Template Matching Research Articles

A Space-ground Joint Target Detection System Based on Artificial Intelligence

Due to the low efficiency and poor adaptability of template matching in target detection, this paper represents a space-ground joint detection system based on artificial intelligence, which takes advantage of the high computation power of the ground system to train neural networks and utilize the measurement and control channel to update and synchronize the on-orbit system. Based on YOLOv5, an oriented bounding box is used for labeling and training, the angle is considered as a class and the loss for the angle is added to the code. After training the network with the ground system, the weight will be sent to the satellite. This process would repeat once there are new training data that worse the update. Based on the new routine for target detection, the accuracy and robustness are significantly higher than before.

Triplet-Net Classification of Contiguous Stem Cell Microscopy Images.

Cellular microscopy imaging is a common form of data acquisition for biological experimentation. Observation of gray-level morphological features allows for the inference of useful biological information such as cellular health and growth status. Cellular colonies can contain multiple cell types, making colony level classification very difficult. Additionally, cell types growing in a hierarchical, downstream fashion, can often look visually similar, although biologically distinct. In this paper, it is determined empirically that traditional deep Convolutional Neural Networks (CNN) and classical object recognition techniques are not sufficient to distinguish between these subtle visual differences, resulting in misclassifications. Instead, Triplet-net CNN learning is employed in a hierarchical classification scheme to improve the ability of the model to discern distinct, fine-grain features of two commonly confused morphological image-patch classes, namely Dense and Spread colonies. The Triplet-net method improves classification accuracy over a four-class deep neural network by ∼ 3 %, a value that was determined to be statistically significant, as well as existing state-of-the-art image patch classification approaches and standard template matching. These findings allow for the accurate classification of multi-class cell colonies with contiguous boundaries, and increased reliability and efficiency of automated, high-throughput experimental quantification using non-invasive microscopy.

A Specific Earthquake Processing Workflow for Studying Long‐Lived, Explosive Volcanic Eruptions With Application to the 2008 Okmok Volcano, Alaska, Eruption

AbstractBy providing unrivaled resolution in both time and space, volcano seismicity helps to chronicle and interpret eruptions. Standard earthquake detection methods are often insufficient as the eruption itself produces continuous seismic waves that obscure earthquake signals. We address this problem by developing an earthquake processing workflow specific to a high‐noise volcanic environment and applying it to the explosive 2008 Okmok Volcano eruption. This process includes applying single‐channel template matching combined with machine‐learning and fingerprint‐based techniques to expand the existing earthquake catalog of the eruption. We detected an order of magnitude more earthquakes, then located, relocated, determined locally calibrated magnitudes, and classified the events in the enhanced catalog. This new high‐resolution earthquake catalog increases the number of observations by about a factor of 10 and enables the detailed spatiotemporal seismic analysis during a large eruption.

Sistem Pembayaran Parkir Non-Tunai Berbasis Mikrokontroler dengan Metode Template Matching

Parking is an activity to stop or store vehicles in a place that has been provided in public places that have a parking system. The parking system is a system that is applied to a parking environment. Several parking systems developed are manual parking systems, computer-based parking systems, semi-automatic parking systems and RFID-based parking systems. Indonesia has developed a lot of computer-based parking systems, but many areas still use manual parking systems. So that there are still many vehicles parked carelessly, security is not guaranteed and the payment system has not been accumulated based on the length of parking time. This paper describes the development of a parking system based on the template matching method by developing non-cash payments based on the length of parking time. Researchers use a webcam to detect vehicles that will enter the parking area. The captured images are processed in a database to store vehicle entry time information. Researchers use qr codes as a substitute for admission tickets. When leaving the parking area, a QR code will be scanned on the provided application, then users can make payments through the application. This system can be carried out independently by the user so that it does not require full 24-hour supervision.

Printing Defect Detection Based on Scale-Adaptive Template Matching and Image Alignment.

Printing defects are extremely common in the manufacturing industry. Although some studies have been conducted to detect printing defects, the stability and practicality of the printing defect detection has received relatively little attention. Currently, printing defect detection is susceptible to external environmental interference such as illuminance and noise, which leads to poor detection rates and poor practicality. This research develops a printing defect detection method based on scale-adaptive template matching and image alignment. Firstly, the research introduces a convolutional neural network (CNN) to adaptively extract deep feature vectors from templates and target images at a low-resolution version. Then, a feature map cross-correlation (FMCC) matching metric is proposed to measure the similarity of the feature map between the templates and target images, and the matching position is achieved by a proposed location refinement method. Finally, the matching image and the template are both sent to the image alignment module, so as to detect printing defects. The experimental results show that the accuracy of the proposed method reaches 93.62%, which can quickly and accurately find the location of the defect. Simultaneously, it is also proven that our method achieves state-of-the-art defect detection performance with strong real-time detection and anti-interference capabilities.

Template matching based on convolution neural network for UAV visual localization

Currently, unmanned aerial vehicle (UAVs) are used in many fields, among which UAV localization is the key to UAV autonomous flight capability, and how to locate UAVs in the absence of GNSS signals is a difficult task. In recent years, deep learning has developed rapidly, among which convolutional neural networks are widely used in visual images. In this paper, we apply the convolutional neural network DenseNet to the task of matching between UAV images and satellite remote sensing images for visual localization of UAVs. We make some improvements to address the problems in practice. We propose a quality-aware template matching method based on adaptive adjustment of convolutional feature weights to enhance the feature extraction capability of the model, and introduce a fusion mechanism of multi-scale feature layers. The feature maps of UAV images and satellite images are quality scored so as to locate the position of UAV in satellite images. Qualitative and quantitative experiments are conducted, and the results show the effectiveness and superiority of the method.

Wafer Surface Defect Detection Based on Background Subtraction and Faster R-CNN.

Concerning the problem that wafer surface defects are easily confused with the background and are difficult to detect, a new detection method for wafer surface defects based on background subtraction and Faster R-CNN is proposed. First, an improved spectral analysis method is proposed to measure the period of the image, and the substructure image can then be obtained on the basis of the period. Then, a local template matching method is adopted to position the substructure image, thereby reconstructing the background image. Then, the interference of the background can be eliminated by an image difference operation. Finally, the difference image is input into an improved Faster R-CNN network for detection. The proposed method has been validated on a self-developed wafer dataset and compared with other detectors. The experimental results show that compared with the original Faster R-CNN, the proposed method increases the mAP effectively by 5.2%, which can meet the requirements of intelligent manufacturing and high detection accuracy.

A New Instrument Monitoring Method Based on Few-Shot Learning

As an important part of the industrialization process, fully automated instrument monitoring and identification are experiencing an increasingly wide range of applications in industrial production, autonomous driving, and medical experimentation. However, digital instruments usually have multi-digit features, meaning that the numeric information on the screen is usually a multi-digit number greater than 10. Therefore, the accuracy of recognition with traditional algorithms such as threshold segmentation and template matching is low, and thus instrument monitoring still relies heavily on human labor at present. However, manual monitoring is costly and not suitable for risky experimental environments such as those involving radiation and contamination. The development of deep neural networks has opened up new possibilities for fully automated instrument monitoring; however, neural networks generally require large training datasets, costly data collection, and annotation. To solve the above problems, this paper proposes a new instrument monitoring method based on few-shot learning (FLIMM). FLIMM improves the average accuracy (ACC) of the model to 99% with only 16 original images via effective data augmentation method. Meanwhile, due to the controllability of simulated image generation, FLIMM can automatically generate annotation information for simulated numbers, which greatly reduces the cost of data collection and annotation.

이벤트 센서를 활용한 운전 중 장비 상태 감시 가능성 고찰

Vibration-based condition monitoring technology is regarded as one of the most effective maintenance technologies. Many studies aimed at developing a vision sensor-based condition monitoring system using image processing technologies are being conducted. Event sensors such as dynamic vision sensor have a different operating principle and have many advantages compared to the traditional frame-based sensors; however, for event sensor-based vibration measurement and analysis, the formalized methodology does not exist as an image processing technology. In this study, optical flow and template matching methods were used as tracking methods for event sensor-based condition monitoring. The vibration measurement performance of the representative methods was examined through experiments using a simulator with vertical and horizontal movements. Furthermore, the possibility of extracting changes in the vibration signals according to the state of simulator was investigated.

Automated assessment of human engineered heart tissues using deep learning and template matching for segmentation and tracking.

The high rate of drug withdrawal from the market due to cardiovascular toxicity or lack of efficacy, the economic burden, and extremely long time before a compound reaches the market, have increased the relevance of human in vitro models like human (patient-derived) pluripotent stem cell (hPSC)-derived engineered heart tissues (EHTs) for the evaluation of the efficacy and toxicity of compounds at the early phase in the drug development pipeline. Consequently, the EHT contractile properties are highly relevant parameters for the analysis of cardiotoxicity, disease phenotype, and longitudinal measurements of cardiac function over time. In this study, we developed and validated the software HAARTA (Highly Accurate, Automatic and Robust Tracking Algorithm), which automatically analyzes contractile properties of EHTs by segmenting and tracking brightfield videos, using deep learning and template matching with sub-pixel precision. We demonstrate the robustness, accuracy, and computational efficiency of the software by comparing it to the state-of-the-art method (MUSCLEMOTION), and by testing it with a data set of EHTs from three different hPSC lines. HAARTA will facilitate standardized analysis of contractile properties of EHTs, which will be beneficial for in vitro drug screening and longitudinal measurements of cardiac function.

A Robust Indoor Localization Method Based on DAT-SLAM and Template Matching Visual Odometry

Robust positioning is a central issue in robots. Due to the complex indoor environment, visual simultaneous localization and mapping (VSLAM) is susceptible to light and some scenes contain few features. The robot cannot obtain an accurate position. To solve these problems, this work first proposes dynamic adaptive threshold simultaneous localization and mapping (DAT-SLAM), a method for extracting feature points based on a dynamic adaptive threshold. The method is stable for feature extraction under illumination transformation. Subsequently, template matching visual odometry (VO) is introduced and combined with DAT-SLAM to form a joint indoor localization framework. Experiments on the datasets show that DAT-SLAM has a better performance than oriented fast and rotated brief SLAM2 (ORB-SLAM2). The mean positioning accuracy is improved by 11.74%. Experiments in real scenes show that the joint localization framework can achieve continuous localization in scenes with sparse features.

Research on Identification and Location of Charging Ports of Multiple Electric Vehicles Based on SFLDLC-CBAM-YOLOV7-Tinp-CTMA

With the gradual maturity of autonomous driving and automatic parking technology, electric vehicle charging is moving towards automation. The charging port (CP) location is an important basis for realizing automatic charging. Existing CP identification algorithms are only suitable for a single vehicle model with poor universality. Therefore, this paper proposes a set of methods that can identify the CPs of various vehicle types. The recognition process is divided into a rough positioning stage (RPS) and a precise positioning stage (PPS). In this study, the data sets corresponding to four types of vehicle CPs under different environments are established. In the RPS, the characteristic information of the CP is obtained based on the combination of convolutional block attention module (CBAM) and YOLOV7-tinp, and its position information is calculated using the similar projection relationship. For the PPS, this paper proposes a data enhancement method based on similar feature location to determine the label category (SFLDLC). The CBAM-YOLOV7-tinp is used to identify the feature location information, and the cluster template matching algorithm (CTMA) is used to obtain the accurate feature location and tag type, and the EPnP algorithm is used to calculate the location and posture (LP) information. The results of the LP solution are used to provide the position coordinates of the CP relative to the robot base. Finally, the AUBO-i10 robot is used to complete the experimental test. The corresponding results show that the average positioning errors (x, y, z, rx, ry, and rz) of the CP are 0.64 mm, 0.88 mm, 1.24 mm, 1.19 degrees, 1.00 degrees, and 0.57 degrees, respectively, and the integrated insertion success rate is 94.25%. Therefore, the algorithm proposed in this paper can efficiently and accurately identify and locate various types of CP and meet the actual plugging requirements.

Event-based micro vibration measurement using phase correlation template matching with event filter optimization

Condition monitoring of mechanical systems based on vibration is one of the most effective and useful techniques for predictive maintenance. With advancements in image processing and artificial intelligence technologies, vision camera-based condition monitoring has come into the spotlight. An event camera, also called a dynamic vision sensor, operates on a principle different from that of traditional frame-based cameras. However, there is still no standardized method for event camera-based vibration measurement. This study proposes an event filter-based phase correlation template match (EF-PCTM) method, including its optimal design procedure, to measure micro-vibrations using an event camera. In this study, event filter is designed using an infinite impulse response filter and genetic algorithm, and a cost function is defined to improve the performance of EF-PCTM. The performance of the proposed method was verified by two experiments using a rotor system simulator: i) extraction of micro-vibration and ii) fault condition monitoring. The proposed image processing procedure using EF-PCTM makes it possible to measure micro-vibrations, which had been challenging to measure in the past, and calculate the operational deflection shape.

Optical and SAR Image Registration Based on Multi-Scale Orientated Map of Phase Congruency

Optical and Synthetic Aperture Radar (SAR) images are highly complementary, and their registrations are a fundamental task for other remote sensing applications. Traditional feature-matching algorithms fail to solve the significant nonlinear radiation difference (NRD) caused by different sensors. To address this problem, a robust registration algorithm with the multi-scale orientated map of phase congruency (MSPCO) is proposed. First, a nonlinear diffusion scale space is established to obtain the scale invariance of feature points. Compared with the linear Gaussian scale space, the nonlinear diffusion scale space can better preserve the edge and texture information. Second, to ensure the quantity and repeatability of features, corner points and edge points are detected on the moment map of phase congruency, respectively, which is the foundation to the next feature matching. Third, the MSPCO descriptor is constructed via the orientation of phase congruency (PCO). PCO is highly robust to NRD, and the different scales of PCOs enhance the robustness of the descriptor. Finally, a feature-matching strategy based on an effective scale ratio is proposed, which reduces the number of comparisons among features and improves computational efficiency. The experimental results show that the proposed method is better than the existing feature-based methods in terms of the number of correct matches and registration accuracy. The registration accuracy is only inferior to that of the most advanced template matching method, and the accuracy difference is within 0.3 pixels, which fully demonstrates the robustness and accuracy of the proposed method in optical and SAR image registration.

Mushroom Detection and Three Dimensional Pose Estimation from Multi-View Point Clouds.

Agricultural robotics is an up and coming field which deals with the development of robotic systems able to tackle a multitude of agricultural tasks efficiently. The case of interest, in this work, is mushroom collection in industrial mushroom farms. Developing such a robot, able to select and out-root a mushroom, requires delicate actions that can only be conducted if a well-performing perception module exists. Specifically, one should accurately detect the 3D pose of a mushroom in order to facilitate the smooth operation of the robotic system. In this work, we develop a vision module for 3D pose estimation of mushrooms from multi-view point clouds using multiple RealSense active-stereo cameras. The main challenge is the lack of annotation data, since 3D annotation is practically infeasible on a large scale. To address this, we developed a novel pipeline for mushroom instance segmentation and template matching, where a 3D model of a mushroom is the only data available. We evaluated, quantitatively, our approach over a synthetic dataset of mushroom scenes, and we, further, validated, qualitatively, the effectiveness of our method over a set of real data, collected by different vision settings.

Updating Local Anisotropies with Template Matching During Geostatistical Seismic Inversion

Iterative geostatistical seismic inversion methods are widely used to predict petro-elastic rock properties from seismic reflection data. When the model perturbation technique uses two-point geostatistics, these methods struggle to reproduce complex and nonstationary geological environments such as faults, folds and highly variable depositional systems. These limitations are often due to the use of a global variogram model to express the expected spatial continuity pattern of the property of interest. In complex geological environments a global variogram model might be unable to detect local heterogeneities and rapid variations of lithology, and result in nonrealistic geological models. Local heterogeneities might be predicted from the data using seismic attribute analysis, which can be imposed during geostatistical seismic inversion as local anisotropy models. In these approaches, the information about the local spatial continuity patterns is fixed and will guide and condition the entire inversion procedure, which can lead to errors and uncertainty in areas where this approach is not appropriate due to high local uncertainty of geological features, given the poor signal-to-noise ratio of the data or the presence of important geological features below the seismic resolution. This work proposes an iterative geostatistical seismic inversion method which iteratively updates the local spatial continuity models based on the trace-by-trace misfit between observed and predicted seismic data. The update of the local spatial continuity models aims at surpassing the limitations of the seismic inversion methods that use a fixed a priori variogram model. The method is successfully illustrated in a challenging two-dimensional synthetic data set and in a real case application. The results demonstrate the benefit of updating iteratively the imposed local spatial continuity patterns based on the data misfit. The inverted models are capable of better predicting the location of faults and reproducing the continuity of sinuous channels.

Sign Language Recognition and Response via Virtual Reality

The main aim of this project is to provide a voice to voiceless people with the help of Python and Unity prototype systems. Aforesaid project work focuses on the problem of gesture recognition in real time that sign language is used by the community of deaf people. The problem addressed is based on Digital image processing using color segmentation, Skin detection, Image Segmentation, Image Filtering, and Template Matching techniques. This system recognizes gestures of ISL(Indian Sign Language) including the alphabet and a subset of its words.

Hybrid framework for single-pointer meter identification.

Automated identification of single-pointer meter identification in substations is widely used in the construction of digital substations and it must accurately identify the value of the pointer meter. Current single-pointer meter identification methods are not universally applicable and can only identify one type of meter. In this study, we present a hybrid framework for single-pointer meter identification. First, the input image of the single-pointer meter is modeled to gain a priori knowledge, including the template image, dial position information, the pointer template image, and scale value positions. Based on a convolutional neural network to generate the input image and the template image feature points, image alignment is then applied through a feature point match to mitigate slight changes in the camera angle. Next, a pixel loss-free method of arbitrary point image rotation correction is presented for rotation template matching. Finally, by rotating the input gray mask image of the dial and matching it to the pointer template to get the optimal rotation angle, the meter value is calculated. The experimental findings demonstrate the method's effectiveness in identifying nine different types of single-pointer meters in substations with various ambient illuminations. This study provides a feasible reference for substations to identify the value of different types of single-pointer meters.

An improved BECT spike detection method with functional brain network features based on PLV.

Children with benign childhood epilepsy with centro-temporal spikes (BECT) have spikes, sharps, and composite waves on their electroencephalogram (EEG). It is necessary to detect spikes to diagnose BECT clinically. The template matching method can identify spikes effectively. However, due to the individual specificity, finding representative templates to detect spikes in actual applications is often challenging. This paper proposes a spike detection method using functional brain networks based on phase locking value (FBN-PLV) and deep learning. To obtain high detection effect, this method uses a specific template matching method and the 'peak-to-peak' phenomenon of montages to obtain a set of candidate spikes. With the set of candidate spikes, functional brain networks (FBN) are constructed based on phase locking value (PLV) to extract the features of the network structure during spike discharge with phase synchronization. Finally, the time domain features of the candidate spikes and the structural features of the FBN-PLV are input into the artificial neural network (ANN) to identify the spikes. Based on FBN-PLV and ANN, the EEG data sets of four BECT cases from the Children's Hospital, Zhejiang University School of Medicine are tested with the AC of 97.6%, SE of 98.3%, and SP 96.8%.

Large Displacement Detection Using Improved Lucas–Kanade Optical Flow

Displacement is critical when it comes to the evaluation of civil structures. Large displacement can be dangerous. There are many methods that can be used to monitor structural displacements, but every method has its benefits and limitations. Lucas-Kanade (LK) optical flow is recognized as a superior computer vision displacement tracking method, but it only applies to small displacement monitoring. An upgraded LK optical flow method is developed in this study and used to detect large displacement motions. One motion controlled by a multiple purpose testing system (MTS) and a free-falling experiment were designed to verify the developed method. The results provided by the upgraded LK optical flow method showed 97 percent accuracy when compared with the movement of the MTS piston. In order to capture the free-falling large displacement, the pyramid and warp optical flow methods are included in the upgraded LK optical flow method and compared with the results of template matching. The warping algorithm with the second derivative Sobel operator provides accurate displacements with 96% average accuracy.