Template Matching Process Research Articles

Application of the polyhedral template matching method for characterization of 2D atomic resolution electron microscopy images

High-throughput image segmentation of atomic resolution electron microscopy data poses an ongoing challenge for materials characterization. In this paper, we investigate the application of the polyhedral template matching (PTM) method, a technique widely employed for visualizing three-dimensional (3D) atomistic simulations, to the analysis of two-dimensional (2D) atomic resolution electron microscopy images. This technique is complementary with other atomic resolution data reduction techniques, such as the centrosymmetry parameter, that use the measured atomic peak positions as the starting input. Furthermore, since the template matching process also gives a measure of the local rotation, the method can be used to segment images based on local orientation. We begin by presenting a 2D implementation of the PTM method, suitable for atomic resolution images. We then demonstrate the technique's application to atomic resolution scanning transmission electron microscopy images from close-packed metals, providing examples of the analysis of twins and other grain boundaries in FCC gold and martensite phases in 304 L austenitic stainless steel. Finally, we discuss factors, such as positional errors in the image peak locations, that can affect the accuracy and sensitivity of the structural determinations.

NeuSort: an automatic adaptive spike sorting approach with neuromorphic models

Objective. Spike sorting, a critical step in neural data processing, aims to classify spiking events from single electrode recordings based on different waveforms. This study aims to develop a novel online spike sorter, NeuSort, using neuromorphic models, with the ability to adaptively adjust to changes in neural signals, including waveform deformations and the appearance of new neurons. Approach. NeuSort leverages a neuromorphic model to emulate template-matching processes. This model incorporates plasticity learning mechanisms inspired by biological neural systems, facilitating real-time adjustments to online parameters. Results. Experimental findings demonstrate NeuSort’s ability to track neuron activities amidst waveform deformations and identify new neurons in real-time. NeuSort excels in handling non-stationary neural signals, significantly enhancing its applicability for long-term spike sorting tasks. Moreover, its implementation on neuromorphic chips guarantees ultra-low energy consumption during computation. Significance. NeuSort caters to the demand for real-time spike sorting in brain-machine interfaces through a neuromorphic approach. Its unsupervised, automated spike sorting process makes it a plug-and-play solution for online spike sorting.

Registration of Large Optical and SAR Images with Non-Flat Terrain by Investigating Reliable Sparse Correspondences

Optical and SAR image registration is the primary procedure to exploit the complementary information from the two different image modal types. Although extensive research has been conducted to narrow down the vast radiometric and geometric gaps so as to extract homogeneous characters for feature point matching, few works have considered the registration issue for non-flat terrains, which will bring in more difficulties for not only sparse feature point matching but also outlier removal and geometric relationship estimation. This article addresses these issues with a novel and effective optical-SAR image registration framework. Firstly, sparse feature points are detected based on the phase congruency moment map of the textureless SAR image (SAR-PC-Moment), which helps to identify salient local regions. Then a template matching process using very large local image patches is conducted, which increases the matching accuracy by a significant margin. Secondly, a mutual verification-based initial outlier removal method is proposed, which takes advantage of the different mechanisms of sparse and dense matching and requires no geometric consistency assumption within the inliers. These two procedures will produce a putative correspondence feature point (CP) set with a low outlier ratio and high reliability. In the third step, the putative CPs are used to segment the large input image of non-flat terrain into dozens of locally flat areas using a recursive random sample consensus (RANSAC) method, with each locally flat area co-registered using an affine transformation. As for the mountainous areas with sharp elevation variations, anchor CPs are first identified, and then optical flow-based pixelwise dense matching is conducted. In the experimental section, ablation studies using four precisely co-registered optical-SAR image pairs of flat terrain quantitatively verify the effectiveness of the proposed SAR-PC-Moment-based feature point detector, big template matching strategy, and mutual verification-based outlier removal method. Registration results on four 1 m-resolution non-flat image pairs prove that the proposed framework is able to produce robust and quite accurate registration results.

A fast matching method for skip displacement recognition of hoisting system

Determining if there is any coal left in the coal mine skip unloading is a challenging problem. We propose a new object tracking method to provide new ideas to solve the problem of whether there is residual coal in the skip unloading. In this study, a fast vision-based measurement framework about new diamond search normalized cross-correlation (NDS-NCC), including a movement prediction part and a template tracking part, is used. Firstly, a diamond search method with fast adaptive coarse localization is proposed to overcome the time-consuming problem of NCC template matching process. Secondly, we incorporate a template updating strategies that allows targets to be tracked consistently. The performance of the NDS-NCC algorithm was tested on the bench and at the coal mine site for balancing cylinders and wire rope tracking. The results show that the NDS-NCC algorithm has better efficiency (7 ms/frame) and accuracy (0.4512 px) in the coal mine field application.

Application of Enhanced Optimal-Detection of Time–Frequency Domain Reflectometry on HTS Cable With High-Resolution

Time–frequency domain reflectometry (TFDR) has been used for sensing various types of cables including high temperature superconducting (HTS) cables. Recently, the optimal-detection of time–frequency domain reflectometry (OD-TFDR) has been proposed and has better detection and localization performance than conventional TFDR. Additionally, OD-TFDR has provided a threshold that can distinguish detected signals from Gaussian noise. However, OD-TFDR does not provide a criterion for classifying the detected signals into fault signals and artifacts. Furthermore, the diagnosis result of OD-TFDR is not intuitive because an additional tool is needed to locate each fault. Thus, this paper proposes a new diagnosis algorithm that can provides a criterion for distinguishing between artifacts and faults by considering the propagation direction of the incident signal in the join time–frequency domain while preserving the detection and localization performance of OD-TFDR. Through a template matching process of generalized time–frequency cross-correlation (GTFCC), the core algorithm of OD-TFDR, a modified–generalized time–frequency cross-correlation (M–GTFCC) function is derived. Next, a modified–time–frequency cross-correlation (M–TFCC) function is derived by slicing the M–GTFCC function considering the propagation direction of signal. Finally, M–TFCC function is proposed as a new intuitive diagnosis result with artifact-free and high-resolution. The performance of the proposed algorithm is tested via experimental setup for a real-world single phase HTS cable with an emulated local quench by heating element, and the efficacy of the proposed algorithm is verified based on three performances: fault detection, locating accuracy, and resolution.

A Motion-Aware Siamese Framework for Unmanned Aerial Vehicle Tracking

In recent years, visual tracking has been employed in all walks of life. The Siamese trackers formulate the tracking problem as a template-matching process, and most of them can meet the real-time requirements, making them more suitable for UAV tracking. Because existing trackers can only use the first frame of a video sequence as a reference, the appearance of the tracked target will change when an occlusion, fast motion, or similar target appears, resulting in tracking drift. It is difficult to recover the tracking process once the drift phenomenon occurs. Therefore, we propose a motion-aware Siamese framework to assist Siamese trackers in detecting tracking drift over time. The base tracker first outputs the original tracking results, after which the drift detection module determines whether or not tracking drift occurs. Finally, the corresponding tracking recovery strategies are implemented. More stable and reliable tracking results can be obtained using the Kalman filter’s short-term prediction ability and more effective tracking recovery strategies to avoid tracking drift. We use the Siamese region proposal network (SiamRPN), a typical representative of an anchor-based algorithm, and Siamese classification and regression (SiamCAR), a typical representative of an anchor-free algorithm, as the base trackers to test the effectiveness of the proposed method. Experiments were carried out on three public datasets: UAV123, UAV20L, and UAVDT. The modified trackers (MaSiamRPN and MaSiamCAR) both outperformed the base tracker.

A Hybrid Method of Frequency and Spatial Domain Techniques for TFT-LCD Circuits Defect Detection

Defect detection is a crucial but challenging task in thin film transistor liquid crystal display (TFT-LCD) manufacturing. Existing vision-based methods focus on either spatial domain or frequency domain with unsatisfactory detection. In view of that, this paper proposes a hybrid template matching method by drawing benefits from both frequency and spatial domain techniques. Under proposal, frequency domain template matching method and saliency detector method are firstly adopted separately to obtain two candidate frequency components associated with defects. In template matching process, a novel selection criterion is taken to improve identifying components associated with local spatial anomaly. Subsequently, the inverse Fourier transform is applied on the intersection of the two candidates to reconstruct the defect regions. In final steps, image entropy in the spatial domain is employed to filter out false detection regions to improve accuracy. To meet industry’s real-time inspection requirement, image partition and multi-threading calculation techniques are introduced into the proposed methodology. Experimental results have shown practical viability of the proposed approach to increase the yield rate of panels through robust defect detection in TFT-LCD manufacturing.

Precise detection and localization of R-peaks from ECG signals.

Heart rate variability (HRV) is derived from the R-R interval, which depends on the precise localization of R-peaks within an electrocardiogram (ECG) signal. However, current algorithm assessment methods prioritize the R-peak detection's sensitivity rather than the precision of pinpointing the exact R-peak positions. As a result, it is of great value to develop an R-peak detection algorithm with high-precision R-peak localization. This paper introduces a novel R-peak localization algorithm that involves modifications to the well-established Pan-Tompkins (PT) algorithm. The algorithm was implemented as follows. First, the raw ECG signal $ X\left(i\right) $ was band-pass filtered (5-35 Hz) to obtain a preprocessed signal $ Y\left(i\right) $. Second, $ Y\left(i\right) $ was squared to enhance the QRS complex, followed by a 5 Hz low-pass filter to obtain the QRS envelope, which was transformed into a window signal $ W\left(i\right) $ by dynamic threshold with a minimum width of 200 ms to mark the QRS complex. Third, $ Y\left(i\right) $ was used to generate QRS template $ T\left(n\right) $ automatically, and then the R-peak was identified by a template matching process to find the maximum absolute value of all cross-correlation values between $ T\left(n\right) $ and $ Y\left(i\right) $. The proposed algorithm achieved a sensitivity (SE) of 99.78%, a positive prediction value (PPV) of 99.78% and data error rate (DER) of 0.44% in R-peak localization for the MIT-BIH Arrhythmia database. The annotated-detected error (ADE), which represents the error between the annotated R-peak location and the detected R-peak location, was 8.35 ms for the MIT-BIH Arrhythmia database. These results outperformed the results obtained using the classical Pan-Tompkins algorithm which yielded an SE of 98.87%, a PPV of 99.14%, a DER of 1.98% and an ADE of 21.65 ms for the MIT-BIH Arrhythmia database. It can be concluded that the algorithm can precisely detect the location of R-peaks and may have the potential to enhance clinical applications of HRV analysis.

Dynamic memory network with spatial-temporal feature fusion for visual tracking

Recently, single object tracking has demonstrated great success. However, due to various problems caused by fast motion, occlusion, and deformation, it is still intractable for traditional trackers to adapt to changes in object appearance. In this work, a dynamic memory network is proposed for visual tracking to handle the template matching process globally. More specifically, we first build a memory model, which consists of a memory feature fusion module and a memory bank. By the memory model, the network not only accepts the first frame as initial information but also memorizes the selective frames in the video sequence to provide rich time-domain information. Second, an innovative sampling strategy is adopted in the tracking process. By updating the template and guiding the selection of memory frames, our model can output higher-quality features. In addition, a spatial-channel fused attention module that effectively improves the representational capability and discriminability of the model is introduced. Our proposed method obtains compelling results on six challenging tracking benchmarks, including the OTB100, VOT2019, UAV123, NFS, GOT-10k, and LaSOT datasets. Extensive experiments demonstrate that our approach shows satisfactory robustness and leading application potential in real-time speed.

Real-Time Recognition Method of Video Basketball Technical Action Based on Target Detection Algorithm

Aiming at the problems of poor real-time recognition efficiency and low recognition accuracy of video basketball technical movements, a real-time recognition method of video basketball technical movements based on the target detection algorithm is proposed. This study firstly collects video basketball technical action images and detects their edge contour features. Through machine vision block template matching processing, the video basketball technology motion capture results are output. Then, according to the target probability of video basketball technical action detection, the basketball video sample set is estimated and constructed, the sequence of basketball video is obtained, and the basketball video target detection algorithm is designed. Finally, the similarity between the two vectors of video basketball technical action targets is measured to realize real-time recognition of video basketball technical actions. The experimental results show that the method in this study has high accuracy in extracting feature points of basketball video image decomposition, the real-time recognition time of video basketball technical action is up to 4s, and the recognition error of basketball rotation projection technology is small, which is close to the actual value.

Sistem Pencocokan Aksara Bali Menggunakan Template Matching Dan Komputasi Paralel

Balinese script is a traditional script used to write Balinese language and literature. Balinese script is also one of the richness of Balinese culture. However, Balinese script has a unique shape that is almost the same as each other, and some writings are only distinguished by one line stroke. This can complicate learning as well as in developing applications in this modern era. So because I did research in making Balinese script matching systems using template matching and parallel computation in order to find out what the value of the average dissimilarity of each Balinese script letter can be used as a reference in developing applications related to Balinese script.
 In this study I used the template matching method to find out the average error value of each Balinese accented letter contained in the bali simbar font. From the method, each pixel value of the Balinese script that has been processed in the binary process will match each pixel value to obtain an error value. The error value will be looked for the average and will be made a table to make it easier to find the largest MSE value or Balinese script that has a small similarity value. In the template matching process, parallel computation is also inserted as well as making comparisons between applications that use parallel computing and do not use parallel computing in order to know whether there is a difference in processing time or not.
 Based on the research conducted, the results obtained that the largest MSE value of 0.071354167 has a similarity of 93%. Where this shows that the similarity of the Balinese script to the others has similarities above 90% and the results of the data processing time for systems that use parallel computing are 4977 and systems that do not use parallel computing are 4789.
 
 Keywords: Template matching, parallel computing, Balinese script matching

Interstitial lung disease detection using template matching combined sparse coding and blended multi class support vector machine.

Interstitial lung disease (ILD), representing a collection of disorders, is considered to be the deadliest one, which increases the mortality rate of humans. In this paper, an automated scheme for detection and classification of ILD patterns is presented, which eliminates low inter-class feature variation and high intra-class feature variation in patterns, caused by translation and illumination effects. A novel and efficient feature extraction method named Template-Matching Combined Sparse Coding (TMCSC) is proposed, which extracts features invariant to translation and illumination effects, from defined regions of interest (ROI) within lung parenchyma. The translated image patch is compared with all possible templates of the image using template matching process. The corresponding sparse matrix for the set of translated image patches and their nearest template is obtained by minimizing the objective function of the similarity matrix of translated image patch and the template. A novel Blended-Multi Class Support Vector Machine (B-MCSVM) is designed for tackling high-intra class feature variation problems, which provides improved classification accuracy. Region of interests (ROIs) of five lung tissue patterns (healthy, emphysema, ground glass, micronodule, and fibrosis) selected from an internal multimedia database that contains high-resolution computed tomography (HRCT) image series are identified and utilized in this work. Performance of the proposed scheme outperforms most of the state-of-art multi-class classification algorithms.

Resting-state functional MRI for motor cortex mapping in childhood-onset focal epilepsy.

Task-based functional MRI (fMRI) mapping of the motor function prior to epilepsy surgery has limitations in children with epilepsy. We present a data-driven method to automatically delineate the motor cortex using task-free, resting-state fMRI (rsfMRI) data. We used whole-brain rsfMRI for independent component analysis (ICA). A template matching process with Discriminability Index-based Component Identification score was used for each participant to select and combine motor ICA components in their native brain space, resulting in a whole-brain ICA Motor Map (wIMM). We validated wIMM by comparing individual results with bilateral finger-tapping motor task fMRI activation, and evaluated its reproducibility in controls. Data from 64 patients and 12 controls were used to generate group wIMM maps. The hit rate between wIMM and motor task activation ranged from 60% to 79% across all participants. Sensitivity of wIMM for capturing the task activation peak was 87.5% among 32 patients and 100% in 12 controls with available motor task results. We also showed high similarity in repeated runs in controls. Our results show the sensitivity and reproducibility of an automated motor mapping method based on ICA analysis of rsfMRI in children with epilepsy. The ICA maps may provide different, but useful, information than task fMRI. Future studies will expand our method to mapping other brain functions, and may lead to a surgical planning tool for patients who cannot perform task fMRI and help predict their postsurgical function.

Research on Reconstruction of Basketball Training Action Trajectory Based on Improved K-Means Clustering Algorithm

The automatic capture and analysis of basketball game movements can guide basketball training and provide an effective method for improving the efficiency of basketball training. This paper introduces the research status of clustering methods in the field of trajectory data mining and reconstruction in detail. By analyzing the trajectory data under the constraints of the road network, the spatiotemporal characteristics of the existing trajectory clustering methods, and the deficiencies of the existing trajectory clustering methods, a new trajectory clustering method based on trajectory segmentation and spatiotemporal similarity measurement is implemented. A motion capture and reconstruction method for basketball training based on visual image K-means clustering algorithm is proposed. Multiresolution frame scanning technology is used to collect machine images of basketball training movements, and edge contour processing is performed on the collected high-resolution basketball training movement images. Feature detection uses the three-dimensional model reconstruction method to segment the basketball training action area and combines the irregular triangle network model to realize the machine vision block template matching processing of basketball training actions and capture the basketball training action in the Gaussian fuzzy affine space. In time and feature extraction, wavelet lifting technology is used to identify the ambiguity of basketball training movements, image enhancement technology is used to improve the resolution and adaptability of basketball training movement capture, and machine vision image processing methods are used to achieve basketball training movement capture optimization. The simulation results show that the method has better adaptability and higher feature recognition ability for basketball training motion capture and improves the feature extraction and adaptive capture reconstruction ability of basketball training motion.

Visual Object Tracking for Unmanned Aerial Vehicles Based on the Template-Driven Siamese Network

Visual object tracking for unmanned aerial vehicles (UAV) is widely used in many fields such as military reconnaissance, search and rescue work, film shooting, and so on. However, the performance of existing methods is still not very satisfactory due to some complex factors including viewpoint changing, background clutters and occlusion. The Siamese trackers, which offer a convenient way of formulating the visual tracking problem as a template matching process, have achieved success in recent visual tracking datasets. Unfortunately, these template match-based trackers cannot adapt well to frequent appearance change in UAV video datasets. To deal with this problem, this paper proposes a template-driven Siamese network (TDSiam), which consists of feature extraction subnetwork, feature fusion subnetwork and bounding box estimation subnetwork. Especially, a template library branch is proposed for the feature extraction subnetwork to adapt to the changeable appearance of the target. In addition, a feature aligned (FA) module is proposed as the core of feature fusion subnetwork, which can fuse information in the form of center alignment. More importantly, a method for occlusion detection is proposed to reduce the noise caused by occlusion. Experiments were conducted on two challenging benchmarks UAV123 and UAV20L, the results verified the more competitive performance of our proposed method compared to the existing algorithms.

Artificial Intelligence Techniques Based Learner Authentication in Cybersecurity Higher Education Institutions

Education 4.0 is being authorized more and more by the design of artificial intelligence (AI) techniques. Higher education institutions (HEI) have started to utilize Internet technologies to improve the quality of the service and boost knowledge. Due to the unavailability of information technology (IT) infrastructures, HEI is vulnerable to cyberattacks. Biometric authentication can be used to authenticate a person based on biological features such as face, fingerprint, iris, and so on. This study designs a novel search and rescue optimization with deep learning based learning authentication technique for cybersecurity in higher education institutions, named SRODL-LAC technique. The proposed SRODL-LAC technique aims to authenticate the learner/student in HEI using fingerprint biometrics. Besides, the SRODL-LAC technique designs a median filtering (MF) based preprocessing approach to improving the quality of the image. In addition, the Densely Connected Networks (DenseNet-77) model is applied for the extraction of features. Moreover, search and rescue optimization (SRO) algorithm with deep neural network (DNN) model is utilized for the classification process. Lastly, template matching process is done for fingerprint identification. A wide range of simulation analyses is carried out and the results are inspected under several aspects. The experimental results reported the effective performance of the SRODL-LAC technique over the other methodologies.

Contrastive Learning Ideas in Underwater Terrain Image Matching

The distinctive nature of the intrinsic features of underwater terrain images, along with their variability of intensity and texture due to the complex environment in which they are acquired, poses great challenges to manual feature-based template-matching methods. In this paper, we propose a novel data-driven framework for template matching of underwater terrain images. This combines the idea of contrastive learning with the template-matching process, allowing self-supervised end-to-end training without the need for additional data annotation. The resulting positive and negative sample comparisons improve the discriminatory ability of the model. Specifically, we achieve data enhancement by simulating grayscale and texture differences between images to improve the anti-interference capability of the model, and we propose a patch-based sample-extraction strategy to extract positive and negative samples. To further improve the discriminative ability for features among patches, we propose a feature-fusion module based on an attention mechanism. This improves the level of discrimination of the features to be matched by fusing the contextual features of patches. In addition, we solve the problem of matching multi-scale templates by introducing a masking mechanism. We performed detailed ablation experiments on the components in the framework to verify their effectiveness. We also compared the performance of our model with other state-of-the-art template-matching methods. The experimental results demonstrate that our proposed method has superior matching performance when compared to its competitors.

Automated Crystal Orientation Mapping by Precession Electron Diffraction-Assisted Four-Dimensional Scanning Transmission Electron Microscopy Using a Scintillator-Based CMOS Detector

The recent development of electron-sensitive and pixelated detectors has attracted the use of four-dimensional scanning transmission electron microscopy (4D-STEM). Here, we present a precession electron diffraction-assisted 4D-STEM technique for automated orientation mapping using diffraction spot patterns directly captured by an in-column scintillator-based complementary metal-oxide-semiconductor (CMOS) detector. We compare the results to a conventional approach, which utilizes a fluorescent screen filmed by an external charge charge-coupled device camera. The high-dynamic range and signal-to-noise characteristics of the detector greatly improve the image quality of the diffraction patterns, especially the visibility of diffraction spots at high scattering angles. In the orientation maps reconstructed via the template matching process, the CMOS data yield a significant reduction of false indexing and higher reliability compared to the conventional approach. The angular resolution of misorientation measurement could also be improved by masking reflections close to the direct beam. This is because the orientation sensitive, weak, and small diffraction spots at high scattering angles are more significant. The results show that fine details, such as nanograins, nanotwins, and sub-grain boundaries, can be resolved with a sub-degree angular resolution which is comparable to orientation mapping using Kikuchi diffraction patterns.

Real Time Vehicle Recognition System for Secure Environment

An automatic system based on “Digital Image Processing” technology is developed using MATLAB tool to recognize the vehicle by identifying the license plate. A database is linked to the MATLAB processor, which takes input as the detected number plate. Information about the owner is extracted and electronic format of the challan is generated if the number plate is in wrong format. Also in case of theft or crime, the required number plate is fed to the database system and if it is matched with the detected number plate, an alert is generated. The captured image is in RGB format which is converted into grey scale. Here theories and techniques used to identify the area and Edge detector operators is used to measure threshold values to get the dilated image. After that flood fill operation can be used to stuff the wholes to get a fine binary picture. The fundamental steps to identify the vehicle license plate and plate size, then the partitioned picture is amplified with gray scale only to get the license plate of vehicle. Afterwards the template matching process done to match the image from license plate and template’s picture, thus the number plate detected is then matched with one stored in the database system to get complete information of the vehicle owner. The first section in your paper.

Architecture Design for Feature Extraction and Template Matching in a Real-Time Iris Recognition System

Real-time support for an iris recognition algorithm is a considerable challenge for a portable system that is commonly used in the field. In this paper, an efficient parallel and pipeline architecture design for the feature extraction and template matching processes in the Ridge Energy Direction (RED) algorithm for iris recognition is presented. Several techniques used in the proposed architecture design to reduce the computational complexity while supporting a high performance capability include (i) a circle approximation method for the iris unwrapping process, (ii) a parallel design with an on-chip buffer for 2D convolution in the feature extraction process, and (iii) an approximation method for log2 and inverse-log2 conversion in the template matching process. Performance analysis shows that the proposed architecture achieves a speedup of 881 times compared to the conventional method. The proposed design can be integrated with an embedded microprocessor to realize a complete system-on-chip solution for a portable iris recognition system.