In electromagnetic inverse scattering problems, Scattered field commonly needs to be measured by a large number of receiving antennas to provide enough scattered information for image reconstruction, which may increase the cost of the experimental sy...
Accurate displacement field measurement by holographic interferometry requires robust analysis of high-density fringe patterns, which is hindered by speckle noise inherent in any interferogram, no matter how perfect. Conventional skeletonization meth...
An important aspect of medical rehabilitation using exoskeletons is objective monitoring of the effectiveness of the exercise program. This control is most often manual and relies on the attention of a rehabilitation physician, but advanced rehabilit...
The skeleton model is an essential tool to support the top-down design of complex products, and the rationality of its design has a significant influence on the detailed design of products. However, the current skeleton models of complex products are...
In recent years, skeleton-based human action recognition (HAR) approaches using convolutional neural network (CNN) models have made tremendous progress in computer vision applications. However, using relative features to depict human actions, in addi...
Pig tracking contributes to the assessment of pig behaviour and health. However, pig tracking on real farms is very difficult. Owing to incomplete camera field of view (FOV), pigs frequently entering and exiting the camera FOV affect the tracking acc...
The accurate measurement of a crack width in concrete infrastructure is essential for structural safety assessment and maintenance. However, existing image-based methods either suffer from overestimation in complex geometries or are computationally i...
The advancement of deep learning in human activity recognition (HAR) using 3D skeleton data is critical for applications in healthcare, security, sports, and human–computer interaction. This paper tackles a well-known gap in the field, which is...
The phenotypic parameters of root systems are vital in reflecting the influence of genes and the environment on plants, and three-dimensional (3D) reconstruction is an important method for obtaining phenotypic parameters. Based on the characteristics...
To address the current problem of the difficulty of extracting the phenotypic parameters of tomato plants in a non-destructive and accurate way, we proposed a method of stem and leaf segmentation and phenotypic extraction of tomato plants based on sk...
Automatic acquisition of phenotypic traits in tomato plants is important for tomato variety selection and scientific cultivation. Because of time-consuming and labor-intensive traditional manual measurements, the lack of complete structural informati...
UAV-based visual inspection has become an effective approach for acquiring surface information from three-dimensional building structures. However, existing coverage path planning methods usually treat viewpoint planning and path sequencing as two se...
Current understanding of the load-transfer mechanism in the skeletal contact state of asphalt mixtures and its influence on macroscopic mechanical properties remains insufficient. This knowledge gap leads to difficulties in accurately predicting the...
A three-dimensional (3D) skeletonization algorithm extracts the skeleton of a 3D model and provides it for many applications, such as 3D model classification and identification. There are three major skeletonization methodologies used in the literatu...
The ISTTWN algorithm overcame the defect of separating the production process of skeleton points and skeleton lines in tree branch point cloud skeleton extraction and improved the accuracy of the extracted initial skeletons, but the skeletons need fu...
Tree skeletons play an important role in tree structure analysis and 3D model reconstruction. However, it is a challenge to extract a skeleton from a tree point cloud with complex branches. In this paper, an automatic and fast tree skeleton extractio...
Traffic police gesture recognition is important in automatic driving. Most existing traffic police gesture recognition methods extract pixel-level features from RGB images which are uninterpretable because of a lack of gesture skeleton features and m...
The extraction of skeleton lines of buildings is a key step in building spatial analysis, which is widely performed for building matching and updating. Several methods for vector data skeleton line extraction have been established, including the impr...
A tree skeleton reflects the geometric and structural characteristics of a tree. Terrestrial laser scanning (TLS) provides an effective means to construct tree skeletons that can capture the surface and topological features of trees. However, it is d...
This article presents the Base Point Split (BPSplit) algorithm to generate a complex polygon skeleton based on sets of vector data describing lakes and rivers. A key feature of the BPSplit algorithm is that it is dependent on base points representing...
The primary premise of autonomous railway inspection using unmanned aerial vehicles is achieving autonomous flight along the railway. In our previous work, fitted centerline-based unmanned aerial vehicle (UAV) navigation is proven to be an effective...
Hypergraphs have received widespread attention in modeling complex data correlations due to their superior performance. In recent years, some researchers have used hypergraph structures to characterize complex non-pairwise joints in the human skeleto...
Fall detection in multi-person environments, such as nursing homes and rehabilitation centers, is essential for ensuring the safety of vulnerable populations. Despite advances in deep learning, current vision- and skeleton-based fall detection system...
In this study, we propose a method for minimizing the noise of Kinect sensors for 3D skeleton estimation. Notably, it is difficult to effectively remove nonlinear noise when estimating 3D skeleton posture; however, the proposed randomized unscented K...
In order to improve the accuracy of semantic model intrinsic detection, a skeleton-based high-level semantic model intrinsic self-symmetry detection method is proposed. The semantic analysis of the model set is realized by the uniform segmentation of...
Spatiotemporal graph convolution has made significant progress in skeleton-based action recognition in recent years. Most of the existing graph convolution methods take all the joints of the human skeleton as the overall modeling graph, ignoring the...
Skeleton-based action recognition is a research hotspot in the field of computer vision. Currently, the mainstream method is based on Graph Convolutional Networks (GCNs). Although there are many advantages of GCNs, GCNs mainly rely on graph topologie...
With the increasing aging population, nursing care providers have been facing a substantial risk of work-related musculoskeletal disorders (WMSDs). Visual-based pose estimation methods, like OpenPose, are commonly used for ergonomic posture risk asse...
The recognition of stereotyped action is one of the core diagnostic criteria of Autism Spectrum Disorder (ASD). However, it mainly relies on parent interviews and clinical observations, which lead to a long diagnosis cycle and prevents the ASD childr...
With the developments in high-performance nano-insulation material technology, theoretical studies on the heat transfer mechanisms in these materials have been conducted. However, the conductivity of nanometer-sized skeletons is still unclear. It is...
The purpose of this paper is to propose a methodology to optimise the granular skeleton assembly of cementitious materials containing non-spherical aggregates. The method is general and can be applied to any granular skeleton whatever the aggregate s...
In recent years, human action recognition has received increasing attention as a significant function of human–machine interaction. The human skeleton is one of the most effective representations of human actions because it is highly compact an...
Automatic extraction of the skeleton lines of river systems from high-resolution remote-sensing images has great significance for surveying and managing water resources. A large number of existing methods for the automatic extraction of skeleton line...
Biological structures have excellent mechanical performances including lightweight, high stiffness, etc. However, these are difficult to apply directly to some given complex structures, such as automobile frame, control arm, etc. In this study, a nov...
Human action recognition has been applied in many fields, such as video surveillance and human computer interaction, where it helps to improve performance. Numerous reviews of the literature have been done, but rarely have these reviews concentrated...
Skeleton-based human action recognition aims to recognize human actions from given skeleton sequences. The literature utilizes fixed-stride sampling and uniform aggregations, which are independent of the input data and do not focus on representative...
In computer vision, accurately estimating a 3D human skeleton from a single RGB image remains a challenging task. Inspired by the advantages of multi-view approaches, we propose a method of predicting enhanced 2D skeletons (specifically, predicting t...
One key step to the tree structure study is skeleton processing. Although there are lots of extraction approaches, the existing methods have paid less attention to extraction effectiveness, which highly use redundant points to formulate the skeleton...
Gait recognition is a behavioral biometric technique that identifies individuals based on their unique walking patterns, enabling long-distance identification. Traditional gait recognition methods rely on appearance-based approaches that utilize back...
Three-dimensional (3D) human skeleton extraction is a powerful tool for activity acquirement and analyses, spawning a variety of applications on somatosensory control, virtual reality and many prospering fields. However, the 3D human skeletonization...
Emotion recognition has drawn consistent attention from researchers recently. Although gesture modality plays an important role in expressing emotion, it is seldom considered in the field of emotion recognition. A key reason is the scarcity of labele...
Extracting 3D skeletons from point clouds is a challenging task in computer vision. Most existing deep learning methods rely heavily on supervised data requiring extensive manual annotation. Consequently, re-labeling is often necessary for cross-cate...
Extracting the split line of narrow and long patches is important for the generalization of land-use thematic data. There are two commonly used methods for extracting the split lines: One is based on Delaunay triangulation and the other is based on s...
Skeleton-based human action recognition has made great progress, especially with the development of a graph convolution network (GCN). The most important work is ST-GCN, which automatically learns both spatial and temporal patterns from skeleton sequ...
Skeleton-based action recognition, renowned for its computational efficiency and indifference to lighting variations, has become a focal point in the realm of motion analysis. However, most current methods typically only extract global skeleton featu...
Skeleton-based human action recognition has attracted extensive attention due to the robustness of the human skeleton data in the field of computer vision. In recent years, there is a trend of using graph convolutional networks (GCNs) to model the hu...
Recent years have seen an explosion in interest in and development of action recognition based on skeletal data. Contemporary methods using fully gated units can successfully extract characteristics from human skeletons by relying on the human topolo...
Graph convolutional networks (GCNs) have brought considerable improvement to the skeleton-based action recognition task. Existing GCN-based methods usually use the fixed spatial graph size among all the layers. It severely affects the model’s a...
To automatically evaluate the ergonomics of workers, 3D skeletons are needed. Most ergonomic assessment methods, like REBA, are based on the different 3D joint angles. Thanks to the huge amount of training data, 2D skeleton detectors have become very...
With the advancement in pose estimation techniques, skeleton-based person identification has recently received considerable attention in many applications. In this study, a skeleton-based person identification method using a deep neural network (DNN)...
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