Template Matching Research Articles

The application of template matching and novel unsupervised and supervised machine learning methodology to forensic hair analysis

ABSTRACT Hair pigmentation is a valuable feature in forensic hair analysis and hair comparisons. Microscopic pigmentation features from participants' hair were classified and documented. The discriminating power of template matching, unsupervised and supervised machine learning methods were compared to determine which method best distinguished between participants and accurately assigned hairto an individual, based on pigmentation features. Template matching analysis revealed a higher frequency of inter-person matches compared to intra-person matches, and the unsupervised model’s predicted labels did not align with true labels. These findings suggest the presence of an unknown dimensionality beyond the observable pigmentation features used in forensic hair classification, highlighting the crucial role of forensic expertise. In contrast, supervised machine learning demonstrated superior accuracy and greater interpretability due to its training on labelled data, making it more appropriate for forensic applications. This research underscores the continued importance of human expertise, particularly in the initial classification of training data for supervised machine learning. The potential applications of supervised machine learning in forensic science include training, competency testing and rapid intelligence as a novel and innovative tool while advancing the field towards more objective methodologies.

Computer vision-based substructure isolation method for localized damage identification

In recent years, vision-based structural damage identification techniques have garnered significant attention due to their simplicity and cost-effectiveness. Nevertheless, dynamic response-based vision methods face challenges related to the limitations of the field of view (FOV), i.e., the extent of the FOV is often sacrificed in order to ensure sufficient monitoring accuracy, which reduces the amount of data available for structural health monitoring (SHM). This study presents a solution to this problem by employing the substructure isolation method (SIM), which focuses on local vibrations of the structure and enables local damage identification by isolating the area of interest. Additionally, a method combining kernelized correlation filter (KCF) with sub-pixel template matching is used to extract vibration information recorded by visual sensor. This strategy balances both the efficiency and accuracy of displacement extraction. In numerical simulations, the performance of the SIM based on acceleration response and displacement response is compared, demonstrating the potential compatibility of visual sensors with the SIM. Finally, the effectiveness of the proposed vision-based local damage identification method is validated through vibration testing of a shear frame model.

An Investigation of Multimodal Kinematic Template Matching for Ray Pointing Prediction for Target Selection in VR

We explore the use of multimodal input to predict the landing position of a ray pointer while selecting targets in a virtual reality (VR) environment. We first extend a prior 2D Kinematic Template Matching technique to include head movements. This new technique, Head-Coupled Kinematic Template Matching, was found to improve upon the existing 2D approach, with an angular error of 10.0° when a user was 40% of the way through their movement. We then investigate two additional models that incorporated eye gaze, which were both found to further improve the predicted landing positions. The first model, Gaze-Coupled Kinematic Template Matching resulted in angular error of 6.8° for reciprocal target layouts and 9.1° for random target layouts, when a user was 40% of the way through their movement. The second model, Hybrid Kinematic Template Matching, resulted in angular error of 5.2° for reciprocal target layouts and 7.2° for random target layouts when a user was 40% of the way through their movement. We also found that using just the current gaze location resulted in sufficient predictions in many conditions. We reflect on our results by discussing the broader implications of utilizing multimodal input to inform selection predictions in VR.

Spectroscopic analysis of the strongly lensed SN Encore: Constraints on cosmic evolution of Type Ia supernovae

Abstract Strong gravitational lensing magnifies the light from a background source, allowing us to study these sources in detail. Here, we study the spectra of a z = 1.95 lensed Type Ia supernova SN Encore for its brightest Image A, taken 39 days apart. We infer the spectral age with template matching using the supernova identification (SNID) software and find the spectra to be at 29.0 ±5.0 and 37.4 ±2.8 rest-frame days post maximum respectively, consistent with separation in the observer frame after accounting for time-dilation. Since SNe Ia measure dark energy properties by providing relative distances between low- and high-z SNe, it is important to test for evolution of spectroscopic properties. Comparing the spectra to composite low-z SN Ia spectra, we find strong evidence for similarity between the local sample and SN Encore. The line velocities of common SN Ia spectral lines, Si II 6355 and Ca II NIR triplet are consistent with the distribution for the low-z sample as well as other lensed SNe Ia, e.g. iPTF16geu (z = 0.409)and SN H0pe (z = 1.78). The consistency between the low-z sample and lensed SNe at high-z suggests no obvious cosmic evolution demonstrating their using as high-z distance indicators, though this needs to be confirmed/refuted via a larger sample. We also find that the spectra of SN Encore match the predictions for explosion models very well. With future large samples of lensed SNe Ia e.g. with the Vera C. Rubin Observatory, spectra at such late phases will be important to distinguish between different explosion scenarios.

Lifecycle of an Isolated Seismic Swarm in Continental Southern Norway from Nucleation, Acceleration, and Yielding to the Demise of Fault Slip

Abstract An intraplate earthquake swarm started near the Bitdal valley in the seismically inactive mountains of Southern Norway in summer 2020 and continued for more than two years. We detected 1600 earthquakes through template matching and obtained high-resolution relative magnitudes and locations for the events to resolve the swarm’s spatiotemporal evolution and tectonic implications. The detection results reveal that no earthquakes occurred at the swarm site for at least 22 yr prior to the current activity. In July 2020, six months before the swarm’s initially recognized onset, weak precursory events appeared. Hypocenter relocation shows that the swarm develops along a single 1.5 km patch on a northwest–southeast-striking fault with oblique-normal sense containing a left-lateral component. The seismicity follows a dual-velocity migration for which the slow, general migration reverses sense twice, and 30 short bursts show internal migration both with and against the general migration direction. Changes in the distribution of events let us divide the swarm into four phases: precursory activity, accelerating growth, main slip, and slow decline. The observed seismicity bursts, pauses, and dual migration velocities are consistent with a rupture-driven mechanism for which slip initiates spontaneously and keeps going in slow and quick failure cascades due to stress transfer and slip weakening.

Solving Ambiguity in EBSD Indexing of Long-Period Stacking Ordered (LPSO) Phase in Mg with Template Matching Approach

Magnesium (Mg) alloys with long-period stacking ordered (LPSO) phases are receiving increasing interest because of their excellent mechanical performance. The close similarity in atomic stacking sequences between different LPSO polytypes and Mg lattice often leads to ambiguous indexing in electron backscatter diffraction (EBSD), a commonly used material characterization technique. Instead of the Hough transformation approach used in commercial software, an alternative indexing approach, which can catch subtle differences by matching experimental patterns with simulated ones, is explored in this study. Our results, showing ~94% of mapping data being correctly indexed as the target phase, 14H LPSO, demonstrate the capability of not only resolving the LPSO phases but also distinguishing different LPSO polytypes. This approach offers a valuable, if not unique, solution for the microscale characterization of LPSO phases, enabling precise microstructure tuning to further promote the mechanical properties of Mg alloys.

Naked-eye 3D visualization of cultural relics based on integrated imaging

AbstractDigital technology has become crucial for the protection of cultural relics, enabling their acquisition, preservation, exhibition, and dissemination in a non-contact manner.At present, the three-dimensional(3D) visualization technology of cultural relics focuses on the research of non-true 3D visualization, which cannot provide continuous viewing angle and takes a long time to render. This paper focuses on the acquisition and reproduction of digital information related to cultural relics, and proposes to achieve the true 3D display of cultural relics from a continuous perspective based on integrated imaging(InIm). Building up on traditional InIm, an Element image array (EIA) generation algorithm based on local depth template matching is designed by using spatial geometry relation. Experimental results show that the algorithm not only enables naked-eye 3D visualization of cultural relics, but also the generation speed is more than twice that of the compared 3D display data source generation algorithm.

Six-degrees-of-freedom pelvic bone monitoring on 2D kV intrafraction images to enable multi-target tracking for locally advanced prostate cancer.

Patients with locally advanced prostate cancer require the prostate and pelvic lymph nodes to be irradiated simultaneously during radiation therapy treatment. However, relative motion between treatment targets decreases dosimetric conformity. Current treatment methods mitigate this error by having large treatment margins and often prioritize the prostate at patient setup at the cost of lymph node coverage. Treatment accuracy can be improved through real-time multi-target adaptation which requires simultaneous motion monitoring of both the prostate and lymph node targets. This study developed and evaluated an intrafraction pelvic bone motion monitoring method as a surrogate for pelvic lymph node displacement to be combined with prostate motion monitoring to enable multi-target six-degrees-of-freedom (6DoF) tracking using 2D kV projections acquired during treatment. A method to monitor pelvic bone translation and rotation was developed and retrospectively applied to images from 20 patients treated in the TROG 15.01 Stereotactic Prostate Ablative Radiotherapy with Kilovoltage Intrafraction Monitoring (KIM) trial. The pelvic motion monitoring method performed template matching to calculate the 6DoF position of the pelvis from 2D kV images. The method first generated a library of digitally reconstructed radiographs (DRRs) for a range of imaging angles and pelvic rotations. The normalized 2D cross-correlations were then calculated for each incoming kV image and a subset of DRRs and the DRR with the maximum correlation coefficient was used to estimate the pelvis translation and rotation. Translation of the pelvis in the unresolved direction was calculated using a 3D Gaussian probability estimation method. Prostate motion was measured using the KIM marker tracking method. The pelvic motion monitoring method was compared to the ground truth obtained from a 6DoF rigid registration of the CBCT and CT. The geometric errors of the pelvic motion monitoring method demonstrated sub-mm and sub-degree accuracy and precision in the translational directions ( , , ) and rotational directions ( , , ). The 3D relative displacement between the prostate and pelvic bones exceeded 2, 3, 5, and 7mm for approximately 66%, 44%, 12%, and 7% of the images. Accurate intrafraction pelvic bone motion monitoring in 6DoF was demonstrated on 2D kV images, providing a necessary tool for real-time multi-target motion-adapted treatment.

Model-based estimation of heart movements using microwave Doppler radar sensor.

Heart rate is one of the most crucial vital signs and can be measured remotely using microwave Doppler radar. As the distance between the body and the Doppler radar sensor increases, the output signal weakens, making it difficult to extract heartbeat waveforms. In this study, we propose a new template-matching method that addresses this issue by simulating Doppler radar signals. This method extracts the heartbeat waveform with higher accuracy while the participant is naturally sitting in a chair. An extended triangular wave model was created as a mathematical representation of cardiac physiology, taking into account heart movements. The Doppler radar output signal was then simulated based on this model to automatically obtain a template for one cycle. The validity of the proposed method was confirmed by calculating the PPIs using the template and comparing their accuracy to the R-R intervals (RRIs) of the electrocardiogram for five participants and by analyzing the signals of eight participants in their natural state using the mathematical model of heart movements. All measurements were conducted from a distance of 500 mm. The correlation coefficients between the RRIs of the electrocardiogram and the PPIs using the proposed method were examined for five participants. The correlation coefficients were 0.93 without breathing and 0.70 with breathing. This demonstrates a higher correlation considering the long distance of 500 mm, and the fact that body movements were not specifically restricted, suggesting that the proposed method can successfully estimate RRI. The average correlation coefficients, calculated between the Doppler output signals and the templates for each of the eight participants, exceeded 0.95. Overall, the proposed method showed higher correlation coefficients than those reported in previous studies, indicating that our method performed well in extracting heartbeat waveforms. Our results indicate that the proposed method of remote heart monitoring using microwave Doppler radar demonstrates higher accuracy in estimating the RRI of the electrocardiogram while at rest sitting in a chair, and the ability to extract the heartbeat waveforms from the measured Doppler output signal, eliminating the need to create templates in advance as required by conventional template matching methods. This approach offers more flexibility in the measurement environment than conventional methods.

Visual object tracking based on adaptive rain streak removal and fused dynamic template matching

Visual object tracking based on adaptive rain streak removal and fused dynamic template matching

Projection segmentation-based image recognition technology for automatic reading of gas meter

In view of the shortcomings of the existing gas meter reading methods, this paper introduces an automatic reading method and calibration device based on the projection segmentation method, which uses the color difference between the character part and the rest part of the last code wheel of the gas meter counter to realize image recognition of the turned characters, and then calculates the cumulative volume indication of gas meter based on the number of turned characters. The pixel projection value scanned by the camera device at the horizontal centerline of the last code wheel changes alternately when the code wheel rotates. The proposed projection segmentation method does not require recognition of specific characters, simplifying the algorithm and making it suitable for most calibration devices. Experiments show that the accuracy rate of the proposed method is 100% even under low-light conditions, which is a great improvement compared with the traditional character recognition method. Additionally, the reading resolution of the proposed method is improved by 10 times compared with the existing photoelectric sampling method and template matching method, and the total calibration time can be reduced by 6.7%–58.7%, which significantly enhances the calibration efficiency.

Automated quality control analysis for American College of Radiology (ACR) digital mammography (DM) phantom images.

To develop and validate an automated software analysis method for mammography image quality assessment of the American College of Radiology (ACR) digital mammography (DM) phantom images. Twenty-seven DICOM images were acquired using Fuji mammography systems. All images were evaluated by three expert medical physicists using the Royal Australian and New Zealand College of Radiologists (RANZCR) mammography quality control guideline. To enhance the robustness and sensitivity assessment of our algorithm, an additional set of 12 images from a Hologic mammography system was included to test various phantom positional adjustments. The software automatically chose multiple regions of interest (ROIs) for analysis. A template matching method was primarily used for image analysis, followed by an additional method that locates and scores each target object (speck groups, fibers, and masses). The software performance shows a good to excellent agreement with the average scoring of observers (intraclass correlation coefficient [ICC] of 0.75, 0.79, 0.82 for speck groups, fibers, and masses, respectively). No significant differences were found in the scoring of target objects between human observers and the software. Both methods achieved scores meeting the pass criteria for speck groups and masses. Expert observers allocated lower scores to fiber objects, with diameters less than 0.61mm, when compared to the software. The software was able to accurately score objects when the phantom position changed by up to 25mm laterally, up to 5 degrees rotation, and overhanging the chest wall edge by up to 15mm. Automated software analysis is a feasible method that may help improve the consistency and reproducibility of mammography image quality assessment with reduced reliance on human interaction and processing time.

A Hybrid Approach for Robust Object Detection: Integrating Template Matching and Faster R-CNN

Object detection is a critical task in computer vision, with applications ranging from autonomous vehicles to medical imaging. Traditional methods like template matching offer precise localization but struggle with variations in object appearance, while deep learning approaches such as Faster R-CNN excel in handling diverse and complex datasets but often require extensive computational resources and large amounts of labeled data. This paper proposes a hybrid approach that integrates template matching with Faster R-CNN to leverage the strengths of both techniques. By combining the accuracy of template matching with the robustness and generalization capabilities of Faster R-CNN, our method achieves superior performance in challenging scenarios, including objects with occlusions, varying scales, and complex backgrounds. Extensive experiments demonstrate that the hybrid model not only enhances detection accuracy but also reduces computational load, making it a practical solution for real-world applications.

Three-Dimensional Reconstruction of Zebra Crossings in Vehicle-Mounted LiDAR Point Clouds

In the production of high-definition maps, it is necessary to achieve the three-dimensional instantiation of road furniture that is difficult to depict on traditional maps. The development of mobile laser measurement technology provides a new means for acquiring road furniture data. To address the issue of traffic marking extraction accuracy in practical production, which is affected by degradation, occlusion, and non-standard variations, this paper proposes a 3D reconstruction method based on energy functions and template matching, using zebra crossings in vehicle-mounted LiDAR point clouds as an example. First, regions of interest (RoIs) containing zebra crossings are obtained through manual selection. Candidate point sets are then obtained at fixed distances, and their neighborhood intensity features are calculated to determine the number of zebra stripes using non-maximum suppression. Next, the slice intensity feature of each zebra stripe is calculated, followed by outlier filtering to determine the optimized length. Finally, a matching template is selected, and an energy function composed of the average intensity of the point cloud within the template, the intensity information entropy, and the intensity gradient at the template boundary is constructed. The 3D reconstruction result is obtained by solving the energy function, performing mode statistics, and normalization. This method enables the complete 3D reconstruction of zebra stripes within the RoI, maintaining an average planar corner accuracy within 0.05 m and an elevation accuracy within 0.02 m. The matching and reconstruction time does not exceed 1 s, and it has been applied in practical production.

AUTOMATIC PANEL QUALITY MONITORING SYSTEM USING VISION-BASED ABNORMAL OBJECT DETECTION

The rate of “Not Good” products due to the presence of abnormal objects during the quality control inspection of mobile phone’s panels is as high as 38 percent without supervision and 19 percent with naked eye’s inspection. To reduce the loss of small abnormal objects and improve the recognition rate of object detection of automatic panel quality monitoring, the paper proposes abnormal object recognition based on a template matching and subtract background technique. Firstly, the object recognition network based on convolutional neural networks (CNNs) is introduced initially. Then, the Canny Edge Detection method improves the image input quality. Next, template matching is utilized to discover things that depart from a standard pattern or a predetermined model. Models for background reduction will be implemented instantly into smartphone panel quality checking systems. Continually, a vision-based quality control (QC) strategy will be developed. Lastly, experimental results from a practical vision-based automatic anomalous object identification system demonstrate the viability and enhancement of the suggested strategy.

Microseismicity at the Time of a Large Creep Event on the Calaveras Fault is Unresponsive to Stress Changes

The potential relationship between surface creep and deeper geological processes is unclear, even on one of the world’s best-studied faults. From June to August 2021, a large creep event with surface slip of more than 16 mm was recorded on the Calaveras fault in California, part of the San Andreas fault system. This event initially appeared to be accompanied by along-fault migration of seismicity, suggesting it penetrated to depth. Other studies have suggested that surface creep events are likely a shallow feature, unrelated to deep seismicity. To provide more detail on the relationship between earthquakes, surface creep, and potential aseismic slip at seismogenic depth, we tripled the number of earthquakes in the Northern California Earthquake Catalog in the region of the creep event for all of 2021. This was accomplished by implementing earthquake detection techniques based on both template matching (EQCorrscan) and AI-based automatic earthquake phase picking (PhaseNet). After manual inspection, the detected earthquakes were first located using Hypoinverse and subsequently relocated via GrowClust. Our enhanced catalog indicates that the spatiotemporal pattern of earthquakes here is not strongly influenced by the creep event and is better explained by structural heterogeneity than transient stress changes, indicating a decoupling of seismicity rate and surficial creep on this major fault.

Real-Time Detection of Young and Old Faces Using Template Matching and Fuzzy Associative Memory

Real-Time Detection of Young and Old Faces Using Template Matching and Fuzzy Associative Memory

A Real-Time Global Re-Localization Framework for a 3D LiDAR-Based Navigation System

Place recognition is widely used to re-localize robots in pre-built point cloud maps for navigation. However, current place recognition methods can only be used to recognize previously visited places. Moreover, these methods are limited by the requirement of using the same types of sensors in the re-localization process and the process is time consuming. In this paper, a template-matching-based global re-localization framework is proposed to address these challenges. The proposed framework includes an offline building stage and an online matching stage. In the offline stage, virtual LiDAR scans are densely resampled in the map and rotation-invariant descriptors can be extracted as templates. These templates are hierarchically clustered to build a template library. The map used to collect virtual LiDAR scans can be built either by the robot itself previously, or by other heterogeneous sensors. So, an important feature of the proposed framework is that it can be used in environments that have never been visited by the robot before. In the online stage, a cascade coarse-to-fine template matching method is proposed for efficient matching, considering both computational efficiency and accuracy. In the simulation with 100 K templates, the proposed framework achieves a 99% success rate and around 11 Hz matching speed when the re-localization error threshold is 1.0 m. In the validation on The Newer College Dataset with 40 K templates, it achieves a 94.67% success rate and around 7 Hz matching speed when the re-localization error threshold is 1.0 m. All the results show that the proposed framework has high accuracy, excellent efficiency, and the capability to achieve global re-localization in heterogeneous maps.

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%.

Reducing Instrumental Errors in Parkes Pulsar Timing Array Data

This paper demonstrates the impact of state-of-the-art instrumental calibration techniques on the precision of arrival times obtained from 9.6 yr of observations of millisecond pulsars using the Murriyang 64 m CSIRO Parkes Radio Telescope. Our study focuses on 21 cm observations of 25 high-priority pulsars that are regularly observed as part of the Parkes Pulsar Timing Array project, including those predicted to be the most susceptible to calibration errors. We employ measurement equation template matching (METM) for instrumental calibration and matrix template matching (MTM) for arrival time estimation, resulting in significantly improved timing residuals with up to a sixfold reduction in white noise compared to arrival times estimated using scalar template matching and conventional calibration based on the ideal feed assumption. The median relative reduction in white noise is 33%, and the maximum absolute reduction is 4.5 μs. For PSR J0437−4715, METM and MTM reduce the best-fit power-law amplitude (2.7σ) and spectral index (1.7σ) of the red noise in the arrival time residuals, which can be tentatively interpreted as mitigation of 1/f noise due to otherwise unmodeled steps in polarimetric response. These findings demonstrate the potential to directly enhance the sensitivity of pulsar timing array experiments through more accurate methods of instrumental calibration and arrival time estimation.