Template Matching Research Articles

The Matrix Profile in Seismology: Template Matching of Everything With Everything

AbstractTemplate matching has proven to be an effective method for seismic event detection, but is biased toward identifying events similar to previously known events, and thus is ineffective at discovering events with non‐matching waveforms (e.g., those dissimilar to existing catalog events). In principle, this limitation can be overcome by cross‐correlating every segment (possible template) of a seismogram with every other segment to identify all similar event pairs, but doing so has been previously considered computationally infeasible for long time series. Here we describe a method, called the ‘Matrix Profile’ (MP), a “correlate everything with everything” calculation that can be efficiently and scalably computed. The MP returns the maximum value of the correlation coefficient of every sub‐window of continuous data with every other sub‐window, as well as the best‐correlated sub‐window location. Here we show how MP methods can obtain valuable results when applied to months and years of continuous seismic data in both local and global case studies. We find that the MP can identify many new events in Parkfield, California seismicity that are not contained in existing event catalogs and that it can efficiently find clusters of similar earthquakes in global seismic data. Either used by itself, or as a starting point for subsequent template matching calculations, the MP is likely to provide a useful new tool for seismology research.

Application and analysis of machine learning in handwritten digit recognition

It appears from the information that Character recognition research is currently focused on handwritten digit recognition, a significant subfield of optical character recognition, i.e. the use of computers to recognise and process digital information. In today's increasingly mainstream computer and data era, handwritten numeric recognition can simplify the process of paper-based offices, reduce the intensity of work when analysing data statistics afterwards and improve work efficiency. There are many algorithms to achieve recognition, each with different recognition accuracy, implementation efficiency and application scope. Based on the basic concepts described above, this thesis investigates the efficiency and accuracy of three algorithms - template matching, SVM and deep learning - in recognising handwritten digits with different sample sizes. The models or kernel functions currently used to process data of varying complexity and restricted scenarios also require continuous improvement to ensure accuracy, making it all the more important to discuss them in detail.

Hand Written Character Recognition Using CNN Model

Abstract: Handwritten character recognition (HWR) plays a critical role in document processing, robotic automation, and historical document analysis. While traditional methods relied on template matching and feature engineering, these often struggled with diverse writing styles and noise. Convolutional neural networks (CNNs) have emerged as a powerful alternative, achieving remarkable accuracy in HWR tasks. This paper delves into enhancing HWR performance using CNNs on the EMNIST Balanced dataset. We investigate the impact of data augmentation techniques on model generalizability and propose a custom architecture optimized for the task. Through detailed analysis of performance metrics, confusion matrices, and visualization of predictions, we gain valuable insights into the model's behavior and potential areas for improvement.

Automatic evaluation method for vehicle audio warning system using MFCC-polynomial hybrid feature

In the evaluation of vehicle audio warning system, there is no automatic method. Besides, due to the noise interference of in-vehicle environmental, the quantity limitation and only positive training samples, the accuracy of traditional template matching or identification methods for audio is low. To solve the above problems, an efficient, accurate, and automatic evaluation method is proposed for vehicle audio warning system. Firstly, logmmse-spectrum subtraction method is used to filter the dynamic noise and static noise of the evaluation audio acquired in the in-vehicle environment. Secondly, the end point detection based on short-time energy is used to obtain the effective audio segment after noise reduction, and the start time of the audio warning segment can be accurately obtained. Then, the Mel Frequency Cepstrum Coefficient (MFCC) feature and the polynomial fitting feature of each audio segment are extracted. The hybrid features are treated as the input of the Hidden Markov Model-Gaussian Mixture Model (GMM-HMM) based audio matching model. Finally, according to frame shift set by endpoint detection and the audio sampling frequency, the emitted time of matched audio warning can be calculated to support the evaluation of vehicle audio warning system. The experimental result shows that, with dynamic-static noise reduction and MFCC-polynomial hybrid feature, the average matching accuracy of the proposed method reaches 99.6% in the case of only five training samples.

Low-Frequency Blast Detection Using a Large-N Dark Fiber in Noisy Environments: Template Matching and Optimal Channel Selection

Abstract Distributed acoustic sensing (DAS), deployed on dark telecom fiber, is well-positioned to play a significant role in seismic monitoring networks because of the combination of a large aperture, fine spatial resolution, broadband sensitivity, and the ubiquitous presence of unused telecommunication fibers in many areas of the world. In this study, we explore the feasibility of dark-fiber array deployed in a noisy environment for detecting small explosions. We test the effectiveness of template matching for the detection of low-frequency blasts generated by mining activities in the Imperial Valley, California. We first evaluate dark-fiber detection performance by analyzing the relationship between detection threshold (DT) and the number of DAS channels used. We find that although, as expected, increasing the number of channels yields higher detection significance and lowers DT, the gain in performance is far from linear, with local anomalies across the DAS cable associated with zones of higher noise. We focus on investigating the types of noise affecting template matching and practical approaches mitigating anthropogenic noise that lower detection performance. Using median absolute deviation, we identify two types of noise sources affecting detection performance. Next, we design a voting scheme that selects DAS channels contributing to lowering of the DT and ensures improvement in detection when adding sequential channels. Finally, we compare dark-fiber detection performance with nearby conventional seismometers and find that a single station can outperform up to ∼10 DAS channels. However, using the full aperture of our dark-fiber transect allows to obtain ∼10% lower DT and yields fewer false-positive detections than an array of four seismometers. Methodological solutions for noise assessment and channel selection allow us to fully benefit from the large aperture and dense sampling offered by dark fiber. The findings of this study are a step toward incorporating existing telecom fibers into novel explosion-monitoring workflows.

Molecular dynamics simulation of deformation behavior of nanocrystal CuNiAlCo medium-entropy alloys

Applying the molecular simulation method, the melting, elastic, and plastic properties of nanocrystal CuNiAlCo medium entropy alloy are investigated in this work. The results show that the melting point in nanocrystal CuNiAlCo is decreased with decreasing crystallite size from 5.0 to 3.1 nm. Compared to the bulk modulus, the crystallite size has a discernible influence on both the shear and Young's modulus. The uniaxial tension calculations suggest that the tensile strength decreases with decreasing crystallite size, and the inverse Hall–Petch relationship is found in nanocrystal CuNiAlCo. The polyhedral template matching analysis illustrates that the FCC constitution of CuNiAlCo gradually decreases with the decrease in crystallite size. When the strain is 30%, the amorphization would occur in uniaxial tension. The atomic structural evolution of CuNiAlCo with the crystallite size of 5.0 nm under the uniaxial strain verifies that the stacking fault reconstruction and amorphization are the dominant plastic deformation mechanism for nanocrystal CuNiAlCo medium entropy alloy. The shockley dislocation plays an essential role in uniaxial tension.

DCSPose: A Dual-Channel Siamese Framework for Unseen Textureless Object Pose Estimation

The demand for object pose estimation is steadily increasing, and deep learning has propelled the advancement of this field. However, the majority of research endeavors face challenges in their applicability to industrial production. This is primarily due to the high cost of annotating 3D data, which places higher demands on the generalization capabilities of neural network models. Additionally, existing methods struggle to handle the abundance of textureless objects commonly found in industrial settings. Finally, there is a strong demand for real-time processing capabilities in industrial production processes. Therefore, in this study, we introduced a dual-channel Siamese framework to address these challenges in industrial applications. The architecture employs a Siamese structure for template matching, enabling it to learn the matching capability between the templates constructed from high-fidelity simulated data and real-world scenes. This capacity satisfies the requirements for generalization to unseen objects. Building upon this, we utilized two feature extraction channels to separately process RGB and depth information, addressing the limited feature issue associated with textureless objects. Through our experiments, we demonstrated that this architecture effectively estimates the three-dimensional pose of objects, achieving a 6.0% to 10.9% improvement compared to the state-of-the-art methods, while exhibiting robust generalization and real-time processing capabilities.

Point cloud based hand gesture recognition using template matching

Hand gesture recognition is important in human-computer interaction with wide applications in many fields. Different from common hand gesture recognition based on 2D images acquired from RGB camera, the utilization of 3D images provides additional spatial information about the target and attracts more and more attention in hand gesture recognition. However, most 3D images for hand gesture recognition are based on depth maps, which only take the distance information as a channel of 2D images, without taking full use of the 3D information. Besides, greater data volume of 3D images brings challenges to the arithmetic facility of hand gesture recognition. Here, we proposed a point cloud based method for hand gesture recognition. To fully use the 3D information, plane points for template matching were extracted based on their normal distributions, which leads to the average recognition rate over 97%. Pre-classification was implemented to ensure a high-efficient recognition without additional requirements for the computer. The proposed method may provide approach for accurate and efficient hand gesture recognition based on 3D images.

Automated Scanning Probe Tip State Classification without Machine Learning.

The manual identification and in situ correction of the state of the scanning probe tip is one of the most time-consuming and tedious processes in atomic-resolution scanning probe microscopy. This is due to the random nature of the probe tip on the atomic level, and the requirement for a human operator to compare the probe quality via manual inspection of the topographical images after any change in the probe. Previous attempts to automate the classification of the scanning probe state have focused on the use of machine learning techniques, but the training of these models relies on large, labeled data sets for each surface being studied. These data sets are extremely time-consuming to create and are not always available, especially when considering a new substrate or adsorbate system. In this paper, we show that the problem of tip classification from a topographical image can be solved by using only a single image of the surface along with a small amount of prior knowledge of the appearance of the system in question with a method utilizing template matching (TM). We find that by using these TM methods, comparable accuracy and precision can be achieved to values obtained with the use of machine learning. We demonstrate the efficacy of this technique by training a machine learning-based classifier and comparing the classifications with the TM classifier for two prototypical silicon-based surfaces. We also apply the TM classifier to a number of other systems where supervised machine learning-based training was not possible due to the nature of the training data sets. Finally, the applicability of the TM method to surfaces used in the literature, which have been classified using machine learning-based methods, is considered.

An effective premature ventricular contraction detection algorithm based on adaptive template matching and characteristic recognition

An effective premature ventricular contraction detection algorithm based on adaptive template matching and characteristic recognition

Multiple mechanisms of visual prediction as revealed by the timecourse of scene-object facilitation.

Not only semantic, but also recently learned arbitrary associations have the potential to facilitate visual processing in everyday life-for example, knowledge of a (moveable) object's location at a specific time may facilitate visual processing of that object. In our prior work, we showed that previewing a scene can facilitate processing of recently associated objects at the level of visual analysis (Smith and Federmeier in Journal of Cognitive Neuroscience, 32(5):783-803, 2020). In the current study, we assess how rapidly this facilitation unfolds by manipulating scene preview duration. We then compare our results to studies using well-learned object-scene associations in a first-pass assessment of whether systems consolidation might speed up high-level visual prediction. In two ERP experiments (N = 60), we had participants study categorically organized novel object-scene pairs in an explicit paired associate learning task. At test, we varied contextual pre-exposure duration, both between (200 vs. 2500 ms) and within subjects (0-2500 ms). We examined the N300, an event-related potential component linked to high-level visual processing of objects and scenes and found that N300 effects of scene congruity increase with longer scene previews, up to approximately 1-2 s. Similar results were obtained for response times and in a separate component-neutral ERP analysis of visual template matching. Our findings contrast with prior evidence that scenes can rapidly facilitate visual processing of commonly associated objects. This raises the possibility that systems consolidation might mediate different kinds of predictive processing with different temporal profiles.

Learning accurate template matching with differentiable coarse-to-fine correspondence refinement

Template matching is a fundamental task in computer vision and has been studied for decades. It plays an essential role in manufacturing industry for estimating the poses of different parts, facilitating downstream tasks such as robotic grasping. Existing methods fail when the template and source images have different modalities, cluttered backgrounds, or weak textures. They also rarely consider geometric transformations via homographies, which commonly exist even for planar industrial parts. To tackle the challenges, we propose an accurate template matching method based on differentiable coarse-to-fine correspondence refinement. We use an edge-aware module to overcome the domain gap between the mask template and the grayscale image, allowing robust matching. An initial warp is estimated using coarse correspondences based on novel structure-aware information provided by transformers. This initial alignment is passed to a refinement network using references and aligned images to obtain sub-pixel level correspondences which are used to give the final geometric transformation. Extensive evaluation shows that our method to be significantly better than state-of-the-art methods and baselines, providing good generalization ability and visually plausible results even on unseen real data.

COMPARATIVE STUDY: THRESHOLD AND TEMPLATE MATCHING TECHNIQUE

COMPARATIVE STUDY: THRESHOLD AND TEMPLATE MATCHING TECHNIQUE

TomoNet: A streamlined cryogenic electron tomography software pipeline with automatic particle picking on flexible lattices.

Cryogenic electron tomography (cryoET) is capable of determining in situ biological structures of molecular complexes at near-atomic resolution by averaging half a million subtomograms. While abundant complexes/particles are often clustered in arrays, precisely locating and seamlessly averaging such particles across many tomograms present major challenges. Here, we developed TomoNet, a software package with a modern graphical user interface to carry out the entire pipeline of cryoET and subtomogram averaging to achieve high resolution. TomoNet features built-in automatic particle picking and three-dimensional (3D) classification functions and integrates commonly used packages to streamline high-resolution subtomogram averaging for structures in 1D, 2D, or 3D arrays. Automatic particle picking is accomplished in two complementary ways: one based on template matching and the other using deep learning. TomoNet's hierarchical file organization and visual display facilitate efficient data management as required for large cryoET datasets. Applications of TomoNet to three types of datasets demonstrate its capability of efficient and accurate particle picking on flexible and imperfect lattices to obtain high-resolution 3D biological structures: virus-like particles, bacterial surface layers within cellular lamellae, and membranes decorated with nuclear egress protein complexes. These results demonstrate TomoNet's potential for broad applications to various cryoET projects targeting high-resolution in situ structures.

Enhancing Beat-to-Beat Analysis of Heart Signals With Respiration Harmonics Reduction Through Demodulation and Template Matching

Enhancing Beat-to-Beat Analysis of Heart Signals With Respiration Harmonics Reduction Through Demodulation and Template Matching

Localization of Defects on PCBs with Diverse Acquisitions

The Printed Circuit Board (PCB) accommodates various Integrated Circuit (IC) components arranged in a specific layout of bond pads, lines, and tracks. Throughout the manufacturing process, irregularities or defects often occur during drilling, etching, stripping, and other stages, impacting the performance and functionality of the circuit board. Many of these defects are related to soldering pads and copper balance, identifying them through manual inspection is time-consuming and error-prone. This necessitates the use of Automated Optical Inspection (AOI). Practices like template matching often require two identical PCBs, which are compared using mathematical algorithms to detect differences. However, they are not resilient to viewpoint variations and non-rigid deformations. The current inspection process primarily focuses on rectifying PCB images captured with tilts ranging from 0 to 84 using homography principles. This correction process operates within a maximum run time of 7.96 s. The adjusted images then undergo analysis via a pattern-matching unit, where the system receives images of the same PCB pattern, each exhibiting different defects. Structural information mapping is performed using various spatial-domain feature-based matching algorithms. When evaluated using SSI and MSE metrics, the model achieved high matching percentages of 99.67%, 99.75%, and 99.30%, and low error rates of 0.343%, 0.358%, and 0.721% for three different types of PCB designs considered. Additionally, the model excels in precisely identifying the location of defects in the PCB images without using bounding boxes, in accordance with the description of the co-images through a segmentation approach. Overall, the proposed system effectively corrects skew, accurately detects abnormalities and outperforms traditional assessment systems.

Design and Development a Software for Wear Measurement of Control Rod Guide Card Based on Machine Vision

The control rod guide tube (CRGT) is used to protect the rod cluster control assembly (RCCA) from bending while providing guidance for the up and down movement of the RCCA. This ensures that the dropping time of the RCCA meets nuclear safety requirements and stops nuclear reactions within the specified time frame. During the up and down movement of the RCCA, the control rod guide cards (CRGC) may experience axial and vibration wear. If the wear is severe, the CRGC may lose its guiding function, resulting in serious problems such as delayed dropping time. In this paper, a machine vision method is proposed to measure the dimensions of the CRGC. Edge extraction, image rectification, and template matching are used to realize autonomous measurement of the CRGC based on its features. The developed measurement software has been used 5 times in domestic nuclear power plant overhauls and has detected 15 CRGTs that were close to the wear limit. This provided data support for the replacement of the CRGTs and effectively improved the quality of nuclear power operation.

BESIII track reconstruction algorithm based on machine learning

Track reconstruction is one of the most important and challenging tasks in the offline data processing of collider experiments. For the BESIII detector working in the tau-charm energy region, plenty of efforts were made previously to improve the tracking performance with traditional methods, such as template matching and Hough transform etc. However, for difficult tracking tasks, such as the tracking of low momentum tracks, tracks from secondary vertices and tracks with high noise level, there is still large room for improvement. In this contribution, we demonstrate a novel tracking algorithm based on machine learning method. In this method, a hit pattern map representing the connectivity between drift cells is established using an enormous MC sample, based on which we design an optimal method of graph construction, then an edgeclassifying Graph Neural Network is trained to distinguish the hit-on-track from noise hits. Finally, a clustering method based on DBSCAN and RANSAC is developed to cluster hits from multiple tracks. Track fitting algorithm based on GENFIT2 is also studied to obtain the track parameters, where deterministic annealing filter are implemented to deal with ambiguities and potential noises. The preliminary results on BESIII MC sample presents promising performance, showing potential to apply this method to other trackers based on drift chamber as well, such as the CEPC and STCF detectors under pre-study.

Characterization of Magnetic Labyrinthine Structures Through Junctions and Terminals Detection Using Template Matching and CNN

Characterization of Magnetic Labyrinthine Structures Through Junctions and Terminals Detection Using Template Matching and CNN

Road Traffic Monitoring from Aerial Images Using Template Matching and Invariant Features

Road Traffic Monitoring from Aerial Images Using Template Matching and Invariant Features