the geospatial knowledge, the thematic map can achieve the effect of fast map formation and accurate expression.</p> <p> </p>

Fast Force Estimation of Cable Structures Using Smartphone-Captured Video and Template Matching Algorithm

Cables are important components of long-span bridge structures, whose operation is significantly affected by cable force changes. Nowadays, cable force testing is performed by physical methods; that is, sensors are installed on the cable structure to monitor its force changes. Obviously, this strategy requires an extensive amount of time to achieve cable force calculation, which makes it impossible to monitor the force of the cable structure in real time. Meanwhile, smartphones have attracted extensive attention in the field of structural health monitoring (SHM) because of their higher cost-effectiveness than accelerometers, which include price and lifespan. Besides, many people own a smartphone, which leads to the possibility of a wider range of applications. Therefore, this paper presents a framework for the rapid estimation of the cable force of long-span bridges based on smartphones-captured video and a template matching algorithm. First, the empirical mode decomposition (EMD) method with wavelet decomposition (WD) method, that is, the EMDWD model, is constructed to extract the vibration signal of the bridge cable by eliminating the effects of smartphone vibration and environmental noise on the measured dynamic displacement, thus effectively improving the accuracy of data processing. In addition, the vibration identification model of bridge cable based on a template matching algorithm is established, and the deformation curve of cable is obtained. Finally, the frequency of bridge suspender is calculated by the Fourier transform method (FFT), and the cable force is estimated based on the smartphone-captured video.

Dazzling Evaluation of the Impact of a High-Repetition-Rate CO2 Pulsed Laser on Infrared Imaging Systems.

This article utilizes the Canny edge extraction algorithm based on contour curvature and the cross-correlation template matching algorithm to extensively study the impact of a high-repetition-rate CO2 pulsed laser on the target extraction and tracking performance of an infrared imaging detector. It establishes a quantified dazzling pattern for lasers on infrared imaging systems. By conducting laser dazzling and damage experiments, a detailed analysis of the normalized correlation between the target and the dazzling images is performed to quantitatively describe the laser dazzling effects. Simultaneously, an evaluation system, including target distance and laser power evaluation factors, is established to determine the dazzling level and whether the target is recognizable. The research results reveal that the laser power and target position are crucial factors affecting the detection performance of infrared imaging detector systems under laser dazzling. Different laser powers are required to successfully interfere with the recognition algorithm of the infrared imaging detector at different distances. And laser dazzling produces a considerable quantity of false edge information, which seriously affects the performance of the pattern recognition algorithm. In laser damage experiments, the detector experienced functional damage, with a quarter of the image displaying as completely black. The energy density threshold required for the functional damage of the detector is approximately 3 J/cm2. The dazzling assessment conclusions also apply to the evaluation of the damage results. Finally, the proposed evaluation formula aligns with the experimental results, objectively reflecting the actual impact of laser dazzling on the target extraction and the tracking performance of infrared imaging systems. This study provides an in-depth and accurate analysis for understanding the influence of lasers on the performance of infrared imaging detectors.

Study on Relay Contact Bounce Based on the Adaptive Weight Rotation Template Matching Algorithm

In order to analyze the relay action process from an imaging perspective and further investigate the bounce phenomenon of relay contacts during the contact process, this paper utilizes a high-speed shooting platform to capture images of relay action. In light of the situation where the stationary contact in the image is inclined and continuously changing, a rotation template matching algorithm based on adaptive weight is proposed. The algorithm identifies and obtains the inclination angle of the stationary contact, enabling the study of the relay contact bounce process. By extracting contact bounce distance data from the images, a bounce process curve is plotted. Combined with the analysis of the contact bounce process, the reasons for the bounce are explored. The results indicate that the proposed rotation template matching algorithm can accurately identify stationary contacts and their angles at different angles. By analyzing the contact status and bounce process of the relay contacts in conjunction with the relay structure, parameters such as the bounce time, bounce height, and time required to reach the maximum distance can be calculated. Additionally, the main reason for contact bounce in the relay studied in this paper is the limitation imposed on the continued movement of the stationary contact by the presence of the relay brackets when the kinetic energy of the contact is too high. This phenomenon occurs during the first vibration peak in the vibration process after the moving contact contacts the stationary contact. The research results provide a reference for further studying the relay contact bounce process, optimizing relay structure, and suppressing contact bounce.

Edge-Computing Oriented Real-Time Missing Track Components Detection

Track integrity is critical for railroad safety. Traditional track inspections are either labor-intensive or require centralized data processing, making them susceptible to human error and lapses between data collection and situation awareness. The advent of deep learning and computer vision provides promising potential for automated track inspections. However, few existing systems are edge-computing oriented or provide inspection results in real time. In this study, a novel ultra-portable system for real-time detection of track components, such as spikes, bolts, and clips, is developed by integrating the cutting-edge YOLOv8 object detection model with a tailored template matching algorithm. In this system, YOLOv8 serves to recognize track components, while the template matching algorithm discerns missing components based on predefined patterns. Field blind testing results verified the exceptional performance of the model in detecting track components and a remarkable speed of 98.12 frames per second. Leveraging these detection results, the proposed template matching technique displayed an impressive recall rate of 90% and an accuracy rate of 90.77% in identifying missing components. The proposed system provides an affordable and versatile solution for track inspection, aiming to improve railway safety.

A single-stage anchor-free rotating target visual detection algorithm suitable for flexible body vibration displacement measurement

The phenomenon of angular inclination of flexible structures during vibration poses a significant challenge to the applicability of visual vibration measurement methods because the target locked in the captured image will produce unknown geometric deformations such as scale, displacement, and angle in the time domain space, and the horizontal rectangular frame used for matching during target detection will also increase the false detection rate of the target due to the introduction of more background information. Such subtle geometric deformations and false detections can lead to severe fit errors in the displacement curves regressed by the visual vibration measurement algorithm. To effectively improve the accuracy and robustness of vibration image target recognition, this article takes the flexible body captured by a high-speed camera as the target of vibration displacement measurement. It introduces the rotating target detection method based on deep learning into the field of visual vibration measurement, which verifies the feasibility of the deep learning method in flexible body vibration measurement, and based on the deep convolutional neural network framework, a high-precision displacement measurement algorithm based on single-stage anchor-free rotating target detection is proposed. The algorithm in this article first uses the CSPDarknet backbone network to extract multi-scale features of flexible structural image sequences. It then uses PANet to fuse the top-down and bottom-up bidirectional feature maps of the four bridge target feature maps obtained through the backbone network. The shallow and deep information is used for semantic feature fusion and combined with the Coordinate Attention mechanism to achieve target finding and fine positioning on the feature map. Finally, we use the coordinates of the bounding box obtained from the test to regress the position offset of the object’s center point. To verify the accuracy of the algorithm in this article, we conducted experimental validation on the cable-stayed bridge model and the actual bridge and compared the performance with the traditional template matching algorithm, differential optical flow method, and various deep learning algorithms with different localization principles, as well as the displacement signals collected and processed by accelerometers. The experimental results of time-frequency characteristics analysis show that the vibration displacement trajectories regressed by the algorithm in this paper have the best overlap with the displacement measurements collected by the accelerometer, which verifies that the algorithm in this article has good application potential and implementation space in the field of condition monitoring of flexible structural bodies.