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Background and purposeMotion management is essential to reduce normal tissue exposure and maintain adequate tumor dose in lung stereotactic body radiation therapy (SBRT). Lung SBRT using an articulated robotic arm allows dynamic tracking during radiation dose delivery. Two stereoscopic X-ray tracking modes are available - fiducial-based and fiducial-free tracking. Although X-ray detection of implanted fiducials is robust, the implantation procedure is invasive and inapplicable to some patients and tumor locations. Fiducial-free tracking relies on tumor contrast, which challenges the existing tracking algorithms for small (e.g., 15 mm) and tumor locations (with/without thoracic anatomy overlapping).ResultsOn X-ray views that conventional methods failed to track the lung tumor, Deep Match achieved robust performance as evidenced by >80 % 3 mm-Hit (detection within 3 mm superior/inferior margin from ground truth) for 70 % of patients and

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Deep learning-based template matching in remote sensing has received increasing research attention. Existing anchor box-based and anchor-free methods often suffer from low template localization accuracy in the presence of multimodal, nonrigid deformation and occlusion. To address this problem, we transform the template matching task into a center-point localization task for the first time and propose an end-to-end template matching method based on a novel fully convolutional Siamese network. Furthermore, we propose an adaptive shrinkage cross-correlation scheme, which improves the precision of template localization and alleviates the impact of background clutter without adding any parameters. We also design a scheme that leverages keypoint information to assist in locating the template center, thereby enhancing the precision of template localization. We construct a multimodal template matching dataset to verify the performance of the method in dealing with differences in view, scale, rotation and occlusion in practical application scenarios. Extensive experiments on a public dataset, OTB, the proposed dataset, as well as a remote sensing dataset, SEN1-2, demonstrate that our method achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]

In this study, a high-speed template matching system is proposed for wafer-vision alignment. The proposed system is designed to rapidly locate markers in semiconductor equipment used for wafer-vision alignment. We optimized and implemented a template-matching algorithm for the high-speed processing of high-resolution wafer images. Owing to the simplicity of wafer markers, we removed unnecessary components in the algorithm and designed the system using a field-programmable gate array (FPGA) to implement high-speed processing. The hardware blocks were designed using the Xilinx ZCU104 board, and the pyramid and matching blocks were designed using programmable logic for accelerated operations. To validate the proposed system, we established a verification environment using stage equipment commonly used in industrial settings and reference-software-based validation frameworks. The output results from the FPGA were transmitted to the wafer-alignment controller for system verification. The proposed system reduced the data-processing time by approximately 30% and achieved a level of accuracy in detecting wafer markers that was comparable to that achieved by reference software, with minimal deviation. This system can be used to increase precision and productivity during semiconductor manufacturing processes. [ABSTRACT FROM AUTHOR]

Memory-guided motor shaping is necessary for sensorimotor learning. Vocal learning, such as speech development in human babies and song learning in bird juveniles, begins with the formation of an auditory template by hearing adult voices followed by vocally matching to the memorized template using auditory feedback. In zebra finches, the widely used songbird model system, only males develop individually unique stereotyped songs. The production of normal songs relies on auditory experience of tutor's songs (commonly their father's songs) during a critical period in development that consists of orchestrated auditory and sensorimotor phases. "Auditory templates" of tutor songs are thought to form in the brain to guide later vocal learning, while formation of "motor templates" of own song has been suggested to be necessary for the maintenance of stereotyped adult songs. Where these templates are formed in the brain and how they interact with other brain areas to guide song learning, presumably with template-matching error correction, remains to be clarified. Here, we review and discuss studies on auditory and motor templates in the avian brain. We suggest that distinct auditory and motor template systems exist that switch their functions during development. [ABSTRACT FROM AUTHOR]

The precise tie-points (TPs) on synthetic aperture radar (SAR) images are a critical cornerstone in the global digital elevation model (DEM) and digital ortho map (DOM) production process. While there are abundant studies on SAR TPs matching, improvement opportunities persist in large areas. The correspondences have pixel-level errors during geocoding, which result in misalignment between global products. Consequently, this paper proposed a robust method for SAR images TPs matching, which consists of three key steps: (1) interest point extraction based on the dynamic Harris area entropy (DHAE) grid; (2) adaptive determination of template size; (3) normalized cross correlation (NCC) template matching. DHAE is a regional texture information grid based on the SAR-Harris map, and it is achieved through dynamic block division. Generating the DHAE grid over SAR images enables the extraction of interest points that have regional feature representation and distribution uniformity. A variable-size matching template is adaptively determined based on DHAE to enhance template quality while maintaining computational efficiency. Subsequently, the NCC algorithm is employed to find subpixel-precise correspondences. The proposed method is applied on TPs matching in 57 Terra-SAR images, which cover a large geographical area. Furthermore, the overlapping area is partitioned into five segments according to different coverage types. The experimental results demonstrate that the proposed method outperforms other template matching methods. For all coverage types, the proposed method exhibits high-precision sub-pixel results that reach up to 38.64% in terms of the relative positioning error (RPE), particularly in texture-weak and large areas. [ABSTRACT FROM AUTHOR]

Copyright of Railway Standard Design is the property of Railway Standard Design Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The state of Kerala in India has seen multiple instances of intense cyclones in recent years, resulting in heavy flooding. One of the biggest challenges faced by rescuers is the accessibility to flooded areas and buildings during rescue operations. In such scenarios, unmanned aerial vehicles (UAVs) can deliver reliable aerial visual data to aid planning and operations during rescue. Object detectors based on deep learning methods provide an effective solution to automate the process of detecting relevant information from image/video data. These models are complex and resource-hungry, leading to severe speed constraints during field operations. The pixel displacement algorithm (PDA), a portable and effective technique, is developed in this work to speed up object detection models on devices with limited resources, such as edge devices. This method can be integrated with all object detection models to speed up the inference time. The proposed method is combined with multiple object detection models in this work to show its effectiveness. The YOLOv4 model combined with the proposed method outperformed the AP50 performance of the YOLOv4-tiny model by 6 % while maintaining the same processing time. This approach gave almost 10 × speed improvement to Jetson Nano at an accuracy cost of 3 % when compared to YOLOv4. Further, a model to predict maximum pixel shift with respect to frame skip is proposed using parameters such as the altitude and velocity of the UAV and the tilt of the camera. Accurate prediction of pixel shift leads to a reduced search area, leading to reduced inference time. The effectiveness of the proposed model was tested against annotated locations, and it was found that the method was able to predict the search area for each test video segment with a high degree of accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper proposes a method for quick recognition of jigsaw puzzles using a small number of samples, solely relying on image processing and template matching techniques. In the preliminary stage, this paper proposes a segmentation approach to divide the jigsaw puzzle into individual pieces with partial features, obtaining template data for each puzzle piece. Subsequently, scattered puzzle pieces are captured using a camera, and their contours are extracted to acquire puzzle image data. The puzzle images are then matched with the template data, employing polar coordinate transformation during the matching process to reduce computational time. The matching process also identifies the puzzle piece's number and orientation. Finally, the matching results are integrated with a robotic arm to complete the task of puzzle assembly. This paper not only outlines the generation of puzzle image data and template data through image processing but also elaborates on the determination of corresponding numbers and orientations during image matching. The feasibility of this method is validated through different types of puzzles, and its applicability to industrial parts is also examined for practicality. [ABSTRACT FROM AUTHOR]

Electrical wiring diagrams， which represent the positions of electrical equipment and describe their wiring and cabling methods， need to be converted into XML for smart grid system scheduling and management. This conversion process is known as vectorization. To address the inefficiencies and time-consuming nature of manual vectorization， this paper proposes a solution for vectorizing electrical wiring diagrams. First， the paper introduces an attention mechanism-enhanced YOLOv3 method for precise recognition and localization of graphic elements in electrical wiring diagrams. Next， it proposes a text region detection algorithm based on multi-scale feature extraction to locate electrical text annotation areas on the diagrams， and uses a CRNN model to recognize their content. Finally， the paper presents a vectorization strategy based on template matching， which analyzes the relationships between electrical elements on the diagrams at the graphic element group level. This method has been practically deployed and can efficiently and accurately complete the vectorization of electrical wiring diagrams， meeting the application needs of power systems.

Abstract The continuous development of high-speed Internet technology has made the application of robots increasingly widespread. Current robots and human–computer interaction systems mostly use rigid materials, such as metals and semiconductors, which have limitations in terms of deformability and flexibility. In addition, the biocompatibility and user comfort of these materials are also an issue. Therefore, research into new flexible biosensors is essential to improve the flexibility, comfort, and interactivity of these systems. This research will select polymer hydrogel as the electrode material of the sensor and polydimethylsiloxane as the base material of the sensor to design a resistance flexible biosensor to solve the poor flexibility. The research will use a template-matching method to verify the feasibility of gesture recognition of the flexible sensor. The remote control system of the robot finger is designed by a proportional-integral differential controller tuned by aradial basis function neural network. The feasibility of the research system is verified by simulation and scene experiments. The flexible sensor studied and prepared had a sensitivity of 0.7269, a tensile limit of 300%, and a thickness of 0.16 mm, showing good sensitivity and stability. The recognition accuracy of the sensor designed in the study was 92.8%, which was 8.1% higher than that of the data glove. Compared with traditional proportional-integral derivative (PID) controllers, the improved controller system error was within 10 to 3 rad, which had better adaptability and stability. Key information includes the design method of the flexible biosensor, its high sensitivity and stability under multiple stretches, and the proposal and validation of a new RBFNN–PID control model. These results showed that using this new sensor and control model significantly improved the control accuracy of mechanical fingers and the effect of gesture recognition. These results have important implications for the development of more advanced human–computer interaction systems. They not only improve the performance and reliability of the system, but also improve the user's interactive experience. These technologies are particularly promising in the fields of prosthetics for disabled people, advanced game controllers, and remotely controlled robots operating in hazardous environments. The research results are expected to lead to the development of advanced prosthetics, augmented reality devices, advanced game controllers, and automated robots. The main contribution of the research is to design a resistive flexible biosensor, which improves the traditional sensor's poor flexibility and large size and improves the sensor's ability to sense small changes. Future research may focus on further improving the sensor's long-term stability and performance under a variety of environmental conditions. In addition, commercializing these technologies and making them universal is also an important direction for the future.

Efficient multi-modal image fusion plays an important role in the non-destructive evaluation (NDE) of infrastructures, where an essential challenge is the precise visualizing of defects. While automatic defect detection represents a significant advancement, the determination of the precise location of both surface and subsurface defects simultaneously is crucial. Hence, visible and infrared data fusion strategies are essential for acquiring comprehensive and complementary information to detect defects across vast structures. This paper proposes an infrared and visible image registration method based on Euclidean evaluation together with a trade-off between key-point threshold and non-maximum suppression. Moreover, we employ a multi-modal fusion strategy to investigate the robustness of our image registration results. [ABSTRACT FROM AUTHOR]

This paper proposes a new method for recognizing, extracting, and processing Phase-Resolved Partial Discharge (PRPD) patterns from two-dimensional plots to identify specific defect types affecting electrical equipment without human intervention while retaining the principals that make PRPD analysis an effective diagnostic technique. The proposed method does not rely on training complex deep learning algorithms which demand substantial computational resources and extensive datasets that can pose significant hurdles for the application of on-line partial discharge monitoring. Instead, the developed Cosine Cluster Net (CCNet) model, which is an image processing pipeline, can extract and process patterns from any two-dimensional PRPD plot before employing the cosine similarity function to measure the likeness of the patterns to predefined templates of known defect types. The PRPD pattern recognition capabilities of the model were tested using several manually classified PRPD images available in the existing literature. The model consistently produced similarity scores that identified the same defect type as the one from the manual classification. The successful defect type reporting from the initial trials of the CCNet model together with the speed of the identification, which typically does not exceed four seconds, indicates potential for real-time applications. [ABSTRACT FROM AUTHOR]

In view of the problems such as slow processing speed and low accuracy of condition monitoring technology for electrical equipment at this stage, an online status monitoring method of electrical equipment based on the multi change point detection technology and template matching strategy is proposed. Based on the buffer model and the sliding window model, this method uses the TSTKS (Ternary Search Tree and Kolmogorov - Smirnov) algorithm to form the feature vector of the window dimension and the buffer dimension. The running state matching and status switching time positioning of the electrical equipment are realized by template matching of the two dimensions. Simulation experiments results of the household refrigerator show that the proposed method has the advantages of fast detection speed and high accuracy, and can provide a reference for the state monitoring field of electrical equipment. [ABSTRACT FROM AUTHOR]

The detection of specific biological macromolecules in cryogenic electron tomography data is frequently approached by applying cross‐correlation‐based 3D template matching. To reduce computational cost and noise, high binning is used to aggregate voxels before template matching. This remains a prevalent practice in both practical applications and methods development. Here, the relation between template size, shape and angular sampling is systematically evaluated to identify ribosomes in a ground‐truth annotated data set. It is shown that at the commonly used binning, a detailed subtomogram average, a sphere and a heart emoji result in near‐identical performance. These findings indicate that with current template‐matching practices macromolecules can only be detected with high precision if their shape and size are sufficiently different from the background. Using theoretical considerations, the experimental results are rationalized and it is discussed why primarily low‐frequency information remains at high binning and that template matching fails to be accurate because similarly shaped and sized macromolecules have similar low‐frequency spectra. These challenges are discussed and potential enhancements for future template‐matching methodologies are proposed. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aircraft type recognition has been researched deeply for plenty years on remote sensing and radar signal processing fields because of military needs. However, with the rapid development of UAV visualization technology in recent years, the advanced optical sensors on high-altitude drones are able to capture high-definition aircraft images of various aircraft flight postures, which brings aircraft type recognition into a brand-new research field. Different from remote sensing images, images with aircrafts in flight have larger intra-class difference caused by flight posture variation, which is 3D-view and more challenging for aircraft type recognition. Facing the new challenges, we propose a aircraft type recognition framework in 3D-view optical images. The framework consists of two stages: a coarse stage for aircraft contour segmentation and a fine stage for aircraft contour template matching. At the coarse stage, we propose a instance segmentation network CP-Deepsnake with a novel loss function CP loss which improves the accuracy of extracted aircraft contours by supervising the global distribution of contour points during contour deformation. At the fine stage, we utilize contour template matching to realize aircraft type recognition in 3D-view images. Based on IDSC contour feature descriptor, we propose a fast contour template matching approach with a new matching evaluation criterion and establish an aircraft contour template database for aircraft contour template matching. To train and test our aircraft type recognition framework, we build two datasets with 3D-view aircraft images of 10 aircraft types. Experiments show that our method achieves considerable recognition accuracy on the aircraft type testing dataset. Besides, we evaluate the proposed CP-Deepsnake network on two challenging instance segmentation public datasets SBD and KINS, where it compares favorably against state-of-the-art methods, which means our CP-Deepsnake network can be extended to more instance segmentation sences like road pedestrian detection and vehicle detection. [ABSTRACT FROM AUTHOR]

Tracking objects from aerial imagery is significant in numerous remote sensing-based applications, including environmental monitoring, security surveillance, and search & rescue. However, tracking specific targets in aerial images is still challenging due to target appearance variation and similar object distraction. To address these challenges, we propose a joint target and background temporal propagation approach for aerial tracking, dubbed JTBP. JTBP leverages the temporal coherence present in video sequences and consists of two modules: the target temporal propagation module and the background temporal propagation module. The former adjusts the template by mining target-specific information from the temporal domain. It utilizes a key–value mechanism to weigh the channels of the template based on target-specific features, allowing the template to adapt to variations in target appearance. The latter identifies background objects from the temporal domain and effectively distinguishes similar objects by leveraging the temporal coherence of objects in the background. On four benchmarks, the new tracker JTBP shows consistent improvements over baselines and achieves leading performance compared to advanced trackers. Notably, our approach outperforms the state-of-the-art method by 2.8 points in terms of success rate on UAV112Track_L dataset. The project is available at https://chnleixu.github.io/JTBP-Web/. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The work in this study proposes to deal with microscope images with slightly misaligned stages. A sliding template-based method for position-based whole slide microscopy (WSM) image stitching is proposed to find the translational properties of the consecutive images. The mechanical stability of the stage is one of the major issues, along with flat field disturbances that make the image stitching process difficult. This calls for further research in the field of image stitching. The proposed method employs a sliding template (a small image) to find the translational offset with the adjacent images, which is fast enough to avoid the complicated feature detection and matching algorithms to measure the distances between the features of all the images. The results of the proposed work show comparably accurate stitching while being more resistant to mechanical instability as well as faster than other methods. For slightly misaligned stages, position-based WSM image stitching using a sliding template can be a viable solution. [ABSTRACT FROM AUTHOR]

The efficient arrangement of patterns on a substrate surface is of paramount importance across diverse domains, including manufacturing, design, nanotechnology, and printed electronics. This paper introduces three distinct strategies for pattern arrangement within the realm of two-dimensional cutting and packing challenges. Firstly, for irregular patterns, we have developed a modified genetic algorithm that effectively determines the spatial relationships between patterns, facilitating subsequent packing processes. Secondly, we present the “MaxiRectPack” method designed for rectangular patterns. This method utilizes partitioning techniques and a variety of orientation schemes to maximize pattern packing efficiency. Lastly, we propose a technique based on normalized 2-D cross-correlation for patterns of diverse shapes, allowing precise placement while accounting for variations in substrate and pattern energy. These methodologies are adaptable, catering to irregular, rectangular, and diverse pattern shapes, offering versatile solutions for pattern placement in the context of two-dimensional cutting and packing. With a strong emphasis on resource conservation and spatial efficiency, this research equips practitioners and researchers with practical tools for optimizing material utilization and pattern arrangement on substrates.

Monocular Visual Odometry (MVO) is a fundamental element in autonomous navigation systems, providing vehicles/robots with the capability to estimate their positions by analyzing visual images from a single camera. This work delves into a pure appearance-based MVO algorithm that estimates the vehicle displacement and orientation between consecutive image frames alone, without using an Inertial Measurement Unit (IMU) sensor. The proposed method comprises four stages: ground spatial calibration, vehicle displacement, orientation estimation modules, and an actual vehicle heading estimation module. In the first stage, the image pixel coordinates are converted into world coordinates through ground spatial calibration. In the second stage, cross-correlation-based template matching is performed between two successive image frames and vehicle displacement is computed using the obtained world coordinates. Next, the orientation of the matched template is estimated along the ‘u’ and ‘v’ axis of the image. Subsequently, the actual vehicle heading is computed in the fourth stage with respect to the global coordinate system to estimate the vehicle pose. Experimental evaluations demonstrate the superior performance of the developed MVO algorithm compared to existing appearance-based methods that additionally utilize IMU to obtain orientation. When the vehicle is driven for a distance of 1406.35 meters, the average percentage distance error obtained is 1.41%, thereby highlighting the improved performance of the MVO algorithm in terms of higher accuracy and efficacy in real-world applications.

Abstract 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.

The most unexpected and fatal kind of cancer, melanoma, has been on the rise in its spread to different parts of the body. Early detection can significantly reduce its fatality rate. The primary stages of melanoma for its identification by the unaided eye are a difficult task that demands extensive training and understanding. Due to a lack of qualified dermatologists, a computerized and automated method is required to correctly detect melanoma. This study achieved this feat through a proposed system that can effectively detect and classify melanoma as benign or malignant. The process begins with image template matching by using normalized cross-correlation technique induction to mark the infected area of skin lesion as the region of interest (ROI) from ISIC datasets 2017, 2019, and 2020 dermoscopic images. Our novel model dynamically calculated number of clusters is assigned to the $k$ -means clustering algorithm, and ROI is extracted. Histogram equalization is applied to the output image for contrast enhancement. Hu Moment method is implemented for shape classification and part recognition from the segmented image. GLCM-based Haralick feature extractor is used in the proposed system to extract textural features generating the feature vector from the segmented skin lesion. It leads the classifier to identify skin lesions as cancerous or non-cancerous. Random Forest, Decision Tree, Support Vector Machine (SVM), Gaussian Naïve Bayes, Logistic Regression, and K-nearest neighbor (KNN) Classifiers are used for classification. The KNN, SVM, and Random Forest was successful in attaining the highest accuracy of 99.29%, 99.38%, and 99.46% on the given dermoscopic images datasets ISIC-2019, ISIC-2020 and ISIC-2017 respectively. The proposed method, the normalized cross correlation-based k-means clustering model, was found to be more robust and accurate than existing methods and incorporates much more feature information from the images.

Cardiac auscultation is an essential part of physical examination and plays a key role in the early diagnosis of many cardiovascular diseases. The analysis of phonocardiography (PCG) recordings is generally based on the recognition of the main heart sounds, i.e., S1 and S2, which is not a trivial task. This study proposes a method for an accurate recognition and localization of heart sounds in Forcecardiography (FCG) recordings. FCG is a novel technique able to measure subsonic vibrations and sounds via small force sensors placed onto a subject's thorax, allowing continuous cardio-respiratory monitoring. In this study, a template-matching technique based on normalized cross-correlation was used to automatically recognize heart sounds in FCG signals recorded from six healthy subjects at rest. Distinct templates were manually selected from each FCG recording and used to separately localize S1 and S2 sounds, as well as S1–S2 pairs. A simultaneously recorded electrocardiography (ECG) trace was used for performance evaluation. The results show that the template matching approach proved capable of separately classifying S1 and S2 sounds in more than 96% of all heartbeats. Linear regression, correlation, and Bland–Altman analyses showed that inter-beat intervals were estimated with high accuracy. Indeed, the estimation error was confined within 10 ms, with negligible impact on heart rate estimation. Heart rate variability (HRV) indices were also computed and turned out to be almost comparable with those obtained from ECG. The preliminary yet encouraging results of this study suggest that the template matching approach based on normalized cross-correlation allows very accurate heart sounds localization and inter-beat intervals estimation. [ABSTRACT FROM AUTHOR]

Urban visual localization is the process of determining the pose (position and attitude) of the imaging sensor (or platform) with the help of existing geo-referenced data. This task is critical and challenging for many applications, such as autonomous navigation, virtual and augmented reality, and robotics, due to the dynamic and complex nature of urban environments that may obstruct Global Navigation Satellite Systems (GNSS) signals. This paper proposes a block-wise matching strategy for urban visual localization by using geo-referenced Google Street View (GSV) panoramas as the database. To determine the pose of the monocular query images collected from a moving vehicle, neighboring GSVs should be found to establish the correspondence through image-wise and block-wise matching. First, each query image is semantically segmented and a template containing all permanent objects is generated. The template is then utilized in conjunction with a template matching approach to identify the corresponding patch from each GSV image within the database. Through the conversion of the query template and corresponding GSV patch into feature vectors, their image-wise similarity is computed pairwise. To ensure reliable matching, the query images are temporally grouped into query blocks, while the GSV images are spatially organized into GSV blocks. By using the previously computed image-wise similarities, we calculate a block-wise similarity for each query block with respect to every GSV block. A query block and its corresponding GSV blocks of top-ranked similarities are then input into a photogrammetric triangulation or structure from motion process to determine the pose of every image in the query block. A total of three datasets, consisting of two public ones and one newly collected on the Purdue campus, are utilized to demonstrate the performance of the proposed method. It is shown it can achieve a meter-level positioning accuracy and is robust to changes in acquisition conditions, such as image resolution, scene complexity, and the time of day. [ABSTRACT FROM AUTHOR]

We analyze nearest‐neighbor proximities of earthquakes in California based on the joint distribution (T, R) of rescaled time T and rescaled distance R between pairs of earthquakes (Zaliapin & Ben‐Zion, 2013a, https://doi.org/10.1002/jgrb.50179), using seismic catalogs from several regions and several catalogs for the San Jacinto Fault Zone (SJFZ). The study aims to identify informative modes in nearest‐neighbor diagrams beyond the general background and clustered modes, and to assess seismic catalogs derived by different methods. The results show that earthquake clusters with large and small‐to‐medium mainshocks have approximately diagonal and horizontal (T, R) distributions of the clustered mode, respectively, reflecting different triggering distances of mainshocks. Earthquakes in the creeping section of San Andreas Fault have a distinct "repeaters mode" characterized by very large rescaled times T and very small rescaled distances R, due to nearly identical locations of repeating events. Induced seismicity in the Geysers and Coso geothermal fields follow mostly the background mode, but with larger rescaled times T and smaller rescaled distances R compared to tectonic background seismicity. We also document differences in (T, R) distributions of catalogs constructed by different techniques (analyst‐picks, template‐matching and deep‐learning) for the SJFZ, and detect a mode with very large R and small T in the template‐matching and deep‐learning based catalogs. This mode may reflect dynamic triggering by passing waves and/or catalog artifacts. Plain Language Summary: Earthquake patterns in space and time provide important information about the properties of active faults, the surrounding crust, and different loading mechanisms. Nearest‐neighbor diagrams of earthquake proximities in a combined space‐time‐magnitude domain can be used to detect different types of seismicity patterns. Using such diagrams to analyze several earthquake catalogs in California, we examine characteristics of different sections of the San Andreas and San Jacinto faults along with geothermal areas and the entire southern California. The results highlight features of seismicity beyond the well‐known background and clustered modes of earthquakes, including a mode of repeating events in the creeping section of the San Andreas fault and differences between events in regular tectonic areas and geothermal fields. Analyses of different catalogs for the region around the San Jacinto fault also reveal differences related to the different techniques used to construct the catalogs. The results show that nearest‐neighbor diagrams can provide a simple and efficient way to identify different types of seismicity and assess the quality of earthquake catalogs. Key Points: Nearest‐neighbor diagrams provide an efficient way to identify different types of earthquake patternsRepeating events, large versus small‐moderate mainshocks, and induced seismicity have characteristic signaturesNearest‐neighbor diagrams help to evaluate features in catalogs constructed by different methods [ABSTRACT FROM AUTHOR]

Among its main benefits, telemonitoring enables personalized management of chronic diseases by means of biomarkers extracted from signals. In these applications, a thorough quality assessment is required to ensure the reliability of the monitored parameters. Motion artifacts are a common problem in recordings with wearable devices. In this work, we propose a fully automated and personalized method to detect motion artifacts in multimodal recordings devoted to the monitoring of the Cardiac Time Intervals (CTIs). The detection of motion artifacts was carried out by using template matching with a personalized template. The method yielded a balanced accuracy of 86%. Moreover, it proved effective to decrease the variability of the estimated CTIs by at least 17%. Our preliminary results show that personalized detection of motion artifacts improves the robustness of the assessment CTIs and opens to the use in wearable systems. [ABSTRACT FROM AUTHOR]

The Manchu language holds historical significance, but a complete dataset of Manchu script letters for training optical character recognition machine-learning models is currently unavailable. Therefore, this paper describes the process of creating a robust dataset of extracted Manchu script letters. Rather than performing automatic letter segmentation based on whitespace or the thickness of the central word stem, an image of the Manchu script was manually inspected, and one copy of the desired letter was selected as a region of interest. This selected region of interest was used as a template to match all other occurrences of the same letter within the Manchu script image. Although the dataset in this study contained only 4,000 images of five Manchu script letters, these letters were collected from twenty-eight writing styles. A full dataset of Manchu letters is expected to be obtained through this process. The collected dataset was normalized and trained using a simple convolutional neural network to verify its effectiveness. [ABSTRACT FROM AUTHOR]

In most liquid crystal display (LCD) backlight modules (BLMs), diffuser plates (DPs) play the essential role in blurring the backlight. A common BLM consists of multiple superimposed optical films. In a vision-based automated assembly system, to ensure sufficient accuracy, each of the multiple cameras usually shoots a local corner of the DP and jointly estimates the target pose, guiding the robot to assemble the DP on the BLM. In general, DPs are typical of texture-less objects with simple shapes. Due to the image background of superimposed multilayer optical films, the robustness of the most common detection methods must be improved to meet industrial needs. To solve the above problem, a texture-less surface matching method based on gray-scale images was proposed. An augmented and normalized gray-scale vector represents the texture-less gray-scale surface in a low-dimensional space. The cosine distance is then used to calculate the similarity between the template and matching vectors, combined with shape-based matching (SBM); the proposed method can obtain high robustness when detecting DPs. An image database from actual production lines was used in the experiment. In comparative tests with the NCC, SBM, YOLOv5s, and YOLOv5x methods, our proposed method had the best precision at all confidence thresholds. Although recall was slightly inferior to SBM, the comprehensive evaluation F1-Score reached 0.826, significantly outperforming the other methods. Regarding localizing accuracy, our algorithm also performed best, reaching 5.7 pixels. Although the time consumption of a single prediction is about 0.6 s, it can still meet industrial needs. These experimental results show that the proposed method has high robustness in detecting DPs and is especially suitable for vision-based automatic assembly tasks in BLM. [ABSTRACT FROM AUTHOR]

Road traffic monitoring is an imperative topic widely discussed among researchers. Systems used to monitor traffic frequently rely on cameras mounted on bridges or roadsides. However, aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger area. To this end, different models have shown the ability to recognize and track vehicles. However, these methods are not mature enough to produce accurate results in complex road scenes. Therefore, this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image bursts. The extracted frames were converted to grayscale, followed by the application of a georeferencing algorithm to embed coordinate information in to the images. The masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring system. Next, Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise reduction. After preprocessing, the blob detection algorithm helped detect the vehicles. Vehicles of varying sizes have been detected by implementing a dynamic thresholding scheme. Detection was done on the first image of every burst. Then, to track vehicles, the model of each vehicle was made to find its matches in the succeeding images using the template matching algorithm. To further improve the tracking accuracy by incorporating motion information, Scale Invariant Feature Transform (SIFT) features have been used to find the best possible match among multiple matches. An accuracy rate of 87% for detection and 80% accuracy for tracking in the A1 Motorway Netherland dataset has been achieved. For the Vehicle Aerial Imaging from Drone (VAID) dataset, an accuracy rate of 86% for detection and 78% accuracy for tracking has been achieved. [ABSTRACT FROM AUTHOR]

Astrocytes are non-neural cells, restricted to the brain and spinal cord, whose functions and morphology depend on their location. Astrocyte–astrocyte and astrocyte–neuron interactions occur through cytoplasmic Ca2+ level changes that are assessed to determine cell function and response (i.e., drug testing). The evaluation of alterations in intracellular Ca2+ levels primarily relies on fluorescence imaging techniques, performed through video recording of cells incubated with Ca2+-sensitive dyes. By observing ion concentration shifts over time in a delimited region of interest (ROI) encompassing a single cell, it is possible to draw conclusions on cell responses to specific stimuli. Our work describes a tool named SIGAA—signaling automated analysis, for astrocyte ROI-based fluorescent imaging. This tool is specifically tailored for two wavelengths excited dyes by using two inputs of Ca2+ signaling recorded frames/videos and outputting a set of features relevant to the experiment's conclusions and cell characterization. SIGAA performs automatic drift correction for the two recorded videos with a template matching algorithm, followed by astrocyte identification (ROI) using morphological reconstruction techniques. Subsequently, SIGAA extracts intracellular Ca2+ evolution functions for all identified ROIs detects function transients, and estimates a set of features for each signal. These features closely resemble those obtained through traditional methods and software used thus far. SIGAA is a new fully automated tool, which can speed up hour-long studies and analysis to a few minutes, showing reliable results as the validity tests indicate. [ABSTRACT FROM AUTHOR]

The field of real-time mobile object tracking is a crucial aspect of computer vision. Despite numerous algorithms proposed for efficient tracking, the high computational complexity presents challenges in achieving real-time performance. This paper presents a novel approach by introducing an adaptive search region proposal block that works in tandem with Mean-Shift and Unscented Kalman Filter. The block efficiently searches the region of the estimated object location. The dynamic changes in the appearance and size of a moving target make tracking difficult, but the proposed Multi-scale Template Matching technique addresses this challenge by utilizing the Normalized Cross-Correlation method in the adaptive search region. This optimization results in a reduced computational complexity and an increased frame rate of 53.4 FPS. Comparisons with various state-of-the-art trackers show that the proposed algorithm achieves the best results in terms of precision, success rate, and object tracking error. [ABSTRACT FROM AUTHOR]

In this paper, two new accelerated and robust template matching and object detection algorithms are established. The algorithms retain the accuracy measures compared to relevant well-known standard and recent schemes, and remarkably accelerate the execution time simultaneously. Instead of dealing with images in the grayscale or color representations, the proposed methods are established by measuring resemblance on extracted binary images. The two different techniques are based on the φ-correlation coefficient and logical circuits. Both techniques enhance the accuracy, compared to two classical and two recent methods, while giving very efficient running time. The algorithms are carried out on two different datasets used in state-of-the-art methods with performance and comparison analysis. Robustness of the proposed algorithms against normal and artificial noises is examined and assured as well. [ABSTRACT FROM AUTHOR]

The Mj6.5 (Mw6.2) event that occurred on 5 May 2023 near the northern shoreline of the northeastern tip of the Noto Peninsula, central Japan, is the largest event to date in a long‐lasting, intense earthquake swarm. Here we have created a more precise aftershock catalog associated with the 2023 Mj6.5 and the second‐largest 2022 Mj5.4 sequence to understand the rupture process of this largest earthquake. Most of the aftershocks are aligned along a ∼45° SE‐dipping plane. The mainshock initially ruptured the same deep section of the fault zone that had been ruptured by the 2022 Mj5.4 event, before propagating rapidly to shallow depths and to offshore along the ruptured fault plane. The aftershock front migrated at a speed of ∼20 km/hr. This rapid upward migration of the immediate aftershocks might be driven by upwelling of crustal fluids along the intensely fractured and permeable fault zone via mainshock dynamic rupture. Plain Language Summary: Near the northeastern tip of the Noto Peninsula, central Japan, a long‐lasting, intense earthquake swarm has continued since November 2020. On 5 May 2023, the largest Mj6.5 (Mw6.2) event to date occurred. We precisely located the aftershock distribution following the 2023 Mj6.5 and the second‐largest 2022 Mj5.4 sequence and enhanced the catalog by searching events based on waveform similarity to understand the rupture process of this largest earthquake. The 2023 Mj6.5 event initially ruptured the same deep section of the fault that had been ruptured by the 2022 Mj5.4 event, before propagating rapidly to shallow depths and to offshore along the ruptured fault. We can see that the aftershock front moved at a speed of ∼20 km/hr, which is a rare case that constrains the rapid movement of aftershocks. The rapid upward movement of the aftershocks may have been caused by the upwelling of crustal fluids along the permeable fault zone created by the dynamic rupture of the mainshock. Key Points: We constructed a more precise aftershock catalog associated with the two major ruptures during a long‐lasting intense seismic swarmWe identify a rapid migration of early aftershocks following the largest 2023 Mj6.5 earthquake to date during the seismic swarmUpwelling of crustal fluids along the fractured permeable fault zone could drive the rapid migration of early aftershocks [ABSTRACT FROM AUTHOR]

Featured Application: This work has the capability to estimate the pose of unseen textureless objects, which can find applications in automated assembly processes. For example, it can assist a robotic arm in assembly or docking tasks by determining the relative spatial poses of multiple objects. Additionally, this work has potential applications in augmented reality tasks, such as estimating the pose of a mold to enable coloring or overlaying visual effects on the mold. 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]