Transformation Parameters Research Articles

Characteristics of the GLLE Family of Lifetime Distributions, and Applications

ABSTRACT. A Generalized Log-Logistic (GLL) family of lifetime distributions is one in which any pair of distributions are related through a GLL transformation, for some (non-negative) value of the transformation parameter (the odds function of the second distribution is the power of the odds function of the first distribution). We consider GLL families generated from an exponential distribution. We derive some useful characteristics of these distributions; moreover, we discuss the hazard rates and Kullback-Leibler divergence, then illustrate the usefulness of this distribution family by fitting real data sets.

A Drone-Based Structure from Motion Survey, Topographic Data, and Terrestrial Laser Scanning Acquisitions for the Floodgate Gaps Deformation Monitoring of the Modulo Sperimentale Elettromeccanico System (Venice, Italy)

The structural deformation monitoring of civil infrastructures can be performed using different geomatic techniques: topographic measurements with total stations and levels, TLS (terrestrial laser scanning) acquisitions, and drone-based SfM (structure from motion) photogrammetric surveys, among others, can be applied. In this work, these techniques are used for the floodgate gaps and the rubber joints deformation monitoring of the MOSE system (Modulo Sperimentale Elettromeccanico), the civil infrastructure that protects Venice and its lagoon (Italy) from high waters. Since the floodgates are submerged most of the time and cannot be directly measured and monitored using high-precision data, topographic surveys were performed in accessible underwater tunnels. In this way, after the calculation of the coordinates of some reference points, the coordinates of the floodgate corners were estimated knowing the geometric characteristics of the system. A specific activity required the acquisition of the TLS scans of the stairwells in the shoulder structures of the Treporti barrier because many of the reference points fixed on the structures were lost during the placement of elements on the seabed. They were replaced with new points whose coordinates in the project/as-built reference system were calculated by applying the Procrustean algorithm by means of homologous points. The procedure allowed the estimation of the transformation parameters with maximum residuals of less than 2.5 cm, a value in agreement with the approximation of the real concrete structures built. Using the obtained parameters, the coordinates of the new reference points were calculated in the project reference system. Once the 3D orientation of all caissons in the barrier was reconstructed, the widths of the floodgate gaps were estimated and compared with the designed values and over time. The obtained values were validated in the Treporti barrier using a drone-based SfM photogrammetric survey of the eight raised floodgates, starting from the east shoulder caisson. The comparison between floodgate gaps estimated from topographic and TLS surveys, and those obtained from measurements on the 3D photogrammetric model, provided a maximum difference of 1.6 cm.

Geometry-Aware Enhanced Mutual-Supervised Point Elimination with Overlapping Mask Contrastive Learning for Partitial Point Cloud Registration

Point cloud registration is one of the fundamental tasks in computer vision, but faces challenges under low overlap conditions. Recent approaches use transformers and overlapping masks to improve perception, but mask learning only considers Euclidean distances between features, ignores mismatches caused by fuzzy geometric structures, and is often computationally inefficient. To address these issues, we introduce a novel matching framework. Firstly, we fuse adaptive graph convolution with PPF features to obtain rich feature perception. Subsequently, we construct a PGT framework that uses GeoTransformer and combines it with location information encoding to enhance the geometry perception between source and target clouds. In addition, we improve the visibility of overlapping regions through information exchange and the AIS module, aiming at subsequent keypoint extraction, preserving points with distinct geometrical structures while suppressing the influence of non-overlapping regions to improve computational efficiency. Finally, the mask is refined through contrast learning to preserve geometric and distance similarity, which helps to compute the transformation parameters more accurately. We have conducted comprehensive experiments on synthetic and real-world scene datasets, demonstrating superior registration performance compared to recent deep learning methods. Our approach shows remarkable improvements of 68.21% in RRMSE and 76.31% in tRMSE on synthetic data, while also excelling in real-world scenarios with enhancements of 76.46% in RRMSE and 45.16% in tRMSE.

Accuracy and efficiency of 2D/3D single-vertebra spine navigation registration method based on dual-view feature fusion

Objective: To investigate the accuracy and efficiency of spine 2D/3D preoperative CT and intraoperative X-ray registration through a framework for spine 2D/3D single-vertebra navigation registration based on the fusion of dual-position image features. Methods: The preoperative CT and intraoperative anteroposterior (AP) and lateral (LAT) X-ray images of 140 lumbar spine patients who visited Huashan Hospital Affiliated to Fudan University from January 2020 to December 2023 were selected. In order to achieve rapid and high-precision single vertebra registration in clinical orthopedic surgery, a designed transformation parameter feature extraction module combined with a lightweight module of channel and spatial attention (CBAM) was used to accurately extract the local single vertebra image transformation information. Subsequently, the fusion regression module was used to complement the features of the anterior posterior (AP) and lateral (LAT) images to improve the accuracy of the registration parameter regression. Two 1×1 convolutions were used to reduce the parameter calculation amount, improve computational efficiency, and accelerate intraoperative registration time. Finally, the regression module outputed the final transformation parameters. Comparative experiments were conducted using traditional iterative methods (Opt-MI, Opt-NCC, Opt-C2F) and existing deep learning methods convolutional neural network (CNN) as control group. The registration accuracy (mRPD), registration time, and registration success rate were compared among the iterative methods. Results: Through experiments on real CT data, the image-guided registration accuracy of the proposed method was verified. The method achieved a registration accuracy of (0.81±0.41) mm in the mRPD metric, a rotational angle error of 0.57°±0.24°, and a translation error of (0.41±0.21) mm. Through experimental comparisons on mainstream models, the selected DenseNet alignment accuracy was significantly better than ResNet as well as VGG (both P<0.05). Compared to existing deep learning methods [mRPD: (2.97±0.99) mm, rotational angle error: 2.64°±0.54°, translation error: (2.15±0.41) mm, registration time: (0.03±0.05) seconds], the proposed method significantly improved registration accuracy (all P<0.05). The registration success rate reached 97%, with an average single registration time of only (0.04±0.02) seconds. Compared to traditional iterative methods [mRPD: (0.78±0.26) mm, rotational angle error: 0.84°±0.57°, translation error: (1.05±0.28) mm, registration time: (35.5±10.5) seconds], registration efficiency of the proposed method was significantly improved (all P<0.05). The dual-position study also compensated for the limitations in the single-view perspective, and significantly outperforms both the front and side single-view perspectives in terms of positional transformation parameter errors (both P<0.05). Conclusion: Compared to existing methods, the proposed CT and X-ray registration method significantly reduces registration time while maintaining high registration accuracy, achieving efficient and precise single vertebra registration.

Determination of Optimum Coordinate Transformation Parameters for GNSS and LiDAR-Based Localization in Automated Vehicles

Determination of Optimum Coordinate Transformation Parameters for GNSS and LiDAR-Based Localization in Automated Vehicles

Low‐light image enhancement via lightweight custom non‐linear transform network

AbstractConvolutional neural network (CNN)‐based models have shown significant progress in low light image enhancement. However, many existing models possess a large number of parameters, making them unsuitable for deployment on terminal devices. Moreover, adjustments to brightness, contrast, and colour in images are often non‐linear, and convolution is not the best at capturing complex non‐linear relationships in image data. To address these issues, a model based on an end‐to‐end custom non‐linear transform network (CNTNet) is proposed. CNTNet combines a custom non‐linear transform layer with CNN layers to achieve image contrast and detail enhancement. The CNT layer in this model introduces transformation parameters at multiple scales to manipulate input images within various ranges. CNTNet progressively processes images by stacking multiple non‐linear transform layers and convolutional layers while integrating residual connections to capture and leverage subtle image features. The final output is generated through convolutional layers to obtain enhanced images. Experimental results of CNTNet demonstrate that, while maintaining a comparable level of image quality evaluation metrics to mainstream models, it significantly reduces the parameter count to only 2K.

Optimization of transformation conditions of the yeast &lt;i&gt;Saccharomyces cerevisiae&lt;/i&gt; to determine coregulators of the transcription factor NIN in a yeast two-hybrid system

BACKGROUND: The method of protein-protein interaction analysis using the yeast two-hybrid system in Saccharomyces cerevisiae cells is used to search for protein coregulators. This method is also used for mass screening of libraries of cloned fragments of complementary DNA (cDNA) that are translated in the cell. The key factor in the success of such screening is the level of efficiency of yeast cell transformation, since the resolution of the analysis is based on this. AIM: The aim of this study is to search for optimal parameters for chemical transformation of yeast cells to increase the resolution of cDNA library screening. MATERIALS AND METHODS: Plasmids pDEST22 and pDEST32 were used for chemical transformation of the yeast strain S. cerevisiae pJ69-4A. For screening, a cDNA library was used, obtained on the basis of mRNA isolated from the roots of pea Pisum sativum cultivar Finale, inoculated with rhizobia. RESULTS: Factors influencing the efficiency of transformation were identified. Among them are the molecular weight of polyethyleneglycol used for transformation, as well as the number of cell division cycles that the culture undergoes. The effect of the number and size of plasmids used in transformation was also shown. Using the optimized protocol, a cDNA library was successfully screened to find coregulators of the NIN transcription factor. CONCLUSIONS: Based on the data obtained, optimal parameters were determined that allow achieving a high level of competence in yeast cells. The use of the described protocol allowed for successful screening of the library to identify coregulators of the NIN transcription factor.

A robust correspondence-based registration method for large-scale outdoor point cloud

ABSTRACT Large-scale outdoor point cloud registration is essential for the 3D reconstruction of outdoor scenes. Its central objective is to achieve accurate point cloud registration by determining accurate spatial transformation parameters. While feature-based methods eliminate the need for initial position estimation, they encounter challenges in handling high outlier rates. Therefore, a method capable of effectively managing outliers is crucial for enhancing the efficiency and accuracy of large-scale outdoor point cloud registration. This paper introduces the maximal clique with adaptive voting (MCAV) method, which leverages graph-based inlier compatibility to optimize potential matches. MCAV employs adaptive parameter voting (APV) to enhance computational efficiency, demonstrating significant speedup characteristics in datasets with a significant number of inliers. To further reduce outliers in potential matches, we integrate Black-Rangarajan Duality (BRD) and graduated non-convexity (GNC) into the truncated least squares (TLS) framework (BG-TLS). Accordingly, we propose the efficient BG-TLS (EBG-TLS) method for computing the registration model. Comparative analyses with traditional and deep learning-based methods across various real-world environments demonstrate that the proposed method outperforms existing algorithms in terms of rotation error, translation error, and efficiency, particularly in complex, high-noise settings. This method finds broad applications in geospatial mapping and surveying, autonomous navigation, and environmental monitoring.

Introduction of a Framework for the Integration of a Kinematic Robot Arm Model in an Artificial Neural Network - Extended Kalman Filter Approach

The aim of this paper is to introduce a model in which systematic effects can be assigned according to their origin or mode of action. The approach intends to improve the positional accuracy of a robot arm. We show the impact of unaccounted model biases on estimated parameters when applying sequential approaches and conclude the necessity of jointly determining all influencing variables. Therefore, we propose a simultaneous estimation of transformation parameters, robot’s kinematic parameters and non-geometric parameters modelled by an artificial neural network (ANN) in further consequence. Thus, the main contribution of this paper is a new approach of the simultaneous estimation of the geometric and non-geometric components of a robot arm model. The integration of the geometric model (transformations, kinematic robot model) with the non-geometric one (ANN) is realised in the extended Kalman filter. The functionality of the algorithm has been proven on simulated data. The adaptive behaviour of machine learning approaches is made possible by an additional iteration of the ANN. The initialisation of the ANN parameters must not deviate from the nominal parameters by more than 10% so that the ANN can learn the non-geometric part. In this setup, the robot arm position corrections are reduced by 32.5%. A final sensitivity analysis proves the estimability of most kinematic parameters in the course of a future adaptive extension of the approach.

High-resolution multicomponent LFM parameter estimation based on deep learning

This paper addresses the complex challenge of parameter estimation in multi-component Linear Frequency Modulation (LFM) signals by introducing an innovative approach to high-resolution Fractional Fourier Transform (FrFT) parameter estimation, facilitated by convolutional neural networks. Initially, it analyzes the issues of peak shifts and the masking of weaker components due to spectral overlap in the FrFT domain of multi-component LFM signals. Convolutional neural networks are then employed to train and achieve high-resolution representations of FrFT parameters. Specifically, convolutional modules with residual structures are utilized to learn coarse features, while a weighted attention mechanism refines independent features across both channel and spatial dimensions. This approach effectively addresses the challenges posed by spectral peak overlap and frequency shifts in multi-component LFM signals, thereby enhancing the quality of high-resolution parameter estimation. Experimental results demonstrate that the proposed method significantly outperforms traditional methods in processing multi-component LFM signals. Moreover, it exhibits robust detection capabilities for both weak and compact components, thereby underscoring its potential applicability in the field of complex signal processing.

Bending stiffness analysis of regular polyhedron sandwich structures

Abstract As a new type of sandwich structure, regular polyhedron sandwich structure has excellent structure performances such as high bending resistance and energy-absorbing features. However, the current design process of regular polyhedron sandwich structures often ignores the influence of unit-cell parameters on structural bending stiffness. Based on origami geometry, this study analyzes the geometric characteristics of polyhedron unit-cell structures. A simulation model was established to analyze the bending performance of regular polyhedron sandwich structures. The influence of the number of polyhedron surfaces and similarity transformation parameters on bending stiffness was analyzed by finite element software. Through finite simulation analysis, the influence graph of bending performance in the impact of various parameters was obtained. This analysis provides valuable insights for the optimization design of regular polyhedron sandwich structures.

Deep-UEO: Empowering medical image registration with hybrid strategy based on deep learning and united equilibrium optimizer

Traditional optimization methods encounter challenges in determining the optimal geometric transformation parameters and often lacking to achieve global optimum which leads to extended computational time required for convergence. To address this challenge, a dual-phase strategy is proposed for multimodal biomedical image registration by amalgamating the united equilibrium optimizer (UEO) with Visual Geometry Group (VGG-19). In first phase, the input images are aligned by identifying the optimal values of rigid transformation parameters through UEO, with mutual information (MI) maximization serving as an objective function. In second phase, VGG19 is employed to enhance the reliability of resulting registered image obtained through UEO optimization for extracting both the low- and high-level features of reference and target images. Following this, dynamic inlier selection is employed to refine the matching process, integrating expectation maximization for iterative updating of inlier selection, thus improving registration accuracy. Finally, thin plate spline interpolation is utilized to calculate the transformation matrix, ensuring precise alignment between reference and UEO registered image. Extensive experiments are conducted on multimodal medical images sourced from publicly available repositories and clinical(in-house) datasets to validate the reliability of the proposed scheme. These experiments showcase substantial improvements in RMSE from 22.21094 to 10.983654, SSIM from 0.7190832 to 0.8409321, PSNR from 30.176044 to 34.095471, and CC from 0.9304633 to 0.9809432, outperforming state-of-the-art methods.

Monte Carlo inference for semiparametric Bayesian regression

Data transformations are essential for broad applicability of parametric regression models. However, for Bayesian analysis, joint inference of the transformation and model parameters typically involves restrictive parametric transformations or nonparametric representations that are computationally inefficient and cumbersome for implementation and theoretical analysis, which limits their usability in practice. This paper introduces a simple, general, and efficient strategy for joint posterior inference of an unknown transformation and all regression model parameters. The proposed approach directly targets the posterior distribution of the transformation by linking it with the marginal distributions of the independent and dependent variables, and then deploys a Bayesian nonparametric model via the Bayesian bootstrap. Crucially, this approach delivers (1) joint posterior consistency under general conditions, including multiple model misspecifications, and (2) efficient Monte Carlo (not Markov chain Monte Carlo) inference for the transformation and all parameters for important special cases. These tools apply across a variety of data domains, including real-valued, positive, and compactly-supported data. Simulation studies and an empirical application demonstrate the effectiveness and efficiency of this strategy for semiparametric Bayesian analysis with linear models, quantile regression, and Gaussian processes. The R package SeBR is available on CRAN.

Non-coplanar CBCT image reconstruction using a generative adversarial network for non-coplanar radiotherapy.

To develop a non-coplanar cone-beam computed tomography (CBCT) image reconstruction method using projections within a limited angle range for non-coplanar radiotherapy. A generative adversarial network (GAN) was utilized to reconstruct non-coplanar CBCT images. Data from 40 patients with brain tumors and two head phantoms were used in this study. In the training stage, the generator of the GAN used coplanar CBCT and non-coplanar projections as the input, and an encoder with a dual-branch structure was utilized to extract features from the coplanar CBCT and non-coplanar projections separately. Non-coplanar CBCT images were then reconstructed using a decoder by combining the extracted features. To improve the reconstruction accuracy of the image details, the generator was adversarially trained using a patch-based convolutional neural network as the discriminator. A newly designed joint loss was used to improve the global structure consistency rather than the conventional GAN loss. The proposed model was evaluated using data from eight patients and two phantoms at four couch angles (±45°,±90°) that are most commonly used for brain non-coplanar radiotherapy in our department. The reconstructed accuracy was evaluated by calculating the root mean square error (RMSE) and an overall registration error ε, computed by integrating the rigid transformation parameters. In both patient data and phantom data studies, the qualitative and quantitative metrics results indicated that±45° couch angle models performed better than±90° couch angle models and had statistical differences. In the patient data study, the mean RMSE and ε values of couch angle at 45°, -45°, 90°, and -90° were 58.5 HU and 0.42mm, 56.8 HU and 0.41mm, 73.6 HU and 0.48mm, and 65.3 HU and 0.46mm, respectively. In the phantom data study, the mean RMSE and ε values of couch angle at 45°, -45°, 90°, and -90° were 91.2 HU and 0.46mm, 95.0 HU and 0.45mm, 114.6 HU and 0.58mm, and 102.9 HU and 0.52mm, respectively. The results show that the reconstructed non-coplanar CBCT images can potentially enable intra-treatment three-dimensional position verification for non-coplanar radiotherapy.

Likelihood-based inference for semi-parametric transformation cure models with interval censored data

A simple yet effective way of modeling survival data with cure fraction is by considering Box-Cox transformation cure model (BCTM) that unifies mixture and promotion time cure models. In this article, we numerically study the statistical properties of the BCTM when applied to interval censored data. Time-to-events associated with susceptible subjects are modeled through proportional hazards structure that allows for non-homogeneity across subjects, where the baseline hazard function is estimated by distribution-free piecewise linear function with varied degrees of non-parametricity. Due to missing cured statuses for right censored subjects, maximum likelihood estimates of model parameters are obtained by developing an expectation-maximization (EM) algorithm. Under the EM framework, the conditional expectation of the complete data log-likelihood function is maximized by considering all parameters (including the Box-Cox transformation parameter α) simultaneously, in contrast to conventional profile-likelihood technique of estimating α. The robustness and accuracy of the model and estimation method are established through a detailed simulation study under various parameter settings, and an analysis of real-life data obtained from a smoking cessation study.

Study on the forming mechanism and evolutionary pattern of stagnant region in mechanical scratching

In this paper, a combination of theoretical modeling, finite element simulation, and experimental methods is employed to investigate the forming mechanism and evolutionary pattern of the stagnant region during mechanical scratching with a diamond wedge tool. The study is structured as follows: Firstly, a theoretical calculation model for the geometric parameters of the stagnant region on the formed groove surface is established based on the contact friction partition mechanism and slip-line field theory. The model indicates that the geometric parameters lB-sg, lV-sg, and ∆lsg of the stagnant region are determined by the length of the stagnant region lp-sg in the plastic flow plane and the transformation parameters. Secondly, the formation process of the stagnant region in mechanical scratching is investigated using an orthogonal cutting simulation model with a negative rake angle tool. The results reveal that the stagnant region is a plastic deformation region formed due to the geometrical characteristics of the negative front surface of the scratching tool and its excessive extrusion, which leads to the formation of adhesive friction within the material. Thirdly, the characteristics of the stagnant region are determined through scratching experiments. Compared to the material in the plastic flow region, the material within the stagnant region exhibits finer and denser microstructures, reduced surface hardening peaks and hardened layer depths, and significantly improved surface roughness. Finally, the evolutionary pattern of the stagnant region under the influence of scratching processing parameters is examined based on the theoretical calculation model of the geometric parameters and the scratching experiment. The findings indicate that as the wedge angle of the scratching tool decreases, the relief angle increases, the absolute value of the rotation angle around the Y-axis decreases, the scratching speed decreases, and the material’s plastic adherence improves, the PI/k value decreases, the lp-sg value increases, and consequently, the geometric parameters lB-sg, lV-sg, and ∆lsg of the stagnant region on the formed groove surface also increase. The deviation analysis of the geometric parameters of the stagnant region reveals a consistent trend between the theoretical and experimental values of lV-sg and ∆lsg, with maximum deviations of 15 μm and 4.13%, respectively. This study provides theoretical and experimental evidence for the establishment of the theoretical model of the stagnant region in mechanical scratching, the analysis of its forming mechanism, and the control of the stagnant region geometric parameters on the formed groove surface.

Direct, age- and gender-specific reference intervals: applying a modified M-estimator of the Yeo-Johnson transformation to clinical real-world data

Abstract Objectives Reference intervals for the general clinical practice are expected to cover non-pathological values, but also reflect the underlying biological variation present in age- and gender-specific patient populations. Reference intervals can be inferred from routine patient data measured in high capacity using parametric approaches. Stratified reference distributions are obtained which may be transformed to normality via e.g. a Yeo-Johnson transformation. The estimation of the optimal transformation parameter for Yeo-Johnson through maximum likelihood can be highly influenced by the presence of outlying observations, resulting in biased reference interval estimates. Methods To reduce the influence of outlying observations on parametric reference interval estimation, a reweighted M-estimator approach for the Yeo-Johnson (YJ) transformation was utilised to achieve central normality in stratified reference populations for a variety of laboratory test results. The reweighted M-estimator for the YJ transformation offers a robust parametric approach to infer relevant reference intervals. Results The proposed method showcases robustness up to 15 % of outliers present in routine patient data, highlighting the applicability of the reweighted M-estimator in laboratory medicine. Furthermore, reference intervals are personalised based on the patients’ age and gender for a variety of analytes from routine patient data collected in a tertiary hospital, robustly reducing the dimensionality of the data for more data-driven approaches. Conclusions The method shows the advantages for estimating reference intervals directly and parametrically from routine patient data in order to provide expected reference ranges. This approach to locally inferred reference intervals allows a more nuanced comparison of patients’ test results.

Mixing “Magnetic” and “Electric” Ehlers–Harrison transformations: the electromagnetic swirling spacetime and novel type I backgrounds

In this paper, we obtain a complete list of stationary and axisymmetric spacetimes, generated from a Minkowski spacetime using the Ernst technique. We do so by operating on the associated seed potentials with a composition of Ehlers and Harrison transformations. In particular, assigning an additional “electric” or “magnetic” tag to the transformations, we investigate the new spacetimes obtained either via a composition of magnetic Ehlers and Harrison transformations (first part) or via a magnetic-electric combination (second part). In the first part, the resulting type D spacetime, dubbed electromagnetic swirling universe, features key properties, separately found in swirling and (Bonnor–)Melvin spacetimes, the latter recovered in appropriate limits. A detailed analysis of the geometry is included, and subtle issues are addressed. A detailed proof that the spacetime belongs to the Kundt family, is included, and a notable relation to the5 planar-Reissner–Nordström-NUT black hole is also meticulously worked out. This relation is further exploited to reverse-engineer the form of the solution in the presence of a nontrivial cosmological constant. A Schwarzschild black hole embedded into the new background is also discussed. In the second part, we present four novel stationary and axisymmetric asymptotically nonflat type I spacetimes, which are naively expected to be extensions of the Melvin or swirling solution including a NUT parameter or electromagnetic charges. We actually find that they are, under conditions, free of curvature and topological singularities, with the physical meaning of the electric transformation parameters in these backgrounds requiring further investigation.

AI-driven generalized polynomial transformation models for unsupervised fundus image registration.

We introduce a novel AI-driven approach to unsupervised fundus image registration utilizing our Generalized Polynomial Transformation (GPT) model. Through the GPT, we establish a foundational model capable of simulating diverse polynomial transformations, trained on a large synthetic dataset to encompass a broad range of transformation scenarios. Additionally, our hybrid pre-processing strategy aims to streamline the learning process by offering model-focused input. We evaluated our model's effectiveness on the publicly available AREDS dataset by using standard metrics such as image-level and parameter-level analyzes. Linear regression analysis reveals an average Pearson correlation coefficient (R) of 0.9876 across all quadratic transformation parameters. Image-level evaluation, comprising qualitative and quantitative analyzes, showcases significant improvements in Structural Similarity Index (SSIM) and Normalized Cross Correlation (NCC) scores, indicating its robust performance. Notably, precise matching of the optic disc and vessel locations with minimal global distortion are observed. These findings underscore the potential of GPT-based approaches in image registration methodologies, promising advancements in diagnosis, treatment planning, and disease monitoring in ophthalmology and beyond.

2D human skeleton action recognition with spatial constraints

AbstractHuman actions are predominantly presented in 2D format in video surveillance scenarios, which hinders the accurate determination of action details not apparent in 2D data. Depth estimation can aid human action recognition tasks, enhancing accuracy with neural networks. However, reliance on images for depth estimation requires extensive computational resources and cannot utilise the connectivity between human body structures. Besides, the depth information may not accurately reflect actual depth ranges, necessitating improved reliability. Therefore, a 2D human skeleton action recognition method with spatial constraints (2D‐SCHAR) is introduced. 2D‐SCHAR employs graph convolution networks to process graph‐structured human action skeleton data comprising three parts: depth estimation, spatial transformation, and action recognition. The initial two components, which infer 3D information from 2D human skeleton actions and generate spatial transformation parameters to correct abnormal deviations in action data, support the latter in the model to enhance the accuracy of action recognition. The model is designed in an end‐to‐end, multitasking manner, allowing parameter sharing among these three components to boost performance. The experimental results validate the model's effectiveness and superiority in human skeleton action recognition.