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Modern network systems (unmanned aerial vehicles groups, social networks, network production chains, transport and logistics networks, communication networks, cryptocurrency networks) are distinguished by their multi-element nature and the dynamics of connections between its elements. A number of discrete problems on the construction of optimal substructures of network systems described in the form of various classes of graphs are NP-complete problems. In this case, the variability and dynamism of the structures of network systems leads to an "additional" complication of the search for solutions to discrete optimization problems. At the same time, for some subclasses of dynamical graphs, which are used to model the structures of network systems, conditions for the solvability of a number of NP-complete problems can be distinguished. This subclass of dynamic graphs includes pre-fractal graphs. The article investigates NP-complete problems on pre-fractal graphs: a Hamiltonian cycle, a skeleton with the maximum number of pendant vertices, a monochromatic triangle, a clique, an independent set. The conditions under which for some problems it is possible to obtain an answer about the existence and to construct polynomial (when fixing the number of seed vertices) algorithms for finding solutions are identified.

Skeleton-based human action recognition has received extensive attention due to its efficiency and robustness to complex backgrounds. Though the human skeleton can accurately capture the dynamics of human poses, it fails to recognize human actions induced by the interaction between human and objects, making it is of great importance to further explore the interaction between the human and objects for human action recognition. In this paper, we devise the multi-stream interaction networks (MSIN), to simultaneously explore the dynamics of human skeleton, objects, and the interaction between human and objects. Specifically, apart from the traditional human skeleton stream, 1) the second stream explores the dynamics of object appearance from the objects surrounding the human body joints; and 2) the third stream captures the dynamics of object position in regard to the distance between the object and different human body joints. Experimental results on three popular skeleton-based human action recognition datasets, NTU RGB+D, NTU RGB+D 120, and SYSU, demonstrate the effectiveness of the proposed method, especially for recognizing the human actions with human-object interactions.

In complicated product industry such as aircraft manufacturing, an assembly model contains abundant engineering information for use in design, manufacture, and maintenance. Assembly retrieval can be used to find relevant models for knowledge reuse. However, an assembly with rotatable joints may have many poses, which brings difficulty to assembly retrieval, since there is no pose principle for assembly design. Therefore, focused on rotatable joints in assembly, a skeleton-based descriptor for pose-free assembly retrieval is proposed. The centroid points of part surfaces and contact faces in an assembly are extracted to construct a spatial-contact skeleton. The skeleton-based distance is proposed to measure the distance between two surface points, which is invariant to the rotatable joints. The distribution of skeleton distances between two parts is used to describe the pair. Considering a part paired with all other parts in the assembly, the set of part pairs is used to represent a part, and the modified Hausdorff distance is used to measure the dissimilarity between parts for assembly retrieval. Experiments are conducted to compare the accuracy of the proposed descriptor to holistic and structureless descriptors. The proposed method is shown to retrieve assemblies with similar parts and structures regardless of their rotatable joints.

Skeleton representation has attracted a great deal of attention recently as an extremely robust feature for human action recognition. However, its non-Euclidean structural characteristics raise new challenges for conventional solutions. Recent studies have shown that there is a native superiority in modeling spatiotemporal skeleton information with a Graph Convolutional Network (GCN). Nevertheless, the skeleton graph modeling normally focuses on the physical adjacency of the elements of the human skeleton sequence, which contrasts with the requirement to provide a perceptually meaningful representation. To address this problem, in this paper, we propose a perceptually-enriched graph learning method by introducing innovative features to spatial and temporal skeleton graph modeling. For the spatial information modeling, we incorporate a Local-Global Graph Convolutional Network (LG-GCN) that builds a multifaceted spatial perceptual representation. This helps to overcome the limitations caused by over-reliance on the spatial adjacency relationships in the skeleton. For temporal modeling, we present a Region-Aware Graph Convolutional Network (RA-GCN), which directly embeds the regional relationships conveyed by a skeleton sequence into a temporal graph model. This innovation mitigates the deficiency of the original skeleton graph models. In addition, we strengthened the ability of the proposed channel modeling methods to extract multi-scale representations. These innovations result in a lightweight graph convolutional model, referred to as Graph2Net, that simultaneously extends the spatial and temporal perceptual fields, and thus enhances the capacity of the graph model to represent skeleton sequences. We conduct extensive experiments on NTU-RGB+D 60&120, Northwestern-UCLA, and Kinetics-400 datasets to show that our results surpass the performance of several mainstream methods while limiting the model complexity and computational overhead.

Accurate lower-limb pose estimation is a prerequisite of skeleton based pathological gait analysis. To achieve this goal in free-living environments for long-term monitoring, single depth sensor has been proposed in research. However, the depth map acquired from a single viewpoint encodes only partial geometric information of the lower limbs and exhibits large variations across different viewpoints. Existing off-the-shelf three-dimensional (3D) pose tracking algorithms and public datasets for depth based human pose estimation are mainly targeted at activity recognition applications. They are relatively insensitive to skeleton estimation accuracy, especially at the foot segments. Furthermore, acquiring ground truth skeleton data for detailed biomechanics analysis also requires considerable effort. To address these issues, we propose a novel cross-domain self-supervised complete geometric representation learning framework, with knowledge transfer from the unlabelled synthetic point clouds of full lower-limb surfaces. The proposed method can significantly reduce the number of ground truth skeletons (with only 1\%) in the training phase, meanwhile ensuring accurate and precise pose estimation and capturing discriminative features across different pathological gait patterns compared to other methods.

Skeleton‐based action recognition algorithms have been widely applied to human action recognition. Graph convolutional networks (GCNs) generalize convolutional neural networks (CNNs) to non‐Euclidean graphs and achieve significant performance in skeleton‐based action recognition. However, existing GCN‐based models have several issues, such as the topology of the graph is defined based on the natural skeleton of the human body, which is fixed during training, and it may not be applied to different layers of the GCN model and diverse datasets. Besides, the higher‐order information of the joint data, for example, skeleton and dynamic information is not fully utilised. This work proposes a novel multi‐stream adaptive spatial‐temporal attention GCN model that overcomes the aforementioned issues. The method designs a learnable topology graph to adaptively adjust the connection relationship and strength, which is updated with training along with other network parameters. Simultaneously, the adaptive connection parameters are utilised to optimise the connection of the natural skeleton graph and the adaptive topology graph. The spatial‐temporal attention module is embedded in each graph convolution layer to ensure that the network focuses on the more critical joints and frames. A multi‐stream framework is built to integrate multiple inputs, which further improves the performance of the network. The final network achieves state‐of‐the‐art performance on both the NTU‐RGBD and Kinetics‐Skeleton action recognition datasets. The simulation results prove that the proposed method reveals better results than existing methods in all perspectives and that shows the superiority of the proposed method.

The general silhouette‐based gait recognition methods usually rely on binary human silhouette, which is easily affected by external factors, making it unsuitable for situations while wearing heavy clothes or carrying objects, etc. In this study, a new skeleton‐based gait recognition model is proposed. The model first extracts the spatial and temporal features of gait using the space and time relationship between body joints, and second, it eliminates redundant features by decomposing the feature map, to achieve a better recognition accuracy in the presence of external factors. Through abundant experiments on two common datasets, CASIA‐B and OUMVLP‐Pose, the proposed model has been proved to have higher recognition accuracy and remarkable robustness.

In human-human interaction, body language is one of the most important emotional expressions. However, each emotion category contains abundant emotional body gestures, and basic emotions used in most researches are difficult to describe complex and diverse emotional states. It is costly to collect sufficient samples of all emotional expressions, and new emotions or new body gestures that are not included in the training set may appear during testing. To address the above problems, we design a novel mechanism that treats each emotion category as a collection of multiple body gesture categories to make better use of gesture information for emotion recognition. A Generalized Zero-Shot Learning (GZSL) framework is introduced to recognize both seen and unseen body gesture categories with the help of semantic information, and emotion predictions are further provided based on the relationship between gestures and emotions. This framework consists of two branches. The first branch is a Hierarchical Prototype Network (HPN) which learns the prototypes of body gestures and uses them to calculate the emotion attentive prototypes. This branch aims to obtain predictions on samples of the seen gesture categories. The second branch is a Semantic Auto-Encoder (SAE) which utilizes semantic representations to predict samples of unseen gesture categories. Thresholds are further trained to determine which branch result will be used during testing, and the emotion labels are finally obtained from these results. Comprehensive experiments are conducted on an emotion recognition dataset which contains skeleton data of multiple body gestures, and the performance of our framework is superior to both the traditional emotion classifier and state-of-the-art zero-shot learning methods.

A novel method for skeleton-based action recognition by fusing multi-level spatial features and multi-level temporal features is proposed in this article. Recently, Graph Convolutional Network (GCN) for skeleton-based action recognition has attracted the eyes of many researchers and has a great performance in the field of action recognition. But most of them focus on changing architecture of single-stream network and only use simple methods like average fusion to fuse different forms of skeleton data. In this article, we shift the focus to the problem that insufficient interactions between the different forms of features for that networks are unable to fully capture efficient information from skeleton data. To tackle this problem, we propose a multi-stream network called Symmetrical Enhanced Fusion Network (SEFN). The network is composed of a spatial stream, a temporal stream and a fusion stream. The spatial stream extracts spatial features from skeleton data by GCN. The temporal stream is able to extract temporal features from skeleton data with the help of the embedded Motion Sequence Calculation Algorithm. The fusion stream provides an early fusion method and extra fusion information for the whole network. It gathers multi-level features from two feature extractions and fuses them with the Multi-perspective Attention Fusion Module (MPAFM) we propose. The MPAFM enables different forms of data to enhance each other and can strengthen feature extractions. In the final, we generalize the skeleton data from joint data to bone data and evaluate our network in three large-scale benchmarks: NTU-RGBD, NTU-RGBD 120 and Kinetics-Skeleton. Experiment results demonstrate that our method achieves competitive performance.

Person re-identification (Re-id) is one of the important tools of video surveillance systems, which aims to recognize an individual across the multiple disjoint sensors of a camera network. Despite the recent advances on RGB camera-based person re-identification methods under normal lighting conditions, Re-id researchers fail to take advantages of modern RGB-D sensor-based additional information (e.g. depth and skeleton information). When traditional RGB-based cameras fail to capture the video under poor illumination conditions, RGB-D sensor-based additional information can be advantageous to tackle these constraints. This work takes depth images and skeleton joint points as additional information along with RGB appearance cues and proposes a person re-identification method. We combine 4-channel RGB-D image features with skeleton information using score-level fusion strategy in dissimilarity space to increase re-identification accuracy. Moreover, our propose method overcomes the illumination problem because we use illumination invariant depth image and skeleton information. We carried out rigorous experiments on two publicly available RGBD-ID re-identification datasets and proved the use of combined features of 4-channel RGB-D images and skeleton information boost up the rank 1 recognition accuracy.

The purpose of the study. Study the organizational aspects of providing primary health care for victims with the facial department of the skeleton as a result of traffic accidents. Materials and methods. To achieve this goal, a retrospective study was conducted. The work included victims of road accidents with damage to the facial part of the skull between the ages of 18 and 70 years. In total, 150 victims were included in the study between 2000 and 2019. Results of the study and their analysis. The study shows that injuries to the facial skeleton as a result of traffic accidents are relevant. As a result of the study, it was found that as a result of an accident, there is approximately the same number of closed and open injuries. At the same time, open injuries, which could be regarded as medium and severe, were identified in 45 (30%) cases. In addition, data analysis showed that in 30,7% of observations, first aid was provided by random witnesses and/ or relatives of victims who did not have the skills and necessary knowledge to provide such assistance. In addition, an additional 19,3 per cent of first aid was provided by staff of various services who have some knowledge and skills in first aid. This is due to the fact that in the first group, first aid was provided faster than in the second group. The lack of the necessary skills to provide first aid to victims with such an injury can be explained by the rather high percentage of errors, which in the first group amounted to 18,7%, while employees of various services showed good results in providing such assistance and errors were not noted. Key words: primary health care, damage to the facial skeleton, traffic accident.

We propose a new algorithm for curve skeleton computation which differs from previous algorithms by being based on the notion of local separators. The main benefits of this approach are that it is able to capture relatively fine details and that it works robustly on a range of shape representations. Specifically, our method works on shape representations that can be construed as a spatially embedded graphs. Such representations include meshes, volumetric shapes, and graphs computed from point clouds. We describe a simple pipeline where geometric data is initially converted to a graph, optionally simplified, local separators are computed and selected, and finally a skeleton is constructed. We test our pipeline on polygonal meshes, volumetric shapes, and point clouds. Finally, we compare our results to other methods for skeletonization according to performance and quality., Comment: preprint, 25 pages, 17 figures

The term melodic template or skeleton refers to a basic melody which is subject to variation during a music performance. In many oral music tradition, these templates are implicitly passed throughout generations without ever being formalized in a score. In this work, we introduce a new geometric optimization problem, the spanning tube problem, to approximate a melodic template for a set of labeled performance transcriptions corresponding to an specific style in oral music traditions. Given a set of $n$ piecewise linear functions, we solve the problem of finding a continuous function, $f^*$, and a minimum value, $\varepsilon^*$, such that, the vertical segment of length $2\varepsilon^*$ centered at $(x,f^*(x))$ intersects at least $p$ functions ($p\leq n$). The method explored here also provide a novel tool for quantitatively assess the amount of melodic variation which occurs across performances.

Graph convolutional networks (GCN) have received more and more attention in skeleton-based action recognition. Many existing GCN models pay more attention to spatial information and ignore temporal information, but the completion of actions must be accompanied by changes in temporal information. Besides, the channel, spatial, and temporal dimensions often contain redundant information. In this paper, we design a temporal graph convolutional network (FTGCN) module which can concentrate more temporal information and properly balance them for each action. In order to better integrate channel, spatial and temporal information, we propose a unified attention model of the channel, spatial and temporal (CSTA). A basic block containing these two novelties is called FTC-GCN. Extensive experiments on two large-scale datasets, compared with 17 methods on NTU-RGB+D and 8 methods on Kinetics-Skeleton, show that for skeleton-based human action recognition, our method achieves the best performance.

Online action recognition is an important task for human-centered intelligent services. However, it remains a highly challenging problem due to the high varieties and uncertainties of spatial and temporal scales of human actions. In this paper, the following core ideas are proposed to deal with the online action recognition problem. First, we combine spatial and temporal skeleton features to represent human actions, which include not only geometrical features, but also multiscale motion features, such that both spatial and temporal information of the actions are covered. We use an efficient one-dimensional convolutional neural network to fuse spatial and temporal features and train them for action recognition. Second, we propose a group sampling method to combine the previous action frames and current action frames, which are based on the hypothesis that the neighboring frames are largely redundant, and the sampling mechanism ensures that the long-term contextual information is also considered. Third, the skeletons from multiview cameras are fused in a distributed manner, which can improve the human pose accuracy in the case of occlusions. Finally, we propose a Restful style based client-server service architecture to deploy the proposed online action recognition module on the remote server as a public service, such that camera networks for online action recognition can benefit from this architecture due to the limited onboard computational resources. We evaluated our model on the data sets of JHMDB and UT-Kinect, which achieved highly promising accuracy levels of 80.1% and 96.9%, respectively. Our online experiments show that our memory group sampling mechanism is far superior to the traditional sliding window.

Any smooth tropical plane curve contains a distinguished trivalent graph called its skeleton. In 2020 Morrison and Tewari proved that the so-called big face graphs cannot be the skeleta of tropical curves for genus $12$ and greater. In this paper we answer an open question they posed to extend their result to the prism graphs, proving that they are the skeleton of a smooth tropical plane curve precisely when the genus is at most $11$. Our main tool is a classification of lattice polygons with two points than can simultaneously view all others, without having any one point that can observe all others., 11 pages, 13 figures

In skeleton-based action recognition, graph convolutional networks (GCNs), which model human body skeletons using graphical components such as nodes and connections, have recently achieved remarkable performance. While the current state-of-the-art methods for skeleton-based action recognition usually assume that completely observed skeletons will be provided, it is problematic to realize this assumption in real-world scenarios since the captured skeletons may be incomplete or noisy. In this work, we propose a skeleton-based action recognition method that is robust to noise interference for the given skeleton features. The key insight of our approach is to train a model by maximizing the mutual information between normal and noisy skeletons using predictive coding in the latent space. We conducted comprehensive skeleton-based action recognition experiments with defective skeletons using the NTU-RGB+D and Kinetics-Skeleton datasets. The experimental results demonstrate that when the skeleton samples are noisy, our approach achieves outstanding performances compared with the existing state-of-the-art methods.

We study the decidability of k-provability in $$\hbox {PA}$$ PA —the relation ‘being provable in $$\hbox {PA}$$ PA with at most k steps’—and the decidability of the proof-skeleton problem—the problem of deciding if a given formula has a proof that has a given skeleton (the list of axioms and rules that were used). The decidability of k-provability for the usual Hilbert-style formalisation of $$\hbox {PA}$$ PA is still an open problem, but it is known that the proof-skeleton problem is undecidable for that theory. Using new methods, we present a characterisation of some numbers k for which k-provability is decidable, and we present a characterisation of some proof-skeletons for which one can decide whether a formula has a proof whose skeleton is the considered one. These characterisations are natural and parameterised by unification algorithms.

With the increasing availability of human motion data captured in the form of 2D or 3D skeleton sequences, more complex motion recordings need to be processed. In this paper, we focus on similarity-based indexing and efficient retrieval of motion episodes — medium-sized skeleton sequences that consist of multiple semantic actions and correspond to some logical motion unit (e.g. a figure skating performance). As a first step toward efficient retrieval, we apply the motion-word technique to transform spatio-temporal skeleton sequences into compact text-like documents. Based on these documents, we introduce a two-phase retrieval scheme that first finds a set of candidate query results and then re-ranks these candidates with more expensive application-specific methods. We further index the motion-word documents using inverted files, which allows us to retrieve the candidate documents in an efficient and scalable manner. We also propose additional query-reduction techniques that accelerate both the retrieval phases by removing semantically irrelevant parts of the motion query. Experimental evaluation is used to analyze the effects of the individual proposed techniques on the retrieval efficiency and effectiveness.

This paper describes the design, construction, and use of an open-source hardware and software tool intended to help Anatomy and Physiology students test their knowledge of muscle actions and joint movements. Orientation sensors are attached to a model skeleton to turn the skeleton into an interactive, physical model for teaching limb movements. A detailed description of the construction of the tool is provided, as well as the configuration and use of companion software.

With the development of 3D hand pose estimation technologies, skeleton-based dynamic hand gesture recognition has attracted widespread attention. In this paper, we propose a novel framework for skeleton-based dynamic hand gesture recognition. In the spatial perception stream (SP-Stream), we design a compact joints encoding method. It can adaptively select compact joints based on the convex hull of the hand skeleton and encode them into a skeleton image for fully extracting spatial features. Besides, we present a global enhancement module (GEM) to enhance key feature maps. In the temporal perception stream (TP-Stream), we propose a motion perception module (MPM) to enhance the notable movement of hand gestures on X / Y / Z coordinate axes. Experimental results show that the proposed framework performs better than the state-of-the-art methods on two benchmark datasets.

This paper presents a sign language recognition system based on color stream and skeleton points. Several approaches have been established to address sign language recognition problems. However, most of the previous approaches still have poor recognition accuracy. The proposed approach uses Kinect sensor based on color stream and skeleton points from the depth stream to improved recognition accuracy. Techniques within this approach use hand trajectories and hand shapes in combating sign recognition challenges. Therefore, for a particular sign a representative feature vector is extracted, which consists of hand trajectories and hand shapes. A sparse dictionary learning algorithm, Label Consistent K-SVD (LC-KSVD) is applied to obtain a discriminative dictionary. Based on that, the system was further developed to a new classification approach for better results. The proposed system was fairly evaluated based on 21 sign words including one-handed signs and two-handed signs. It was observed that the proposed system gets high recognition accuracy of 98.25%, and obtained an average accuracy of 95.34% for signer independent recognition. Keywords: Sign language, Color stream, Skeleton points, Kinect sensor, Discriminative dictionary.

For partially hyperbolic diffeomorphisms with mostly expanding and mostly contracting centers, we establish a topological structure, called skeleton—a set consisting of finitely many hyperbolic periodic points with maximal cardinality for which there exist no heteroclinic intersections. We build the one-to-one corresponding between periodic points in any skeleton and physical measures. By making perturbations on skeletons, we study the continuity of physical measures with respect to dynamics under $$C^1$$ -topology.

The skeleton, or medial axis, is an important attribute of 2-D shapes. The disk B-spline curve (DBSC) is a skeleton-based parametric freeform 2-D region representation, which is defined in the B-spline form. The DBSC describes not only a 2-D region, which is suitable for describing heterogeneous materials in the region, but also the center curve (skeleton) of the region explicitly, which is suitable for animation, simulation, and recognition. In addition to being useful for error estimation of the B-spline curve, the DBSC can be used in designing and animating freeform 2-D regions. Despite increasing DBSC applications, its theory and fundamentals have not been thoroughly investigated. In this article, we discuss several fundamental properties and algorithms, such as the de Boor algorithm for DBSCs. We first derive the explicit evaluation and derivatives formulas at arbitrary points of a 2-D region (interior and boundary) represented by a DBSC and then provide heterogeneous object representation. We also introduce modeling and interactive heterogeneous object design methods for a DBSC, which consolidates DBSC theory and supports its further applications.

Human gait recognition in a multicamera environment is a challenging task in biometrics because of the presence of the large pose and variations in illumination among different views. In this work, to address the problem of variations in view, we present a novel stacked autoencoder for learning discriminant view‐invariant gait representations. Our autoencoder can efficiently and progressively translate skeleton joint coordinates from any arbitrary view to a common canonical view without requiring the prior estimation of the view angle or covariate type and without losing temporal information. Then, we construct a discriminative gait feature vector by fusing the encoded features with two other spatiotemporal gait features to feed into the main recurrent neural network. Experimental evaluations of the challenging CASIA A and CASIA B gait datasets demonstrate that the proposed approach outperformed other state‐of‐the‐art methods on single‐view gait recognition. In particular, the proposed method achieved 46.31% and 33.86% average correct class recognition on probe set ProbeBG and ProbeCL, respectively, of the CASIA B dataset while considering the view variation; this is 0.3% and 30.68% higher than previous best‐performing methods. Furthermore, in cross‐view recognition, our method shows better results over other state‐of‐the‐art methods when the view‐angle variation is large than 36°.

Humans regularly interact with their surrounding objects. Such interactions often result in strongly correlated motions between humans and the interacting objects. We thus ask: “Is it possible to infer object properties from skeletal motion alone, even without seeing the interacting object itself?” In this paper, we present a fine-grained action recognition method that learns toinfersuch latent object properties from human interaction motion alone. This inference allows us todisentanglethe motion from the object property andtransferobject properties to a given motion. We collected a large number of videos and 3D skeletal motions of performing actors using an inertial motion capture device. We analyzed similar actions and learned subtle differences between them to reveal latent properties of the interacting objects. In particular, we learned to identify the interacting object, by estimating its weight, or its spillability. Our results clearly demonstrate that motions and interacting objects are highly correlated and that related object latent properties can be inferred from 3D skeleton sequences alone, leading to new synthesis possibilities for motions involving human interaction. Our dataset is available athttp://vcc.szu.edu.cn/research/2020/IT.html.

Inspiration and motivation for transforming the education system can be found in our human stories of the past, present, and future.

Skeleton‐based action recognition has recently attracted a lot of research interests due to its advantage in computational efficiency. Some recent work building upon Graph Convolutional Networks (GCNs) has shown promising performance in this task by modelling intrinsic spatial correlations between skeleton joints. However, these methods only consider local properties of action sequences in the spatial‐temporal domain, and consequently, are limited in distinguishing complex actions with similar local movements. To address this problem, a novel tri‐attention module (TAM) is proposed to guide GCNs to perceive significant variations across local movements. Specifically, the devised TAM is implemented in three steps: i) A dimension permuting unit is proposed to characterise skeleton action sequences in three different domains: body poses, joint trajectories, and evolving projections. ii) A global statistical modelling unit is introduced to aggregate the first‐order and second‐order properties of global contexts to perceive the significant movement variations of each domain. iii) A fusion unit is presented to integrate the features of these three domains together and leverage as orientation for graph convolution at each layer. Through these three steps, significant‐variation frames, joints, and channels can be enhanced. We conduct extensive experiments on two large‐scale benchmark datasets, NTU RGB‐D and Kinetics‐Skeleton. Experimental results demonstrate that the proposed TAM can be easily plugged into existing GCNs and achieve comparable performance with the state‐of‐the‐art methods.

In industry, today’s approach to assembly design is still largely based on a bottom-up approach which, in contrast with the most advanced top-down techniques, is unfit to deal with very large and complex products. The reason for this lies in the high number of relationships to be established between parts and in the lack of a high-level control of the assembly design. This makes the management of design changes a labor-intensive process and the capture of design intent difficult to achieve. The paper, referring to the most advanced research fields of Concurrent Engineering and Knowledge-Based Engineering, focuses on a top-down modelling approach based on skeleton, which constitutes the most natural but still scarcely exploited way to attain a high reactivity to design modifications. Through the application of suitable methodologies, such as that one for a SKeLeton geometry–based Assembly Context Definition (SKL-ACD), the skeleton is also able to capture and codify assembly process engineering information since the early phases of the product development process. With the purpose of promoting the knowledge of these skeleton-based modelling techniques, that have a great relevance for training professional, technical and mechanical engineers, this paper implements the SKL-ACD methodology to an industrial case study in order to identify, with a unique and repeatable workflow, the reference geometrical entities and the mutual relationships to embed into the product skeleton. The skeleton types and the related fields of use are also described, placing particular emphasis on problems or shortcomings still not resolved, especially in consideration of the need to assist the designer in defining the impact of a parameter on assembly modification and in avoiding loops while defining formulas. A new tool, in the form of a multilayer graph, is finally proposed that is able to display and differentiate clearly the formulas, the design parameters and the impact of their modification on skeleton entities and members of the assembly.

Action recognition based on 3D skeleton data has attracted much attention due to its wide application, and it is one of the most popular research topics in computer vision. The 3D skeleton data is an effective representation of motion dynamics and is not easily affected by light, scene variation, etc. Previous research on action recognition has mainly focused on video or RGB data methods. In recent years, the advantages of combining skeleton data and deep learning have been gradually demonstrated, many impressive methods have been proposed, especially GCN‐based methods. In this survey, we first introduce the development process of 3D skeleton‐data action recognition and the classification of graph convolutional network, then introduce the commonly used NTU RGB + D and NTU RGB + D 120 datasets. Finally, a detailed review of existing variants of three mainstream technologies is provided based on deep learning and their performance was compared from three dimensions. To the best of our knowledge, this is the first research to integrate the research of GCN‐based method and its various evolutionary methods. Comparative investigation of existing variants of research in action‐recognition task from different perspectives is made, a generic framework is described, state‐of‐the‐art practices are summarized, and the emerging trends of this topic are explored.

Exotic species of animals have recently become more and more popular and widespread; the approach to their diagnosis of diseases and treatment has its own nuances. Despite the similarity of species, there are differences that need to be taken into account. One of the fundamental problems of comparative anatomy is the disclosure of general patterns and specific features of the skeleton of animals. Despite the available detailed work in this direction, many questions concerning this problem still remain open and require further study.

Although various studies based on thermal images have been conducted, few studies have focused on the simultaneous extraction of joints and skeleton information of an object from a thermal image, and performed human action recognition using this information. Unlike in the case of visible light images, performing joint detection and skeleton generation on thermal images often leads to the complete disappearance of spatial information such as joints. In this case, it is extremely difficult to extract joints information from the object. Moreover, the accuracy of action recognition is significantly reduced owing to this issue. Therefore, a new method to extract joints and skeleton information is proposed in this study to address these issues. In the proposed method, an original 1-channel thermal image was converted into a 3-channel thermal image and then the images were combined to improve the extraction performance. A generative adversarial network (GAN) was used in the proposed method for extracting joints and skeleton information. In addition, research to recognize various human actions was conducted using the joints and skeleton information extracted by this method. The proposed human action recognition is performed by combining a convolutional neural network (CNN) and long short-term memory (LSTM). As a result of the experiments using self-collected and open data, it was found that the method proposed in this study shows good performance compared to other state-of-the-art methods.

It is known that the kinematics of the human body skeleton reveals valuable information in action recognition. Recently, modeling skeletons as spatio-temporal graphs with Graph Convolutional Networks (GCNs) has been reported to solidly advance the state-of-the-art performance. However, GCN-based approaches exclusively learn from raw skeleton data, and are expected to extract the inherent structural information on their own. This paper describes REGINA, introducing a novel way to REasoning Graph convolutional networks IN Human Action recognition. The rationale is to provide to the GCNs additional knowledge about the skeleton data, obtained by handcrafted features, in order to facilitate the learning process, while guaranteeing that it remains fully trainable in an end-to-end manner. The challenge is to capture complementary information over the dynamics between consecutive frames, which is the key information extracted by state-of-the-art GCN techniques. Moreover, the proposed strategy can be easily integrated in the existing GCN-based methods, which we also regard positively. Our experiments were carried out in well known action recognition datasets and enabled to conclude that REGINA contributes for solid improvements in performance when incorporated to other GCN-based approaches, without any other adjustment regarding the original method. For reproducibility, the REGINA code and all the experiments carried out will be publicly available at this https URL.

Action recognition plays an important role in video understanding, and the dynamic changes of human bones provide important information for it. In order to solve the problem that the source point can only obtain the characteristics of adjacent nodes and the static structure of the skeleton diagram can not meet the adaptive requirements, this paper proposes an Inception structure and dynamic skeleton diagram based on the convolutional neural network, namely, the multi-filter dynamic graph convolutional neural network. The method can not only enable the source point to obtain the characteristics of distant nodes, but also can use the dynamic skeleton diagram structure to generate different body associations for different actions, which improves the adaptability and accuracy of the model and achieves better results.

The components with lattice structure as filling unit have great application potential in aerospace and other fields. The failure of the lattice structure directly affects the functional characteristics of the parts filled with the lattice structure. Aiming at the problem that it is difficult to evaluate the deformation degree of metal lattice structure after mechanical loading in additive manufacturing, firstly, the point cloud model of lattice structure is obtained by using CT scanning and three-dimensional reconstruction, and then the skeleton of lattice structure is automatically extracted based on ${L_1}$ median algorithm. Finally, the deformation angle of rods is measured to evaluate the degree of deformation and damage of parts. In this paper, the deformation evaluation of the rods of the BCC lattice is discussed. The experimental results show that the proposed skeleton extraction technology achieves the evaluation of lattice structure deformation. The experimental model is extended to BCC lattice structure with unit cell number of $n \times n \times n$. When the ratio of the rods with more than 40% severe deformation to all rods in the lattice structure reaches $(2n - 1)/2{n^2}$ it indicates that the lattice structure has undergone a large degree of deformation and should not continue to serve.

Current researches of action recognition which focus on single-view and multi-view recognition can hardly satisfy the requirements of human-robot interaction (HRI) applications for recognizing human actions from arbitrary views. Arbitrary-view recognition is still a challenging issue due to view changes and visual occlusions. In addition, the lack of datasets also sets up barriers. To provide data for arbitrary-view action recognition, we collect a new large-scale RGB-D action dataset for arbitrary-view action analysis, including RGB videos, depth and skeleton sequences. The dataset includes action samples captured in 8 fixed viewpoints and varying-view sequences which cover the entire 360° view angles. In total, 118 persons are invited to act 40 action categories. Our dataset involves more participants, more viewpoints and a large number of samples. More importantly, it is the first dataset containing the entire 360° varying-view sequences. The dataset provides sufficient data for multi-view, cross-view and arbitrary-view action analysis. Besides, we propose a View-guided Skeleton CNN (VS-CNN) to tackle the problem of arbitrary-view action recognition. Experiment results show that the VS-CNN achieves superior performance, and our dataset provides valuable but challenging data for the evaluation of arbitrary-view recognition.

Convolutional neural networks (CNNs) can effectively handle grid-structured data but not dynamic skeletons, which are usually expressed as graph structures. In this study, we first propose a skeleton-based square grid (SSG) for transforming dynamic skeletons into three-dimensional (3D) grid-structured data so that CNNs can be applied to such data. Each SSG contains a joint-based square grid (JSG) and a rigid-based square grid (RSG) based on intrinsic and extrinsic dependencies of various body parts, respectively. Next, to enhance the ability of deep features to capture the correlations among 3D grid-structured data, a two-stream 3D CNN is constructed to learn spatiotemporal features using the JSG and RSG sequences. Finally, we introduce a soft attention model that selectively focuses on the informative body parts in the skeleton sequences. We validate our model in terms of action recognition using three datasets: NTU RGB+D, Kinetics Motion, and SBU Kinect Interaction datasets. Our experimental results demonstrate the effectiveness of the proposed approach as well as its superior performance when compared with those of state-of-the-art methods.

Action recognition has been used in a wide range of applications such as human-computer interaction, intelligent video surveillance systems, video summarization, and robotics. Recognizing action is important for intelligent agents to understand, learn and interact with the environment. The recent technology that allows the acquisition of RGB+D and 3D skeleton data and a deep learning model's development significantly increases the action recognition model's performance. In this research, hierarchical Long Sort-Term Memory is proposed to recognize action based on 3D skeleton joints from Kinect sensor. The model uses the 3D axis of skeleton joints and groups each joint in the axis into parts, namely, spine, left and right arm, left and right hand, and left and right leg. To fit the hierarchically structured layers of LSTM, the parts are concatenated into spine, arms, hands, and legs and then concatenated into the body. The model crosses the body in each axis into a single final body and fed to the final layer to classify the action. The performance is measured using cross-view and cross-subject evaluation and achieves accuracy 0.854 and 0.837, respectively, from the 10 action classes of the NTU RGB+D dataset.

In skeleton-based human action recognition domain, the methods based on graph convolution networks have great success recently. However, most graphical neural networks consider the skeleton as a spatiotemporally uncorrelated graph and rely on a predetermined adjacency matrix, ignoring the spatiotemporal relevance of human actions and taking up significant computational costs. Meanwhile, the methods use graph convolution to focus too much on the neighboring nodes of the joints and ignore the totality of the action. In this work, we propose a lightweight but efficient neural network called NLB-ACSE based on the Graph Convolutional Network (GCN). Our model consists of two large branches: non-local block branch that focuses on the long distance features and adaptive cross-spacetime edge branch that focuses on the short distance features. Both branches extract information across time and space, and focus on long and short information. Some simple but effective strategies also are applied to our model, such as semantics, maxpooling, and fusion inputs, which have small parameter burden but obtain a higher accuracy on ablation study. The proposed method with an order of magnitude smaller size than most previous papers is evaluated on three large datasets, NTU60, NTU120, and Northwesten-UCLA. The experimental results show that our method achieves the state-of-the-art performance.

We develop a cylindrical shape decomposition (CSD) algorithm to decompose an object, a union of several tubular structures, into its semantic components. We decompose the object using its curve skeleton and restricted translational sweeps. For that, CSD partitions the curve skeleton into maximal-length sub-skeletons over an orientation cost, each sub-skeleton corresponds to a semantic component. To find the intersection of the tubular components, CSD translationally sweeps the object in decomposition intervals to identify critical points at which the shape of the object changes substantially. CSD cuts the object at critical points and assigns the same label to parts along the same sub-skeleton, thereby constructing a semantic component. The proposed method further reconstructs the acquired semantic components at the intersection of object parts using generalized cylinders. We apply CSD for segmenting axons in large 3D electron microscopy images and decomposing vascular networks and synthetic objects. We show that our proposal is robust to severe surface noise and outperforms state-of-the-art decomposition techniques in its applications.

The number of the elderly person is increasing every year. Fall detection of the elderly has become an important research topic. Fall detection based on image processing is considered as a good solution. However, algorithms based on motion recognition are difficult to distinguish between a person who has fallen and a person who is sleeping. This research tracks and analyzes the motion speed of human joints, which improves the accuracy of fall detection. The experimental results prove that this method effectively distinguishes falling and sleeping.

Skeleton-based action recognition is a typical classification problem which plays a significant role in human-computer interaction and video understanding. Since a human skeleton has natural graphic features, methods based on graph convolutional networks (GCN) are widely applied in skeleton-based action recognition. Previous studies mainly focus on structural links in GCN to generate high-level features of human skeleton. However, low-level features are also important in many applications. For instance, low-level edge gradient and color information are important for image classification. This paper introduces a multi-branches structure to capture different low-level features of human skeleton. We combine both high-level and low-level features to recognize human action. We validate our method in action recognition with two skeleton datasets, NTU-RGB+D and Kinetics. Experiment results indicate that the proposed method achieves considerable improvement over some state-of-the-art methods.

It is well understood that a supercritical superprocess is equal in law to a discrete Markov branching process whose genealogy is dressed in a Poissonian way with immigration which initiates subcritical superprocesses. The Markov branching process corresponds to the genealogical description ofprolific individuals, that is, individuals who produce eternal genealogical lines of descent, and is often referred to as theskeletonorbackboneof the original superprocess. The Poissonian dressing along the skeleton may be considered to be the remaining non-prolific genealogical mass in the superprocess. Such skeletal decompositions are equally well understood for continuous-state branching processes (CSBP).In a previous article [16] we developed an SDE approach to study the skeletal representation of CSBPs, which provided a common framework for the skeletal decompositions of supercritical and (sub)critical CSBPs. It also helped us to understand how the skeleton thins down onto one infinite line of descent when conditioning on survival until larger and larger times, and eventually forever.Here our main motivation is to show the robustness of the SDE approach by expanding it to the spatial setting of superprocesses. The current article only considers supercritical superprocesses, leaving the subcritical case open.

Skeleton-based human-action-recognition (SBHAR) has wide applications in cognitive science and automatic surveillance. However, the most challenging and crucial task of the skeleton-based human-action-recognition (SBHAR) is a significant view variation while capturing the data. In this area, a significant amount of satisfactory work has already been done, which include the Red Green Blue (RGB) data method. The performance of the SBHAR is also affected by the various factors such as video frame setting, view variations in motion, different backgrounds and inter-personal differences. In this survey, we explicitly address these challenges and provide a complete overview of advancement in this field. The deep learning method has been used in this field for a long time, but so far, no research has fully demonstrated its usefulness. In this paper, we first highlight the need for action recognition and significance of 3D skeleton data and finally, we survey the largest 3D skeleton dataset, i.e. NTU-RGB+D and its new version NTU-RGB+D 120.

The total synthesis of (−)-sigillin A, a highly chlorinated and oxygenated octahydroisocoumarin, is described herein. A hexahydroisocoumarin skeleton was constructed from (R)-4-(trichloromethyl)oxetan-2-one in seven steps. Its unique manganese oxidation provided an enone as the key intermediate of sigillin A. Stereoselective installation of two hydroxy groups and formation of gem-dichloroalkene from the corresponding ketone led to the total synthesis of (−)-sigillin A in a total of 16 steps.

Thiazole derivatives represent an important class of azole heterocycles with a broad spectrum of biological activity and, therefore, the synthesis of these compounds is of remarkable concern. We present here practical and reliable protocol for synthesis of some 5-acylthiazoles and demonstrate their utility in the preparation of several new series of thiazole-containing building blocks through transformation of 5-acyl function. Specifically, thiazole-based alcohols, oximes, primary, and secondary amines were successively synthesized in good to excellent yields. The chemical structures of obtained compounds were confirmed by 1H and 13C NMR-spectroscopy, elemental analysis, and mass-spectrometry.