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Time series segmentation has attracted more interests in recent years, which aims to segment time series into different segments, each reflects a state of the monitored objects. Although there have been many surveys on time series segmentation, most of them focus more on change point detection (CPD) methods and overlook the advances in boundary detection (BD) and state detection (SD) methods. In this paper, we categorize time series segmentation methods into CPD, BD, and SD methods, with a specific focus on recent advances in BD and SD methods. Within the scope of BD and SD, we subdivide the methods based on their underlying models/techniques and focus on the milestones that have shaped the development trajectory of each category. As a conclusion, we found that: (1) Existing methods failed to provide sufficient support for online working, with only a few methods supporting online deployment; (2) Most existing methods require the specification of parameters, which hinders their ability to work adaptively; (3) Existing SD methods do not attach importance to accurate detection of boundary points in evaluation, which may lead to limitations in boundary point detection. We highlight the ability to working online and adaptively as important attributes of segmentation methods, the boundary detection accuracy as a neglected metrics for SD methods. [ABSTRACT FROM AUTHOR]

Improving the horizontal boundaries of subsurface geological structures is one of the main objectives in interpreting potential fields. To solve this problem, a number of different algorithms have been introduced based on the derivatives of the field. However, these algorithms have some drawbacks, e.g., the determined edges do not match the actual boundaries. Here, we present a new algorithm based on the gradient amplitude and its derivatives which yields more precise and clear boundaries. The robustness of the proposed technique is illustrated using theoretical examples and a real example from Kon Tum province, Vietnam. Our results show that the proposed technique can produce results with better resolution and minimizes the artifacts in the pseudo-boundary map. [ABSTRACT FROM AUTHOR]

Spray cooling, droplet interaction with complex surfaces, and droplet evaporation from solid walls are promising areas of research. These experiments require automated digital image processing, particularly when recording large amounts of video data. This paper presents methods for recognition of objects in digital images in order to obtain quantitative characteristics of evaporating droplets in low-light conditions. New algorithms have been developed to identify and close object boundaries in conditions of limited visibility, enabling measurement of the geometric parameters of evaporating droplets. A comparison is made with data obtained using manual image processing, and the usability conditions of the proposed algorithms are demonstrated. [ABSTRACT FROM AUTHOR]

As a basic task in information extraction, named entity recognition (NER) can provide effective support for machine translation, relation extraction and other downstream tasks, and is of great research significance. To tackle the problem of fuzzy entity boundary in Chinese named entity recognition methods, a named entity recognition model combining entity boundary cue is proposed. The model is composed of three modules: boundary detection, cue generation and entity classification. Firstly, the entity boundary detection module is used to identify the entity boundary. Then, the entity span is generated according to the boundary information in the cue generation module, and the text sequence with the boundary cue label is obtained. Through the boundary cue label, the model can perceive the entity boundary in the sentence, and learn the semantic dependence characteristics of the entity boundary and context. Finally, the text sequence with boundary cue tags is employed as the input of entity classification module, and the semantic interaction between tags is enhanced by the Biaffine mechanism, then combined with the joint prediction of multilayer perceptron and Biaffine mechanism as the result of entity recognition. The F1 values of this model on ACE2005 Chinese dataset and Weibo dataset reaches 90.47% and 73.54% respectively, which verifies the effectiveness of the model for Chinese named entity recognition. [ABSTRACT FROM AUTHOR]

Currently, weakly supervised semantic segmentation methods based on image-level annotation often rely on pseudo pixel masks generated by seed regions. However, the growth of seed regions is stochastic, and in cases where targets are occluded or overlapped in the image without additional reference information, the segmentation network may encounter issues of missed or incorrect segmentation. To address this problem, this paper proposes a polarized attention mechanism for weakly supervised semantic segmentation networks. The attention mechanism consists of a semantic perception branch and a boundary detection branch. The semantic perception branch allows the network to better distinguish the category of each pixel in the image. Subsequently, the boundary detection branch enables the seed region to naturally expand towards the target boundary. The pseudo pixel mask generated by this method provides better coverage of the target area and improves the performance of the segmentation network. The test set and validation set mean Intersection over Union (mIoU) of the PASCAL VOC 2012 dataset achieved 72.1% and 73.2%. The results of the experiments demonstrated the effectiveness of the proposed method. The experimental results indicated that the attention mechanism, as proposed in this paper, can effectively enhance the segmentation performance in situations where objects in the image are occluded or overlapped.

Semantic segmentation is an important issue in intelligent interpretation of remote sensing, playing an important role in applications such as Earth observation and land data update. However, remote sensing images often contain complex ground objects and the boundaries between them are blurred, which poses a huge challenge to the semantic segmentation task of remote sensing images. This article proposes a dual stream hybrid network (DSHNet) model, which focuses on parallel extraction of semantic and boundary features in remote sensing images, and improves the performance of semantic segmentation by fully integrating dual stream information. In the semantic stream, the ViT model pretrained on remote sensing images is used as the backbone network for feature extraction. In the boundary stream, the boundary detection operator Sobel is used to capture the boundaries of different ground objects in the image, and a boundary enhancement mechanism is taken to optimize and enhance the feature representation of ground object boundaries. In addition, DSHNet designs a feature fusion module to cross-aggregate features from both semantic and boundary streams. Compared with the state-to-art semantic segmentation methods, DSHNet model has achieved the best performance on two datasets of Yellow River Estuary Wetland and Gaofen image dataset.

Abstract The detection of river boundaries is a crucial branch of the intelligent perception of unmanned surface vehicles (USVs), it can be used to determine the driving areas of USVs, and also to ensure driving safety by limiting the effective drivable areas of USVs in the river areas. Aiming to detect the boundaries of incompletely structured river channels, this study proposes a real‐time detection method for river boundaries based on a Light Detection and Ranging (LiDAR) sensor. The point clouds that are disturbed by the water surface noise are filtered firstly, and then the spatial and geometric features are extracted separately from the point cloud detected above the water surface. To prevent the error detection and missing detection, the boundary point information is predicted and calibrated in real time by Extended Kalman Filter (EKF). A planning track generation algorithm for coastal autonomous cruise without relying on high‐precision maps, and a heading and distance adaptive control method by Proportional‐Integral‐Derivative (PID), and different driving line generation methods for driving along the narrow river and wide river are proposed respectively. The experimental data verification of river boundary detection shows that the algorithm is accurate, real‐time, and robust.

Extracting high-level structural information from 3D point clouds is challenging but essential for tasks like urban planning or autonomous driving requiring an advanced understanding of the scene at hand. Existing approaches are still not able to produce high-quality results consistently while being fast enough to be deployed in scenarios requiring interactivity. We propose to utilize a novel set of features describing the local neighborhood on a per-point basis via first and second order statistics as input for a simple and compact classification network to distinguish between non-edge, sharp-edge, and boundary points in the given data. Leveraging this feature embedding enables our algorithm to outperform the state-of-the-art technique PCEDNet in terms of quality and processing time while additionally allowing for the detection of boundaries in the processed point clouds. [Display omitted] [ABSTRACT FROM AUTHOR]

Thyroid nodule is an asymptomatic disorder which mostly occurs due to high production of thyroid hormones from the thyroid gland. The diagnosis is usually made by the radiologist and endocrinologists which heavily relies on their experience and expertise. Ultrasonography is one of the principal means for the initial assessment of nodules which is mainly performed when there is suspect of formation of nodules. In this research work, an optimized convolutional neural network model is proposed for the identification of thyroid nodules using various deep learning techniques like dense neural network, Alexnet, Resnet-50 and Visual geometry group-16. A total of 295 public and 654 collected thyroid ultrasonography datasets are considered in this work. The proposed model is evaluated on 1475 public and 3270 collected thyroid ultrasonography datasets with data augmentation technique. We experimentally determined the best optimized value for learning rate and drop out factor to enhance the performance of the models. The proposed model has achieved an accuracy of 93.75%, sensitivity of 94.62%, specificity of 92.53% and f-measure of 94.09% on the public dataset in experiment-I and an accuracy of 96.89%, sensitivity of 97.80%, specificity of 94.73% and f-measure of 97.26% on the collected dataset in experiment-II. The proposed model has shown an improvement of (4.57%, 7.84%), (5.06%, 8.24%), (4.43%, 6.63%) and (4.66%, 7.83%) in terms of accuracy, sensitivity, specificity and f-measure on (dataset −1, dataset-2) against other state of the art models. [ABSTRACT FROM AUTHOR]

Liver segmentation is a prolific and important area of research that has been deeply studied for the last three decades. Its prominence is increasing in modern Computer-Aided disease Diagnosis (CAD) to deal with a huge amount of images. In this paper, the CT scan image is resized to 256 X 256, and noise is reduced by a median filter, and then local peaks are acquired. The optimal clusters (k) to be formed by Expectation-Maximization (EM) algorithm are obtained by setting the distance between local peaks and height greater than 5. Formulate k number of clusters using the EM algorithm. Crop random section of liver and obtain all the local peaks greater than average of local peaks. This provides the minimum and maximum threshold values using which a threshold-based segmentation is performed. The anticipated algorithm that is verified on 55 CT scan images offers promising results. The experimental outcomes are compared with the existing cluster-based liver segmentation algorithms. [ABSTRACT FROM AUTHOR]

The detection of river boundaries is a crucial branch of the intelligent perception of unmanned surface vehicles (USVs), it can be used to determine the driving areas of USVs, and also to ensure driving safety by limiting the effective drivable areas of USVs in the river areas. Aiming to detect the boundaries of incompletely structured river channels, this study proposes a real‐time detection method for river boundaries based on a Light Detection and Ranging (LiDAR) sensor. The point clouds that are disturbed by the water surface noise are filtered firstly, and then the spatial and geometric features are extracted separately from the point cloud detected above the water surface. To prevent the error detection and missing detection, the boundary point information is predicted and calibrated in real time by Extended Kalman Filter (EKF). A planning track generation algorithm for coastal autonomous cruise without relying on high‐precision maps, and a heading and distance adaptive control method by Proportional‐Integral‐Derivative (PID), and different driving line generation methods for driving along the narrow river and wide river are proposed respectively. The experimental data verification of river boundary detection shows that the algorithm is accurate, real‐time, and robust. [ABSTRACT FROM AUTHOR]

Barrier coverage is a fundamental application in wireless sensor networks, which are widely used for smart cities. In applications, the sensors form a barrier for the intruders and protect an area through intrusion detection. In this paper, we study a new branch of barrier coverage, namely warning barrier coverage (WBC). Different from the classic barrier coverage, WBC has the inverse protect direction, which moves the sensors surrounding a dangerous region and protects any unexpected visitors by warning them away from the dangers. WBC holds a promising prospect in many danger keep out applications for smart cities. For example, a WBC can enclose the debris area in the sea and alarm any approaching ships in order to avoid their damaging propellers. One special feature of WBC is that the target region is usually dangerous and its boundary is previously unknown. Hence, the scattered mobile nodes need to detect the boundary and form the barrier coverage themselves. It is challenging to form these distributed sensor nodes into a barrier because a node can sense only the local information and there is no global information of the unknown region or other nodes. To this end, in response to the newly proposed issue of the formation of barrier cover, we propose a novel solution AutoBar for mobile sensor nodes to automatically form a WBC for smart cities. Notably, this is the first work to trigger the coverage problem of the alarm barrier, wherein the regional information is not pre-known. To pursue the high coverage quality, we theoretically derive the optimal distribution pattern of sensor nodes using convex theory. Based on the analysis, we design a fully distributed algorithm that enables nodes to collaboratively move toward the optimal distribution pattern. In addition, AutoBar is able to reorganize the barrier even if any node is broken. To validate the feasibility of AutoBar, we develop the prototype of the specialized mobile node, which consists of two kinds of sensors: one for boundary detection and another for visitor detection. Based on the prototype, we conduct extensive real trace-driven simulations in various smart city scenarios. Performance results demonstrate that AutoBar outperforms the existing barrier coverage strategies in terms of coverage quality, formation duration, and communication overhead. [ABSTRACT FROM AUTHOR]

Edge detection is a crucial step in many computer vision tasks, and in recent years, models based on deep convolutional neural networks (CNNs) have achieved human-level performance in edge detection. However, we have observed that CNN-based methods rely on pre-trained backbone networks and generate edge images with unwanted background details. We propose four new fusion difference convolution (FDC) structures that integrate traditional gradient operators into modern CNNs. At the same time, we have also added a channel spatial attention module (CSAM) and an up-sampling module (US). These structures allow the model to better recognize the semantic and edge information in the images. Our model is trained from scratch on the BIPED dataset without any pre-trained weights and achieves promising results. Moreover, it generalizes well to other datasets without fine-tuning. [ABSTRACT FROM AUTHOR]

Aiming at the problem that it was difficult for the gray elements and zigzag boundaries in the result of density-based topology optimization method to convert the topology optimization result into the computer-aided design (CAD) model and significantly hinder its engineering application, a new method of boundary smooth reconstruction was proposed for variable density topology optimization result. First, the pseudo density values obtained by the density-based method were used to construet a non-uniform rational B-spline (NURBS) surface in the design domain. Then, a hisection iteration algorithm was constructed to determine the position of the " zero level set" surface, so as to ensure that the transformed model met the volume constraint, and the Visual Basic Application (VBA) was employed to compile the iterative program. Finally, the "zero level set" in the level set method was applied as the structure boundary to construct the CAD model and achieve the smooth boundary extraction. The results of the study show that the proposed method reduces the requirement of reducing gray elements for density-based topology optimization, eliminates the zigzag boundary, and realizes the fast conversion from the optimized configuration to the CAD model. The volume fraction error of the topology optimization model obtained by the three numerical studies is less than 1%, the boundary is smooth and the structural performance is good, which shows the validity and versatility of the method. [ABSTRACT FROM AUTHOR]

Accurate estimation of the change in crime over time is a critical first step toward better understanding of public safety in large urban environments. Bayesian hierarchical modeling is a natural way to study spatial variation in urban crime dynamics at the neighborhood level, since it facilitates principled "sharing of information" between spatially adjacent neighborhoods. Typically, however, cities contain many physical and social boundaries that may manifest as spatial discontinuities in crime patterns. In this situation, standard prior choices often yield overly smooth parameter estimates, which can ultimately produce mis-calibrated forecasts. To prevent potential over-smoothing, we introduce a prior that partitions the set of neighborhoods into several clusters and encourages spatial smoothness within each cluster. In terms of model implementation, conventional stochastic search techniques are computationally prohibitive, as they must traverse a combinatorially vast space of partitions. We introduce an ensemble optimization procedure that simultaneously identifies several high probability partitions by solving one optimization problem using a new local search strategy. We then use the identified partitions to estimate crime trends in Philadelphia between 2006 and 2017. On simulated and real data, our proposed method demonstrates good estimation and partition selection performance. for this article are available online. [ABSTRACT FROM AUTHOR]

Business districts are urban areas that have various functions for gathering people, such as work, consumption, leisure and entertainment. Due to the dynamic nature of business activities, there exists significant tidal effect on the boundary and functionality of business districts. Indeed, effectively analyzing the tidal patterns of business districts can benefit the economic and social development of a city. However, with the implicit and complex nature of business district evolution, it is non-trivial for existing works to support the fine-grained and timely analysis on the tidal effect of business districts. To this end, we propose a data-driven and multi-dimensional framework for dynamic business district analysis. Specifically, we use the large-scale human trajectory data in urban areas to dynamically detect and forecast the boundary changes of business districts in different time periods. Then, we detect and forecast the functional changes in business districts. Experimental results on real-world trajectory data clearly demonstrate the effectiveness of our framework on detecting and predicting the boundary and functionality change of business districts. Moreover, the analysis on practical business districts shows that our method can discover meaningful patterns and provide interesting insights into the dynamics of business districts. For example, the major functions of business districts will significantly change in different time periods in a day and the rate and magnitude of boundaries varies with the functional distribution of business districts. [ABSTRACT FROM AUTHOR]

An electromagnetic (EM) logging while drilling (LWD) tool is proposed for deep reading detection ahead of the drill bit. The EM LWD tool consists of one coil transmitter sub and two separated coil receiver subs. We utilize the attenuations of measured voltage signals as indicators for detecting the boundary ahead of the drill bit. The effects of frequency and the tool length on the detection capability of the tool are discussed. Also, the attenuations of coaxial voltage signals and coplanar voltage signals proactively respond to the anisotropic formation boundary ahead of the drill bit. The attenuations of coaxial and coplanar signals are sensitive to variations of horizontal conductivity of a conductive anisotropic target layer, but not sensitive to variations in its vertical component. Moreover, an air hang test verifies that the depth of detection of the tool could reach more than 30 m, which is also confirmed by the numerical simulations with an air-seawater-sediment model. The proposed EM LWD tool for detecting the formation boundary ahead of the drill bit is promising for applications of deep reading detections.

Named Entity Recognition (NER) is a fundamental but crucial task in natural language understanding, aiming at identifying entity mentions from free text. Current methods mainly use sequence-labeling and span-based models, where the former ignores the importance of token interaction, and the latter pays little attention to the global inter-dependency among entity tokens. In this work, we propose a novel NER model that consists of two branches: a Token-Pair Interaction Module (TPIM) and a U-shaped Network. The TPIM models head-tail relations between token pairs while capturing intrinsic token connectivity within entity boundaries. The U-shaped Network is employed to capture the contextual dependency in the token-pair relation matrix. Furthermore, we build a typical domain-specific entity dataset CCAEE based on real-world applications in the chemical accident domain. The experimental results on CCAEE and CLUENER datasets demonstrate the effectiveness of our proposed model.

In order to solve the problems of semantic loss and inaccurate boundary detection in the process of object tracking, a visual tracking algorithm combining parallel structure with dual attention-aware mechanism is proposed in this paper. As backbone network, parallel structure is composed of Convolutional neural network and Attention Cooperative(CAC) processing module, which is used for feature extraction. Because this structure can capture the local and global information of the target at the same time, it can solve the problem of semantic information loss. Dual Attention-aware Network(DAN) is used for feature enhancement, which is composed of target-aware attention and boundary-aware attention. Template online updating strategy is used to improve template quality, and an effective score prediction module-Template Elimination Mechanism(TEM) is designed in the CAC processing module to select high quality templates. This kind of object tracking algorithm which combines local and global information is called TrackCAC. The evaluation results on different datasets show that the algorithm can maintain high tracking precision and success in different scenarios. It shows good robustness and accuracy in the performance evaluation results on VOT datasets.

The Mekong Delta region of Vietnam has great potential for agricultural development thanks to natural incentives. Many livestock industries have developed for a long time and play an important role in the country with many agricultural export products. In the era of breakthrough technologies and advances in information technology, many techniques are used to support the development of smart agriculture. In particular, computer vision techniques are widely applied to help farmers save a lot of labour and cost. This study presents an approach for counting eels based on Mathematical Morphology Operations and Boundary Detection from images of breeding eels captured with the proposed photo box. The proposed method is evaluated using data collected directly from a breeding eel farm in Vietnam. The authors of the research evaluate and investigate the length distribution of eels to select the appropriate size for counting tasks. The experiments show positive results with an average Mean Absolute Error of 2.2 over a tray of more than 17 eels. The contribution of the research is to provide tools to support farmers in eel farms to save time and effort and improve efficiency.

Abstract MRI brain images are always of low contrast, which makes it difficult to identify to which area the information at the boundary of brain images belongs. This can make the extraction of features at the boundary more challenging, since those features can be misleading as they might mix properties of different brain regions. Hence, to alleviate such a problem, image boundary detection plays a vital role in medical image segmentation, and brain segmentation in particular, as unclear boundaries can worsen brain segmentation results. Yet, given the low quality of brain images, boundary detection in the context of brain image segmentation remains challenging. Despite the research invested to improve boundary detection and brain segmentation, these two problems were addressed independently, i.e., little attention was paid to applying boundary detection to brain segmentation tasks. Therefore, in this paper, we propose a boundary detection-based model for brain image segmentation. To this end, we first design a boundary segmentation network for detecting and segmenting images brain tissues. Then, we design a boundary information module (BIM) to distinguish boundaries from the three different brain tissues. After that, we add a boundary attention gate (BAG) to the encoder output layers of our transformer to capture more informative local details. We evaluate our proposed model on two datasets of brain tissue images, including infant and adult brains. The extensive evaluation experiments of our model show better performance (a Dice Coefficient (DC) accuracy of up to $$5.3\%$$ 5.3 % compared to the state-of-the-art models) in detecting and segmenting brain tissue images.

Visualizing the isosurfaces that represent material boundaries is an important technique for understanding the features of interest in a scalar volumetric dataset. However, one isosurface may contain multiple types of boundaries, i.e., boundaries between different pairs of materials. In this paper, we present a novel isosurface segmentation method that aids in learning structural information of a dataset by separating different types of boundaries in one isosurface. This method uses common boundary tests to classify a point on the isosurface. The test determines whether a point on the isosurface that is at a boundary shared by both the isosurface and a reference isosurface. It uses a gradient-guided sampling approach and is based on material boundary properties. A new region growing algorithm is developed to improve the segmentation results. Our new method can also be used to segment an isosurface that passes through both material boundaries and the interior of a material. Two applications of the new method are also demonstrated in the paper. One is to render and segment section planes to enhance visualization. The other one is to obtain more accurate and meaningful isosurface statistics. • Segment an isosurface that contains multiple types of material boundaries. • Use region growing approach to improve the isosurface segmentation results. • Correctly segment the section planes to show the interior structure of the object. • Segment an isosurface that passes through both material boundaries and material interior. • Obtain more accurate isosurface statistics by averaging them over different portions of the segmented isosurface. [Display omitted] [ABSTRACT FROM AUTHOR]

The most noteworthy neurodegenerative disorder nationwide is apparently the Alzheimer's disease (AD) which ha no proven viable treatment till date and despite the clinical trials showing the potential of preclinical therapy, a sensitive method for evaluating the AD has to be developed yet. Due to the correlations between ocular and brain tissue, the eye (retinal blood vessels) has been investigated for predicting the AD. Hence, en enhanced method named Enhanced Long Short Term Memory (E-LSTM) has been proposed in this work which aims at finding the severity of AD from ocular biomarkers. To find the level of disease severity, the new layer named precise layer was introduced in E-LSTM which will help the doctors to provide the apt treatments for the patients rapidly. To avoid the problem of overfitting, a dropout has been added to LSTM. In the existing work, boundary detection of retinal layers was found to be inaccurate during the segmentation process of Optical Coherence Tomography (OCT) image and to overcome this issue; Particle Swarm Optimization (PSO) has been utilized. To the best of our understanding, this is the first paper to use Particle Swarm Optimization. When compared with the existing works, the proposed work is found to be performing better in terms of F1 Score, Precision, Recall, training loss, and segmentation accuracy and it is found that the prediction accuracy was increased to 10% higher than the existing systems. [ABSTRACT FROM AUTHOR]

Barrier coverage is a fundamental application in wireless sensor networks, which are widely used for smart cities. In applications, the sensors form a barrier for the intruders and protect an area through intrusion detection. In this paper, we study a new branch of barrier coverage, namely warning barrier coverage (WBC). Different from the classic barrier coverage, WBC has the inverse protect direction, which moves the sensors surrounding a dangerous region and protects any unexpected visitors by warning them away from the dangers. WBC holds a promising prospect in many danger keep out applications for smart cities. For example, a WBC can enclose the debris area in the sea and alarm any approaching ships in order to avoid their damaging propellers. One special feature of WBC is that the target region is usually dangerous and its boundary is previously unknown. Hence, the scattered mobile nodes need to detect the boundary and form the barrier coverage themselves. It is challenging to form these distributed sensor nodes into a barrier because a node can sense only the local information and there is no global information of the unknown region or other nodes. To this end, in response to the newly proposed issue of the formation of barrier cover, we propose a novel solution AutoBar for mobile sensor nodes to automatically form a WBC for smart cities. Notably, this is the first work to trigger the coverage problem of the alarm barrier, wherein the regional information is not pre-known. To pursue the high coverage quality, we theoretically derive the optimal distribution pattern of sensor nodes using convex theory. Based on the analysis, we design a fully distributed algorithm that enables nodes to collaboratively move toward the optimal distribution pattern. In addition, AutoBar is able to reorganize the barrier even if any node is broken. To validate the feasibility of AutoBar, we develop the prototype of the specialized mobile node, which consists of two kinds of sensors: one for boundary detection and another for visitor detection. Based on the prototype, we conduct extensive real trace-driven simulations in various smart city scenarios. Performance results demonstrate that AutoBar outperforms the existing barrier coverage strategies in terms of coverage quality, formation duration, and communication overhead.

This paper presents a Lightweight Dense Convolutional (LDC) neural network for edge detection. The proposed model is an adaptation of two state-of-the-art approaches, but it requires less than 4% of parameters in comparison with these approaches. The proposed architecture generates thin edge maps and reaches the highest score (i.e., ODS) when compared with lightweight models (models with less than 1 million parameters), and reaches a similar performance when compare with heavy architectures (models with about 35 million parameters). Both quantitative and qualitative results and comparisons with state-of-the-art models, using different edge detection datasets, are provided. The proposed LDC does not use pre-trained weights and requires straightforward hyper-parameter settings. The source code is released at https://github.com/xavysp/LDC.

With the increase in the scale of mining in horizontal and highly deviated wells, electromagnetic boundary detection while drilling plays an important role in boundary detection. This paper examines three types of antenna structures commonly used in electromagnetic boundary detection and measurement methods and also performs a numerical simulation of the edge detection capability of the three structures in horizontal wells. The simulation experiment analyzes the influence of formation resistivity contrast, frequency, spacing, and other factors on the capability of edge detection and provides data that supports the design of instrument antenna parameters. The numerical simulation shows that the tilted and orthogonal receiving antennas demonstrate improved performance both in detecting the interface when approaching from high-resistance layers and low-resistance layers. In addition, the capability of boundary detection can be improved by decreasing the frequency and increasing the spacing between the transmitter and receiver.

• Reference points (word-gap) between the adjacent words are used as entity boundary representations. • A neighborhood span proposal strategy can generate better training negative samples. • Joint modeling entity boundary detection with entity span classification to identify nested entities. Named entity recognition is a task to extract named entities with predefined entity types. Span classification is a popular method to support this task. It has the advantage to solve nested structures and make full use of token features in a span. The problem is that exhaustively enumerating and verifying all entity spans suffer from high computational complexity and data imbalance. Furthermore, spans with a high overlapping ratio share the same contextual features in a sentence, which is easy to lead to false positive errors caused by inaccurate entity boundaries. In this paper, we present a model to detect the entity boundaries and predict entity candidates jointly. Instead of labeling tokens, our model makes the prediction based on gap representations between words, which avoids the ambiguity when a token has several labels. We also propose a neighborhood span proposal strategy to generate reasonable negative samples for training, which effectively reduces the data imbalance problem. Our model is evaluated on the ACE2005 and GENIA corpora. It achieves performance close to the state-of-the-art in F1 scores of 88.55% and 79.81%, respectively. [ABSTRACT FROM AUTHOR]

Identifying the locations of ice surface and bottom boundary in the radar imagery enables the calculation of ice sheet thickness, which is one of important inputs for ice-sheet modelling and global climate research. Therefore, accurate predictions of the boundaries can contribute to improve the accuracy of global climate analysis and sea level prediction. However, an accurate boundary detection in radar sounder data collected from the polar ice sheet has still been a challenge because the boundaries of the ice layer are usually very weak and noisy, and subglacial topography is highly variable. In recent years, the deep learning methods have surpassed the performances of traditional technology and helped to overcome a series of problems, including image boundary segmentation and target detection. This paper proposes a multi-scale feature fusion network (MFFN) for boundary detection of ice sheet radar echograms, where the ground truth supervises the output of the network at different stages, rather than the output of the last layer of the network. Also, a multi-scale convolution module (MCM) is introduced to learn the rich multi-scale representation of each network stage from shallow to deep, which uses convolution with different dilation rates to obtain multi-scale features. Furthermore, an improved loss function makes the proposed MFFN more effective to solve the sample imbalance problem of boundary detection, and further improves the accuracy of boundary detection. The proposed method is verified experimentally using the radar echograms from 2009 provided by the Center of Remote Sensing of Ice Sheets (CReSIS) that are used as training and test data. In the experiments, the proposed MFFN not only achieves state-of-the-art boundary detection accuracy on the test set but also improves the visual effect by generating fine boundaries. [ABSTRACT FROM AUTHOR]

Given a music recording, music structure analysis aims at identifying important structural elements and segmenting the recording according to these elements. In jazz music, a performance is often structured by repeating harmonic schemata (known as choruses), which lay the foundation for improvisation by soloists. Within the fields of music information retrieval (MIR) and computational musicology, the Weimar Jazz Database (WJD) has turned out to be an extremely valuable resource for jazz research. Containing high-quality solo transcriptions for 456 solo sections, the dataset opened up new avenues for the understanding of creative processes in jazz improvisation using computational methods. In this paper, we complement this dataset by introducing the Jazz Structure Dataset (JSD), which provides annotations on structure and instrumentation of entire recordings. The JSD comprises 340 recordings with more than 3000 annotated segments, along with a segment-wise encoding of the solo and accompanying instruments. These annotations provide the basis for training, testing, and evaluating models for various important MIR tasks, including structure analysis, solo detection, or instrument recognition. As an example application, we consider the task of structure boundary detection. Based on a traditional novelty-based as well as a more recent data-driven approach using deep learning, we indicate the potential of the JSD while critically reflecting on some evaluation aspects of structure analysis. In this context, we also demonstrate how the JSD annotations and analysis results can be made accessible in a user-friendly way via web-based interfaces for data inspection and visualization. All annotations, experimental results, and code for reproducibility are made publicly available for research purposes.

Edge detection is a crucial step in many computer vision tasks, and in recent years, models based on deep convolutional neural networks (CNNs) have achieved human-level performance in edge detection. However, we have observed that CNN-based methods rely on pre-trained backbone networks and generate edge images with unwanted background details. We propose four new fusion difference convolution (FDC) structures that integrate traditional gradient operators into modern CNNs. At the same time, we have also added a channel spatial attention module (CSAM) and an up-sampling module (US). These structures allow the model to better recognize the semantic and edge information in the images. Our model is trained from scratch on the BIPED dataset without any pre-trained weights and achieves promising results. Moreover, it generalizes well to other datasets without fine-tuning.

MRI brain images are always of low contrast, which makes it difficult to identify to which area the information at the boundary of brain images belongs. This can make the extraction of features at the boundary more challenging, since those features can be misleading as they might mix properties of different brain regions. Hence, to alleviate such a problem, image boundary detection plays a vital role in medical image segmentation, and brain segmentation in particular, as unclear boundaries can worsen brain segmentation results. Yet, given the low quality of brain images, boundary detection in the context of brain image segmentation remains challenging. Despite the research invested to improve boundary detection and brain segmentation, these two problems were addressed independently, i.e., little attention was paid to applying boundary detection to brain segmentation tasks. Therefore, in this paper, we propose a boundary detection-based model for brain image segmentation. To this end, we first design a boundary segmentation network for detecting and segmenting images brain tissues. Then, we design a boundary information module (BIM) to distinguish boundaries from the three different brain tissues. After that, we add a boundary attention gate (BAG) to the encoder output layers of our transformer to capture more informative local details. We evaluate our proposed model on two datasets of brain tissue images, including infant and adult brains. The extensive evaluation experiments of our model show better performance (a Dice Coefficient (DC) accuracy of up to 5.3 % compared to the state-of-the-art models) in detecting and segmenting brain tissue images. [ABSTRACT FROM AUTHOR]

We investigate identifying the boundary of a domain from sample points in the domain. We introduce new estimators for the normal vector to the boundary, distance of a point to the boundary, and a test for whether a point lies within a boundary strip. The estimators can be efficiently computed and are more accurate than the ones present in the literature. We provide rigorous error estimates for the estimators. Furthermore we use the detected boundary points to solve boundary-value problems for PDE on point clouds. We prove error estimates for the Laplace and eikonal equations on point clouds. Finally we provide a range of numerical experiments illustrating the performance of our boundary estimators, applications to PDE on point clouds, and tests on image data sets. [ABSTRACT FROM AUTHOR]