Texture Model Research Articles

Predicting MET exon 14 skipping mutation in pulmonary sarcomatoid carcinoma by whole-tumour texture analysis combined with clinical and conventional contrast-enhanced computed tomography features.

Pulmonary sarcomatoid carcinoma (PSC) is a rare, highly malignant type of non-small cell lung cancer (NSCLC) with a poor prognosis. Targeted drugs for MET exon 14 (METex14) skipping mutation can have considerable clinical benefits. This study aimed to predict METex14 skipping mutation in PSC patients by whole-tumour texture analysis combined with clinical and conventional contrast-enhanced computed tomography (CECT) features. This retrospective study included 56 patients with PSC diagnosed by pathology. All patients underwent CECT before surgery or other treatment, and both targeted DNA- and RNA-based next-generation sequencing (NGS) were used to detect METex14 skipping mutation status. The patients were divided into two groups: METex14 skipping mutation and nonmutation groups. Overall, 1,316 texture features of the whole tumour were extracted. We also collected 12 clinical and 20 conventional CECT features. After dimensionality reduction and selection, predictive models were established by multivariate logistic regression analysis. Models were evaluated using the area under the curve (AUC), and the clinical utility of the model was assessed by decision curve analysis. METex14 skipping mutation was detected in 17.9% of PSCs. Mutations were found more frequently in those (I) who had smaller long- or short-axis diameters (P=0.02, P=0.01); (II) who had lower T stages (I, II) (P=0.02); and (III) with pseudocapsular or annular enhancement (P=0.03). The combined model based on the conventional and texture models yielded the best performance in predicting METex14 skipping mutation with the highest AUC (0.89). The conventional and texture models also had good performance (AUC =0.83 conventional; =0.88 texture). Whole-tumour texture analysis combined with clinical and conventional CECT features may serve as a noninvasive tool to predict the METex14 skipping mutation status in PSC.

Simulations of Texture Evolution in the Near-Surface Region During Aluminum Rolling

Prediction of texture changes during cold rolling is important because they affect the recrystallization and anisotropy of an aluminum sheet during successive forming steps. During cold rolling of aluminum alloys, the through-thickness textural change in the subsurface layer depends heavily on the shear stresses exerted on the material. The intensity of this shear stress is determined by the value of and change in the coefficient of friction as the contact length between the rolls and metallic sheet changes. The quality of the texture prediction under constant and variable coefficients of friction are assessed for three established texture models: the grain interaction (GIA) model, the viscoplastic self-consistent (VPSC) approach, and the full-field crystal plasticity Düsseldorf Advanced Material Simulation Kit (DAMASK) code. The simulation results are compared with subsurface layer textures obtained from conducting experimental cold-rolling trials on an aluminum alloy, which are designed to maximize shear in a single rolling pass. The formulation of a variable coefficient of friction is crucial for ensuring both the reasonable prediction of rolling forces and changes in texture. GIA and DAMASK yield the best texture prediction results for a variable coefficient of friction model.

Estimation of Chlorophyll Content in Apple Leaves Infected with Mosaic Disease by Combining Spectral and Textural Information Using Hyperspectral Images

Leaf chlorophyll content (LCC) is an important indicator of plant nutritional status and can be a guide for plant disease diagnosis. In this study, we took apple leaves infected with mosaic disease as a research object and extracted two types of information on spectral and textural features from hyperspectral images, with a view to realizing non-destructive detection of LCC. First, the collected hyperspectral images were preprocessed and spectral reflectance was extracted in the region of interest. Subsequently, we used the successive projections algorithm (SPA) to select the optimal wavelengths (OWs) and extracted eight basic textural features using the gray-level co-occurrence matrix (GLCM). In addition, composite spectral and textural metrics, including vegetation indices (VIs), normalized difference texture indices (NDTIs), difference texture indices (DTIs), and ratio texture indices (RTIs) were calculated. Third, we applied the maximal information coefficient (MIC) algorithm to select significant VIs and basic textures, as well as the tandem method was used to fuse the spectral and textural features. Finally, we employ support vector regression (SVR), backpropagation neural network (BPNN), and K-nearest neighbors regression (KNNR) methods to explore the efficacy of single and combined feature models for estimating LCC. The results showed that the VIs model (R2 = 0.8532, RMSE = 2.1444, RPD = 2.6179) and the NDTIs model (R2 = 0.7927, RMSE = 2.7453, RPD = 2.2032) achieved the best results among the single feature models for spectra and texture, respectively. However, textural features generally exhibit inferior regression performance compared to spectral features and are unsuitable for standalone applications. Combining textural and spectral information can potentially improve the single feature models. Specifically, when combining NDTIs with VIs as input parameters, three machine learning models outperform the best single feature model. Ultimately, SVR achieves the highest performance among the LCC regression models (R2 = 0.8665, RMSE = 1.8871, RPD = 2.7454). This study reveals that combining textural and spectral information improves the quantitative detection of LCC in apple leaves infected with mosaic disease, leading to higher estimation accuracy.

Soil textural class modeling using digital soil mapping approaches: Effect of resampling strategies on imbalanced dataset predictions

In a digital soil mapping (DSM) context, machine learning (ML) algorithms are widely used to model soil textural classes (STCs). However, in the real world most soil class datasets exhibit imbalanced distributions. This poses a challenge as ML algorithms are designed to handle balanced classes, leading to a bias towards the majority classes while often overlooking the minority classes. Furthermore, within the DSM framework, two strategies can be employed to model STCs: direct and indirect approaches. In the direct approach, STCs are directly inputted into the model for prediction. In contrast, the indirect approach involves introducing soil texture fractions (i.e., clay, silt, sand) as initial inputs, then STCs are obtained from the outputs. Limited research has been conducted on the impact of data balancing on STC predictions, and there is a lack of comparative analysis between direct and indirect approaches in this context. Therefore, this study aimed to evaluate the efficacy of a resampling technique (SMOTE: synthetic minority oversampling technique) in handling an imbalanced soil texture dataset collected from the Kuhdasht region in western Iran. Additionally, the study sought to compare the performance of direct and indirect modeling approaches. Environmental covariates derived from Landsat 8 and Sentinel 2 images along with a digital elevation model (DEM) were used as input variables to a random forest (RF) model to model STCs and soil texture fractions. The results revealed that terrain attributes and Euclidean distances played a more significant role in modeling both balanced and imbalanced datasets compared to remotely sensed data. Kappa indices for balanced and imbalanced datasets, as well as for the indirect approach were found to be 89%, 68% and 38% respectively. In the same way, the overall accuracies were 91%, 79% and 68%, respectively. Among the imbalanced classes, clay loam and loam which accounted for the majority of observations showed the highest recall values, followed by sandy clay loam, sandy loam and silty clay loam. When employing the indirect approach, the RF model failed to capture the minority classes in terms of validation statistics. Additionally, modeling with the imbalanced dataset resulted in the exclusion of three minority STCs from the final map. Overall, this study showed the importance of balancing STCs prior to modeling to achieve more accurate estimates of STCs, as well as the superiority of employing the direct approach (using balanced data sets) over the indirect approach.

Enhancing endoscopic scene reconstruction with color-aware inverse rendering through neural SDF and radiance fields.

Virtual surgical training is crucial for enhancing minimally invasive surgical skills. Traditional geometric reconstruction methods based on medical CT/MRI images often fall short in providing color information, which is typically generated through pseudo-coloring or artistic rendering. To simultaneously reconstruct both the geometric shape and appearance information of organs, we propose a novel organ model reconstruction network called Endoscope-NeSRF. This network jointly leverages neural radiance fields and Signed Distance Function (SDF) to reconstruct a textured geometric model of the organ of interest from multi-view photometric images acquired by an endoscope. The prior knowledge of the inverse correlation between the distance from the light source to the object and the radiance improves the real physical properties of the organ. The dilated mask further refines the appearance and geometry at the organ's edges. We also proposed a highlight adaptive optimization strategy to remove highlights caused by the light source during the acquisition process, thereby preventing the reconstruction results in areas previously affected by highlights from turning white. Finally, the real-time realistic rendering of the organ model is achieved by combining the inverse rendering and Bidirectional Reflectance Distribution Function (BRDF) rendering methods. Experimental results show that our method closely matches the Instant-NGP method in appearance reconstruction, outperforming other state-of-the-art methods, and stands as the superior method in terms of geometric reconstruction. Our method obtained a detailed geometric model and realistic appearance, providing a realistic visual sense for virtual surgical simulation, which is important for medical training.

Prediction and modeling of tie-dye polyester fabric's texture and color feature in supercritical carbon dioxide

To mitigate the water-intensive challenges of tie-dye clothing production and address the absence of objective evaluation for finished products, we have explored the correlation between tie-dye techniques in supercritical carbon dioxide (SC-CO2) and image patterns. Employing digital image processing, we extracted the average HSV three-component value of the valid tie-dye area, the proportion of incompletely dyed area in the HSV color space, and the initial three texture features of the digital tie-dye image using the Tamura method. We scrutinized five factors in the supercritical CO2 dyeing process: rotational speed, dyeing temperature, dyeing pressure, dye concentration, and dyeing time. The single-factor climbing test revealed that temperature and pressure are the predominant factors influencing tie-dyeing performance. We propose a correlation between the texture parameters of tie-dyeing and the swelling effect of polyester fabric in a supercritical CO2 environment. Subsequently, we conducted two-factor tests on temperature and pressure to investigate their specific effects on the parameters of supercritical polyester tie-dyeing. The results indicated significant changes in regularity, saturation, and the proportion of undyed area with variations in dyeing temperature and pressure. Building upon these findings, we developed an artificial neural network for predicting tie-dye dyeing temperature and pressure in supercritical CO2, utilizing features regularity, saturation, and proportion of undyed area as inputs. In the 105st round, experimental results demonstrated an accuracy of 0.99445, substantiating the efficacy of tie-dye pattern features in supercritical CO2 for technique forecasting.

Tactile texture recognition of multi-modal bionic finger based on multi-modal CBAM-CNN interpretable method

To solve the problem of lacking surface information while using a single-modal sensor to scan fine textures, a multi-modal bionic finger tactile sensor is used to perceive the information contained in textures, and a multi-modal tactile texture recognition method is proposed to address the issue of poor interpretability for tactile texture recognition models. First, the experimental platform of multi-modal bionic finger tactile texture information acquisition system is built, which can simultaneously collect 14 channels of tactile electrode signals, one channel of pressure signals and one channel of temperature signals. Second, the multi-modal tactile signals can be fused at the data level after signal processing, and then the wavelet transform method is carried out on the basis of data fusion. Third, a multi-modal CBAM-CNN network (Convolutional Block Attention Module, Convolutional Neural Network) is established to extract the multi-modal tactile features after the wavelet transform and the multi-modal tactile features are fused at feature level. To verify the performance of multi-modal CBAM-CNN model, 27(3*9) kinds of millimeter-level fine textures of 3 datasets are tested and the recognition accuracy of each dataset is 98.47%, 97.63%, 99.16%. And it can also be seen that the 14-channel modal data contributes the most to the performance of multi-modal CBAM-CNN model for various textures. The results of ablation experiments prove that the identification accuracy of the fused multi-modal CBAM-CNN is higher than the single-modal CBAM-CNN no matter in time domain or time–frequency domain. Moreover, the accuracy of the weighted fusion method based on correlation function is higher than horizontal fusion method in 14 channels of tactile electrode signals modal. Finally, to interpret the classification decision of the multi-modal CBAM-CNN network in each modal intuitively, the improved multi-modal CAM (Class Activation Map) algorithm is adopted to visualize the interest regions of the samples in detail, and the activated areas in the map make the identification result of CBAM-CNN in each modal more transparent and interpretable.

On [formula omitted] rectangular multifractal multivariate functions

Multifractal analysis has traditionally been based on the pointwise Hölder regularity for locally bounded functions. However, recently an extension to the rectangular pointwise regularity for multivariate locally functions has generated interest. The purpose of this paper is twofold. Firstly, we characterize in hyperbolic wavelet bases the rectangular pointwise Lipschitz regularity for multivariate functions which are non necessarily locally bounded but are locally in Lp. Secondly, we perform applications for both random and deterministic anisotropic self-affine tools for some models of textures in images and flows in turbulence. Actually, we show that fractional anisotropic Wiener field are multivariate monofractal in Lp. We then construct self-affine cascade functions that are multivariate multifractal in Lp.

Optimization of Anti-Skid and Noise Reduction Performance of Cement Concrete Pavement with Different Grooved and Dragged Textures

Cement concrete pavements are crucial to urban infrastructure, significantly influencing road safety and environmental sustainability with their anti-skid and noise reduction properties. However, while texturing techniques like transverse grooving have been widely adopted to enhance skid resistance, they may inadvertently increase road noise. This study addressed the critical need to optimize pavement textures to balance improved skid resistance with noise reduction. Tests were conducted to assess the influence of surface texture on skid resistance and noise, exploring the relationship between texture attributes and their performance in these areas. The investigation examined the effects of texture representation methods, mean profile depth, and the high-speed sideway force coefficient (SFC) on noise intensity and pavement skid resistance. The findings revealed that transverse grooves significantly improved the SFC, enhancing skid resistance. In contrast, longitudinal burlap drag, through its micro- and macro-texture adjustments, effectively reduced vibration frequencies between the tire and pavement, thus mitigating noise. Utilizing the TOPSIS multi-objective optimization framework, an optimization model for pavement textures was developed to augment skid resistance and noise reduction at varying speeds. The results indicated that at 60 km/h, an optimal balance of groove width, depth, and spacing yielded superior skid resistance with a minimal noise increase. At 80 km/h, increased groove spacing and depth were shown to effectively decrease noise while maintaining efficient water evacuation. The optimal pavement texture design must consider the specific context, including traffic volume, vehicle types, and operating speeds. This study provides essential guidance for optimizing urban cement concrete pavement textures, aiming to diminish traffic noise and bolster road safety.

Peripheral material perception.

Humans can rapidly identify materials, such as wood or leather, even within a complex visual scene. Given a single image, one can easily identify the underlying "stuff," even though a given material can have highly variable appearance; fabric comes in unlimited variations of shape, pattern, color, and smoothness, yet we have little trouble categorizing it as fabric. What visual cues do we use to determine material identity? Prior research suggests that simple "texture" features of an image, such as the power spectrum, capture information about material properties and identity. Few studies, however, have tested richer and biologically motivated models of texture. We compared baseline material classification performance to performance with synthetic textures generated from the Portilla-Simoncelli model and several common image degradations. The textures retain statistical information but are otherwise random. We found that performance with textures and most degradations was well below baseline, suggesting insufficient information to support foveal material perception. Interestingly, modern research suggests that peripheral vision might use a statistical, texture-like representation. In a second set of experiments, we found that peripheral performance is more closely predicted by texture and other image degradations. These findings delineate the nature of peripheral material classification.

Experiment and modelling of texture and sliding direction dependence on finger friction behavior

Humans rely on their fingers to sense and interact with external environment. Understanding the tribological behavior between finger skin and object surface is crucial for various fields, including tactile perception, product appearance design, and electronic skin research. Quantitatively describing finger frictional behavior is always challenging, given the complex structure of the finger. In this study, the texture and sliding direction dependence of finger skin friction was quantified based on explicit mathematic models. The proposed double-layer model of finger skin effectively described the nonlinear elastic response of skin and predicted the scaling-law of effective elastic modulus with contact radius. Additionally, the skin friction model on textured surface considering adhesion and deformation factors was established. It revealed that adhesive term dominated finger friction behavior in daily life, and suggested that object texture size mainly influenced friction-induced vibrations rather than the average friction force. Combined with digital image correlation (DIC) technique, the effect of sliding direction on finger friction was analyzed. It was found that the anisotropy in finger friction was governed by the finger’s ratchet pawl structure, which also contributes to enhanced stick-slip vibrations in the distal sliding direction. The proposed friction models can offer valuable insights into the underlying mechanism of skin friction under various operating conditions, and can provide quantitative guidance for effectively encoding friction into haptics.

Xception transfer learning with early stopping for facial age estimation

The rapid development of deep learning attracts more attention to the analysis of person's face images. Deep learning methodsof facial age estimation are more effective compared to methods based on anthropometric models, models of active appearance, texture models, subspace of aging patterns. However, deep learning networks require more computing power to process images. Pre-trained models do not need a large training set and their training time is less. However, the parameters obtained as a result of transfer learning of the pre-training network significantly affect its efficiency. It is also necessary to take into account the properties of the processed images, in particular, the conditions under which they were obtained.Recently, the facial age estimation is implemented in applications in devices with limited resources of computing, for example, in smartphones. The memory size and power consumption of such applications are limited by the computing power of mobile devices. In addition, when photographing a person's face with a smartphone camera, it is very difficult to ensure the uniform lighting. The aim of the research is reducing the error of facial age estimation from uneven illuminated images by applying an early stopping of transfer learning of the Xception network. The proposed technique of transfer learning includes an early stopping of training, if the improvement of the results is not observed within a certain number of epochs. Then the network weights from the epoch with the lowest validation loss are saved. As a result of the proposed technique applying, the average absolute error of age estimation was about five years from unevenly illuminated test images. A number of parameters of the used in this case Xceptionnetwork is less than that of other deep learning neural networks which solved the age estimation problem. Then applying of the Xception network reduces the resource consumption of devices with limited computing power. Prospects for further research are reducing the unevenness of facial image lighting to decrease the error of age estimation. Also, to reduce the computing resources, it is promising to use fast transforms in the Xception convolutional layers.

Recasting Regional Lighting for Shadow Removal

Removing shadows requires an understanding of both lighting conditions and object textures in a scene. Existing methods typically learn pixel-level color mappings between shadow and non-shadow images, in which the joint modeling of lighting and object textures is implicit and inadequate. We observe that in a shadow region, the degradation degree of object textures depends on the local illumination, while simply enhancing the local illumination cannot fully recover the attenuated textures. Based on this observation, we propose to condition the restoration of attenuated textures on the corrected local lighting in the shadow region. Specifically, We first design a shadow-aware decomposition network to estimate the illumination and reflectance layers of shadow regions explicitly. We then propose a novel bilateral correction network to recast the lighting of shadow regions in the illumination layer via a novel local lighting correction module, and to restore the textures conditioned on the corrected illumination layer via a novel illumination-guided texture restoration module. We further annotate pixel-wise shadow masks for the public SRD dataset, which originally contains only image pairs. Experiments on three benchmarks show that our method outperforms existing state-of-the-art shadow removal methods. Project page in: yuhaoliu7456.github.io/RRL-Net.

Hybrid Method of Connection Evaluation and Framework Optimization for Building Surface Reconstruction

The three-dimensional (3D) reconstruction of buildings using photogrammetric point clouds is important for many applications, ranging from digital city construction to urban energy consumption analysis. However, problems such as building complexity and point cloud flaws may lead to incorrect modeling, which will affect subsequent steps such as texture mapping. This paper introduces a pipeline for building surface reconstruction from photogrammetric point clouds, employing a hybrid method that combines connection evaluation and framework optimization. Firstly, the plane segmentation method divides building point clouds into several pieces, which is complemented by a proposed candidate plane generation method aimed at removing redundancies and merging similarities. Secondly, the improved connection evaluation method detects potential skeleton lines from different planes. Subsequently, a framework optimization method is introduced to select suitable undirected polygonal boundaries from planes, forming the basis for plane primitives. Finally, by triangulating all plane primitives and filling holes, a building surface polygonal model is generated. Experiments conducted on various building examples provide both qualitative and quantitative evidence that the proposed hybrid method outperforms many existing methods, including traditional methods and deep learning methods. Notably, the proposed method successfully reconstructs the main building structures and intricate details, which can be further used to generate textural models and semantic models. Experimental results validate that the proposed method can be used for the surface reconstruction from photogrammetric point clouds of planar buildings.

Evaluation of multi-camera images in different SfM-MVS based photogrammetric software and comparison of digital products in generating 3D city models

High-resolution 3D city modeling is generated using the oblique and vertical (nadir) imaging features of multi-camera systems. There are several SfM-MVS based photogrammetric software available. They enable high-resolution image processing and matching and acquire successful outcomes. The orientation of large photogrammetric blocks consisting of multiple sets of images and the accuracy of the 3D city models to be obtained vary according to the ability of the software using these approaches to solve large image matrix problems. In this study, a 3D city model was produced with vertical and oblique images obtained with UltraCam Osprey Mark 3 Premium camera in a selected application area in Kütahya city center. The main purpose of the study is to evaluate the point cloud, textured mesh model, digital elevation model, true orthophoto products and vectorial roof detail drawings via utilizing the vertical + oblique images with different SfM-MVS based photogrammetric software and to compare the results.

Long-term monthly 0.05° terrestrial evapotranspiration dataset (1982–2018) for the Tibetan Plateau

Abstract. Evapotranspiration (ET) plays a crucial role in the water balance of the Tibetan Plateau (TP), often referred to as the “Asian water tower” region. However, accurately monitoring and comprehending the spatial and temporal variations of ET components (including soil evaporation Es, canopy transpiration Ec, and intercepted water evaporation Ew) in this remote area remains a significant challenge due to the limited availability of observational data. This study generates a 37-year dataset (1982–2018) of monthly ET components for the TP using the MOD16-STM (MOD16 soil texture model). This model utilizes up-to-date soil properties, meteorological data, and remote sensing datasets. The estimated ET results strongly correlate with measurements from nine flux towers, demonstrating a low root mean square error of 13.48 mm per month, a mean bias of 2.85 mm per month, a coefficient of determination of 0.83, and an index of agreement of 0.92. The annual average ET for the entire TP, defined as elevations higher than 2500 m, is approximately 0.93(± 0.037) × 103 Gt yr−1. The predominant contributor to ET on the TP is Es, accounting for 84 % of the total ET. Our findings reveal a noteworthy upward trend in ET in most central and eastern parts of the TP, with a rate of approximately 1–4 mm yr−1 (p<0.05) and a significant downward trend with rates between −3 and 1 mm yr−1 in the northwestern part of TP during the period from 1982 to 2018. The average annual increase in ET for the entire TP over the past 37 years is approximately 0.96 mm yr−1. This upward trend can be attributed to the TP's warming and wetting climate conditions. The MOD16-STM ET dataset demonstrates a reliable performance across the TP compared to previous research outcomes. This dataset is valuable for research on water resource management, drought monitoring, and ecological studies. The entire dataset is freely accessible through the Science Data Bank (https://doi.org/10.11922/sciencedb.00020, Ma et al., 2021) and the National Tibetan Plateau Data Center (TPDC) (https://doi.org/10.11888/Terre.tpdc.271913, Yuan, 2021).

Spatial Analysis Using Geographically Weighted Ordinary Logistic Regression (GWOLR) Method for Prediction of Particle-Size Fraction in Soil Surface

Spatial analysis is a method used to understand the spatial variation of geospatial data. In this study, the Geographically Weighted Ordinary Logistic Regression (GWOLR) method was used in spatial analysis to predict the particle size fraction of the surface soil. The particle size fraction of the surface soil is an important parameter in determining soil productivity and environmental quality. However, the particle size fraction in surface soils can vary spatially and is influenced by geographical factors such as elevation, rainfall, and soil texture. This study will be carried out by collecting particle size fraction data and geospatial data at randomly selected locations. Accurate modelling of soil texture is necessary because it‘s a crucial factor in determining how soil management will go. However, because soil texture is a compositional data set, it is one of the soil attributes that is more challenging to model. The challenge presented by this compositional data set is the imposition of constant quantities, specifically the requirement that the total of the fractions of clay, silt, and sand be 100%. Topographical variability can be derived from DEM data, making it an independent variable or predictor for soil texture prediction. The data will then be analyzed using the GWOLR method to predict the particle size fraction at locations that have not been observed before. The resulting prediction model will then be evaluated using cross-validation to check the accuracy of the model. This study will provide benefits for land management and natural resource management and can improve understanding of the spatial variation of particle size fractions in surface soils and the spatial and geographical factors that influence them. The GWOLR model for predicting particle size fractions in surface soils was carried out with a fixed bi-square weight and a bandwidth of 0.28895. The GWOLR model classification accuracy value is 94 percent, this shows that the GWOLR model for predicting soil particle size is more suitable than the ordinal logistic regression model with a classification accuracy of 90 percent. The aims of this study are to: (1) Establish a soil texture prediction model using the GWOLR method; and (2) Test the reliability of the model in predicting surface soil texture.

Progressive Learning of 3D Reconstruction Network From 2D GAN Data.

This paper presents a method to reconstruct high-quality textured 3D models from single images. Current methods rely on datasets with expensive annotations; multi-view images and their camera parameters. Our method relies on GAN generated multi-view image datasets which have a negligible annotation cost. However, they are not strictly multi-view consistent and sometimes GANs output distorted images. This results in degraded reconstruction qualities. In this work, to overcome these limitations of generated datasets, we have two main contributions which lead us to achieve state-of-the-art results on challenging objects: 1) A robust multi-stage learning scheme that gradually relies more on the models own predictions when calculating losses and 2) A novel adversarial learning pipeline with online pseudo-ground truth generations to achieve fine details. Our work provides a bridge from 2D supervisions of GAN models to 3D reconstruction models and removes the expensive annotation efforts. We show significant improvements over previous methods whether they were trained on GAN generated multi-view images or on real images with expensive annotations.

Three-Dimensional Digitization of Documentation and Perpetual Preservation of Cultural Heritage Buildings at Risk of Liquidation and Loss—The Methodology and Case Study of St Adalbert’s Church in Chicago

This paper presents a three-dimensional (3D) digitization methodology for documenting the appearance and geometry of cultural heritage buildings using modern tools for data acquisition and processing. This work presents a method combining laser scanning of building dimensions with photographic texture acquisition techniques in order to develop accurate, photo-realistic 3D models. This work also presents a method for digital reconstruction of the elements of interiors. This case study presents the process and result of scanning the church of St Adalbert in Chicago along with its inner finishing elements, achieved during the interventional 3D scanning of a historical building interior. The obtained colorized point cloud has 3.5 billion points and a volume of 65 GB. Its creation took nearly 3.5 h on a high-end computer. The generated textured mesh model has 1.6 billion triangles and a volume of 60 GB. The analysis of the results showed that the elements of the church furnishings were reproduced with very high accuracy. The developed 3D model of the interior, appearance and dimensions of the church is its perpetual documentation. It can be used for various purposes, such as popularizing the appearance on the Internet, scientific research on interior artefacts, creating files for VR and shooting high-resolution films.

Performance of textured dual mobility total hip prosthesis with a concave dimple during Muslim prayer movements

The single mobility bearing as a previous bearing design of total hip prosthesis has severe mobility constraints that can result in dislocation during Muslim (people who follow the Islam as religion) prayer movements, specifically shalat that requires intense movement. There are five intense movements (i.e., bowing, prostration, sitting, transition from standing to prostration, and final sitting) during Muslim prayer that may generate an impingement problem for patients with total hip prosthesis. In this work, textured dual mobility total hip prosthesis with two textured cases (i.e., textured femoral head and textured inner liner) are presented and their performances are numerically evaluated against untextured surface model during Muslim prayer movement. The concave dimple design is chosen for surface texturing, while for simulating femoral head materials, SS 316L and CoCrMo is choosen. To represent the real condition, three-dimensional computational fluid dynamics (CFD) coupled with two-way fluid–structure interaction (FSI) methods are employed to analyze elastohydrodynamic lubrication problem with non-Newtonian synovial fluid model. The main aim of the present study is to investigate the tribological performance on dual mobility total hip prosthesis with applied textured surface with concave dimple in femoral head and inner liner surface under Muslim prayer movements. It is found that applying surface texturing has a beneficial effect on the lubrication performance for some intense movements. The textured femoral head model performs better than textured inner liner model and untextured model (both femoral head and inner liner). The numerical results also indicate superior performance of CoCrMo femoral head compared to SS 316L femoral head. These findings can be used as a reference for biomedical engineers and orthopedic surgeons in designing and choosing suitable total hip prosthesis for Muslims makes they can carry out Muslim prayer movements like humans in general who have normal hip joints.