Texture Model Research Articles

A Reconstruction of the Shrine of the Prophet Nahum: An Analysis of 3D Documentation Methods and Data Transfer Technology for Virtual and Augmented Realities

This article focuses on modern methods of documentation and visualization for a historic object. Digital photogrammetry and terrestrial laser scanning (TLS), which are essential tools for documenting cultural heritage in view of their rapid development in recent years, were used, compared, and analyzed. Furthermore, the use of available 3D computer graphics technologies for visualization is described and an optimal procedure for converting the object into VR and AR is proposed and implemented. The technologies presented in this article were tested within the context of a project on the reconstruction of the shrine of the Prophet Nahum in the city of Alqosh in northern Iraq, taking the shrine as a case study. Funded by ARCH Int. and provided by GemaArt Int., the restoration project started in 2018 and was completed in 2021. The ongoing documentation was prepared by the CTU and it used the materials for research purposes. Accurate documentation using photogrammetry, drones, and TLS was key to the restoration. Leica BLK360, Faro Focus S150, and GeoSlam laser scanners were used, as well as photogrammetric methods. In particular, the documentation process involved the creation of 3D textured models from the photogrammetry, which were compared to the TLS data to ensure accuracy. These models were necessary to track changes during the reconstruction phases and to calculate the volumes of rubble removed and materials added. Our data analysis revealed significant differences between the construction logs and the analysis of the accurate 3D models; the results showed an underestimation of the displaced material statements by 13.4% for removed material and 4.6% for added material. The use of heat maps and volumetric analyses helped to identify areas of significant change that guided the reconstruction and documented significant changes to the building for the investor. These findings are important for use in the construction industry with respect to historic sites as well as for further research focused on visualization using VR (virtual reality) and AR (augmented reality). The conversion of existing 3D models into VR and AR is rapidly evolving and significant progress was made during this project. The Unreal Engine (UE) game engine was used. Despite the significantly improved performance of the new UE 5 version, the data for conversion to VR and AR needs to be decimated to reduce the amount—in our case, this was by up to 90%. The quality appearance of the objects is then ensured by textures. An important outcome of this part of the research was the debugged workflow developed to optimize the 3D models for VR, which was essential for creating a virtual museum that shows the restoration process.

Creating a textured book for oral health among preschool-children

Poor oral hygiene in young children is a major public health problem that is affected by many societal, communal, and environmental factors. Children as young as 12 months old can get early childhood caries (ECC), which can impact their growth, development, and quality of life. ECC affects between 6% and 90% of people around the world, so kids in preschool need to learn good oral health habits. The study's objective is to create an explained book model that can be used to teach preschoolers about good oral care and get around the problems with current methods. The textured book model was developed with input from faculties from Public Health Dentistry, Pedodontics and pre-school teachers. Questionnaires assessing face, content, construct and criterion validity through a Likert scale captured detailed feedback. The glossy book, made with design tools and printed on cloth, met all four validity criteria, indicating its effectiveness as a teaching tool. This study assessed the validity of an assessment questionnaire for a textured book model aimed at improving oral hygiene knowledge among young learners. Strong correlations were found between overall scores and items Q4, Q5, and Q7 (p<0.001). Factor analysis revealed three key dimensions—comprehensiveness, relevance, and effectiveness—demonstrating the model’s efficacy in promoting positive oral hygiene practices. Content validity ratio was obtained using the Lawshe Method, and estimated to be 0.83. The reliability of the questionnaire was assessed using Cronbach’s alpha, which was 0.81. The significant level was set at p-value <0.05. The textured book model is a fun and engaging way to encourage preschoolers to care for their teeth. It combines learning with play, making it an effective tool to teach good dental habits that can last a lifetime.

Modeling of Texture and Starch Retrogradation of High-in-Fiber Bread Using Response Surface Methodology

Modeling of Texture and Starch Retrogradation of High-in-Fiber Bread Using Response Surface Methodology

High-Fidelity and Efficient Pluralistic Image Completion With Transformers.

Image completion has made tremendous progress with convolutional neural networks (CNNs), because of their powerful texture modeling capacity. However, due to some inherent properties (e.g., local inductive prior, spatial-invariant kernels), CNNs do not perform well in understanding global structures or naturally support pluralistic completion. Recently, transformers demonstrate their power in modeling the long-term relationship and generating diverse results, but their computation complexity is quadratic to input length, thus hampering the application in processing high-resolution images. This paper brings the best of both worlds to pluralistic image completion: appearance prior reconstruction with transformer and texture replenishment with CNN. The former transformer recovers pluralistic coherent structures together with some coarse textures, while the latter CNN enhances the local texture details of coarse priors guided by the high-resolution masked images. To decode diversified outputs from transformers, auto-regressive sampling is the most common method, but with extremely low efficiency. We further overcome this issue by proposing a new decoding strategy, temperature annealing probabilistic sampling (TAPS), which firstly achieves more than 70× speedup of inference at most, meanwhile maintaining the high quality and diversity of the sampled global structures. Moreover, we find the full CNN architecture will lead to suboptimal solutions for guided upsampling. To render more realistic and coherent contents, we design a novel module, named texture-aware guided attention, to concurrently consider the procedures of texture copy and generation, meanwhile raising several important modifications to solve the boundary artifacts. Through dense experiments, we found the proposed method vastly outperforms state-of-the-art methods in terms of four aspects: 1) large performance boost on image fidelity even compared to deterministic completion methods; 2) better diversity and higher fidelity for pluralistic completion; 3) exceptional generalization ability on large masks and generic dataset, like ImageNet. 4) Much higher decoding efficiency over previous auto-regressive based methods.

Modeling and characterizing of surface texture produced by scraping process

Abstract Scraping technology is widely used in the guideways manufacturing of high-precision machine tools, yet the scraped surface texture formation mechanism is still not fully understood owing to the complexity and randomness of the scraping process, which limits the study of the tribological properties of scraped bond surfaces. To reveal the formation mechanism of the scraped surface texture, this study adopts the stratified theory to model and characterize scraped surfaces based on the characteristics of the scraping process. The research found tri-Gaussian stratification features in a scraped surface texture and establishes surface stratified model. Tri-Gaussian segmented separation and continuous separation methods are proposed to calculate the component surface parameters for characterizing the scraped surface texture. Segmented and continuous separation methods are respectively applied to analyze and reconstruct simulated tri-Gaussian stratified surfaces and experimental scraped surfaces. The results indicate that the continuous separation method is superior to the segmented separation method and leads to almost the same height and process parameters as the measured surface in the surface reconstruction. This verifies the feasibility and accuracy of the scraped surface texture model and characterization methods. This paper reveals the surface texture formation mechanism from the viewpoint of the scraping process and provides new insights for modeling and characterizing research on scraped surface texture.

Magnetic Resonance Imaging Texture Analysis Based on Intraosseous and Extraosseous Lesions to Predict Prognosis in Patients with Osteosarcoma.

Objectives: To construct an optimal magnetic resonance imaging (MRI) texture model to evaluate histological patterns and predict prognosis in patients with osteosarcoma (OS). Methods: Thirty-four patients underwent pretreatment MRI and were diagnosed as having OS by surgical resection or biopsy between September 2008 and June 2018. Histological patterns and 3-year survival were recorded. Manual segmentation was performed in intraosseous, extraosseous, and entire lesions on T1-weighted, T2-weighted, and contrast-enhanced T1-weighted images to extract texture features and perform principal component analysis. A support vector machine algorithm with 3-fold cross-validation was used to construct and validate the models. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate diagnostic performance in evaluating histological patterns and 3-year survival. Results: Eight patients were chondroblastic and the remaining twenty-six patients were non-chondroblastic patterns. Twenty-seven patients were 3-year survivors, and the remaining seven patients were non-survivors. In discriminating chondroblastic from non-chondroblastic patterns, the model from extraosseous lesions on the T2-weighted images showed the highest diagnostic performance (AUCs of 0.94 and 0.89 in the training and validation sets). The model from intraosseous lesions on the T1-weighted images showed the highest diagnostic performance in discriminating 3-year non-survivors from survivors (AUCs of 0.99 and 0.88 in the training and validation sets) with a sensitivity, specificity, positive predictive value, and negative predictive value of 85.7%, 92.6%, 75.0%, and 96.2%, respectively. Conclusions: The texture models of extraosseous lesions on T2-weighted images can discriminate the chondroblastic pattern from non-chondroblastic patterns, while the texture models of intraosseous lesions on T1-weighted images can discriminate 3-year non-survivors from survivors.

Modeling Groundwater Levels in California's Central Valley by Hierarchical Gaussian Process and Neural Network Regression

AbstractModeling groundwater levels continuously across California's Central Valley (CV) hydrological system is challenging due to low‐quality well data which is sparsely and noisily sampled across time and space. The lack of consistent well data makes it difficult to evaluate the impact of 2017 and 2019 wet years on CV groundwater following a severe drought during 2012–2015. A novel machine learning method is formulated for modeling groundwater levels by learning from a 3D lithological texture model of the CV aquifer. The proposed formulation performs multivariate regression by combining Gaussian processes (GP) and deep neural networks (DNN). The hierarchical modeling approach constitutes training the DNN to learn a lithologically informed latent space where non‐parametric regression with GP is performed. We demonstrate the efficacy of GP‐DNN regression for modeling non‐stationary features in the well data with fast and reliable uncertainty quantification, as validated to be statistically consistent with the empirical data distribution from 90 blind wells across CV. We show how the model predictions may be used to supplement hydrological understanding of aquifer responses in basins with irregular well data. Our results indicate that on average the 2017 and 2019 wet years in California were largely ineffective in replenishing the groundwater loss caused during previous drought years.

Superhydrophobicity induced antibacterial adhesion and durability of laser bidirectionally-textured zirconia surfaces with different inclination angles

Femtosecond laser bidirectional texturing with different laser energy densities and inclination angles was performed on zirconia (ZrO2) surfaces. The laser bidirectional texturing models under different inclination angles were constructed to predict the water contact angles (WCAs), and the superhydrophobicity induced antibacterial adhesion mechanism was analyzed. The results revealed that under the combined effect of the optimal laser energy density (7.0 J/cm2) and inclination angle (90°), the laser-textured ZrO2 surface could obtain a micro-nano dual-scale structure composed of periodic regular micro-cones and nanoparticles. This structure not only promoted the adsorption of carbon-containing hydrophobic groups from the stearic acid and the air to form a superhydrophobic surface with excellent chemical/mechanical durability and anti-fouling/self-cleaning abilities, but also minimized the contact area with the bacterial suspension (solid/liquid contact area ratio of 6 %) to enhance antibacterial ability. The optimal stearic acid-modified laser-textured ZrO2 surface exhibited an extremely low adhesive force (Fadhesive=6.69 μN) to the bacterial suspension, and thus achieved the highest antibacterial ratio of 85.3 %. This study is expected to enrich the application of ZrO2 ceramics in the medical field.

Historical Elite Differential Evolution Based on Particle Swarm Optimization Algorithm for Texture Optimization with Application in Particle Physics

Within the phenomenology of particle physics, the theoretical model of 4-zero textures is validated using a chi-square criterion that compares experimental data with the computational results of the model. Traditionally, analytical methods that often imply simplifications, combined with computational analysis, have been used to validate texture models. In this paper, we propose a new meta-heuristic variant of the differential evolution algorithm that incorporates aspects of the particle swarm optimization algorithm called “HE-DEPSO” to obtain chi-squared values that are less than a bound value, which exhaustive and traditional algorithms cannot obtain. The results show that the proposed algorithm can optimize the chi-square function according to the required criteria. We compare simulated data with experimental data in the allowed search region, thereby validating the 4-zero texture model.

Computer Modelling of Textures on Images with Human Skin Wound Areas

In this paper, we concentrate on the images of the wounds on the human skin and propose to consider each image as a set of smaller pieces – crops or patches containing different textures. We overview, develop and compare deep learning feature extraction methods to model image crops as 200-dimensional feature vectors using various artificial neural network architectures: convolutional autoencoders, variational convolutional autoencoders, and Siamese convolutional networks trained in the contrastive learning manner. Also, we develop a custom convolutional encoder and decoder, use them in the aforementioned architectures and compare them with the ResNet encoder and decoder alternatives. Finally, we train and evaluate k-nearest neighbors and Multi-Layer Perceptron classifiers on the features extracted with the model above options to discriminate skin, wound, and background image patches. Classification evaluation results on the features, extracted with the Siamese network, show the best test accuracy for all implementations without a significant shift between model versions (accuracy > 93%); variational autoencoders show random results for all options (accuracy around 33%), and convolutional autoencoders reached good results (accuracy > 77%) but with a noticeable difference between the custom and ResNet versions; the latter is better. Custom encoder and decoder implementations are faster and smaller than the ResNet alternatives but may be less stable on larger datasets, which still needs investigation. Possible applications of the feature vectors include an area of interest extraction during wound segmentation or classification and usage as patch embeddings while training vision transformer architectures.

Generating a virtual acoustic environment to approximate the soundscape of a historical cathedral

This paper focuses on the generation of a virtual acoustic environment of a historical cathedral basilica located in St. Augustine, Florida, USA. The virtual acoustic environment is an effective means to study a cultural heritage's soundscape that restricts visitors from accessing and being quiet. The author used a laser scanner to capture the reality of the cathedral's interior, in order to maximize the visual perception of the worship space, and created a 3D digital textured mesh model using the points cloud generated from laser scanning. The spatial experience recorded by a 360 camera with an ambisonic microphone provided the virtual soundwalk at multiple locations. The background and foreground sound were synthesized in Unity using the same music recordings that the church used during the day. The sound pressure levels were closely matched with the ones measured by a sound level meter. The virtual acoustic environment was simulated by using spatial applications in Unity and optimized through the comparison of the room acoustical measurements performed based on ISO 3382.

Development and Evaluation of a Learning-based Model for Real-time Haptic Texture Rendering.

Current Virtual Reality (VR) environments lack the haptic signals that humans experience during real-life interactions, such as the sensation of texture during lateral movement on a surface. Adding realistic haptic textures to VR environments requires a model that generalizes to variations of a user's interaction and to the wide variety of existing textures in the world. Current methodologies for haptic texture rendering exist, but they usually develop one model per texture, resulting in low scalability. We present a deep learning-based action-conditional model for haptic texture rendering and evaluate its perceptual performance in rendering realistic texture vibrations through a multi-part human user study. This model is unified over all materials and uses data from a vision-based tactile sensor (GelSight) to render the appropriate surface conditioned on the user's action in real-time. For rendering texture, we use a high-bandwidth vibrotactile transducer attached to a 3D Systems Touch device. The results of our user study shows that our learning-based method creates high-frequency texture renderings with comparable or better quality than state-of-the-art methods without the need to learn a separate model per texture. Furthermore, we show that the method is capable of rendering previously unseen textures using a single GelSight image of their surface.

Synergistic Effects of Surface Texture and Cryogenic Treatment on the Tribological Performance of Aluminum Alloy Surfaces

In order to improve the tribological properties of the 7075-T6 aluminum alloy used on the rotor surface, a combined method of cryogenic treatment and laser surface texture treatment was applied. Various tests, including metallographic microscopy, scanning electron microscopy, elemental analysis, microhardness measurements, were conducted to examine the wear morphology and modification mechanism of the treated 7075-T6 aluminum alloy surface. A numerical simulation model of surface texture was established using computational fluid dynamics to analyze the lubrication characteristics of V-shaped texture. The research finding that the 7075-T6 aluminum alloy experienced grain refinement during the cryogenic treatment process, enhancing the wear resistance of the V-shaped textures. This improvement delayed the progression of fatigue wear, abrasive wear, and oxidative wear, thereby reducing friction losses. The designed V-shaped texture contributes to reducing contact area, facilitating the capture and retention of abrasives, and enhancing oil film load-bearing capacity, thereby improving tribological performance. The synergistic effect of cryogenic treatment reduced the friction coefficient by 24.8% and the wear loss by 66.4%. Thus, the combination of surface texture and cryogenic treatment significantly improved the tribological properties of the 7075-T6 aluminum alloy.

Bridge Surface Defect Localization Based on Panoramic Image Generation and Deep Learning-Assisted Detection Method

Applying unmanned aerial vehicles (UAVs) and vision-based analysis methods to detect bridge surface damage significantly improves inspection efficiency, but the existing techniques have difficulty in accurately locating damage, making it difficult to use the results to assess a bridge’s degree of deterioration. Therefore, this study proposes a method to generate panoramic bridge surface images using multi-view images captured by UAVs, in order to automatically identify and locate damage. The main contributions are as follows: (1) We propose a UAV-based image-capturing method for various bridge sections to collect close-range, multi-angle, and overlapping images of the surface; (2) we propose a 3D reconstruction method based on multi-view images to reconstruct a textured bridge model, through which an ultra-high resolution panoramic unfolded image of the bridge surface can be obtained by projecting from multiple angles; (3) we applied the Swin Transformer to optimize the YOLOv8 network and improve the detection accuracy of small-scale damages based on the established bridge damage dataset and employed sliding window segmentation to detect damage in the ultra-high resolution panoramic image. The proposed method was applied to detect surface damage on a three-span concrete bridge. The results indicate that this method automatically generates panoramic images of the bridge bottom, deck, and sides with hundreds of millions of pixels and recognizes damage in the panoramas. In addition, the damage detection accuracy reached 98.7%, which is improved by 13.6% when compared with the original network.

GramStatTexNet: Using the Gram Matrix of Multi-Scale Pyramids to Contrastively Learn Texture Model Statistics

GramStatTexNet: Using the Gram Matrix of Multi-Scale Pyramids to Contrastively Learn Texture Model Statistics

Poly(ethylene 2,5-furandicarboxylate) pole figures to determine the microstructural scheme upon uniaxial stretching: Link between orientation and crystallisation

The emergent biobased polymer poly (ethylene 2,5-furandicarboxylate), PEF, has been studied through an innovative approach based on pole figures and orientation factor calculation. PEF uniaxial stretching was performed with different mechanical conditions (different equivalent strain rates), up to several levels of strain and while considering different post-stretching cooling conditions (interrupted, unloaded and ruptured samples). Samples were stretched and interrupted before and after the Natural Draw Ratio (NDR), the deformation for which the material starts strain-hardening. When PEF strain-hardens, it reveals both an increase of the crystalline orientation and crystallinity ratio with the deformation imposed. Unloading the material at temperature tends to decrease partially crystalline orientation, especially regarding the aliphatic part of the chains. Moreover, stretching PEF up to high strains, superior to the NDR, leads to crystal fragmentation. After all, all experimental results were compared to a texture model. It appears that a texture can be developed upon stretching that is close to a fibre texture, as the furan cycles tend to be parallel to the specimen plane.

A Two-Scale Texture Modelling of AA1050 Aluminum Alloy after Accumulative Roll-Bonding (ARB)

Texture evolution during accumulative roll-bonding (ARB) is complicated because of the change in the through-thickness position that results from repeated cutting–stacking and roll-bonding. In this study, a macro–micro two-scale modeling was carried out to investigate the behaviors of texture evolution during ARB. The finite element method (FEM) was used to predict the strain history at a macro-scale, while a crystal plasticity FEM was used to reproduce the texture at a micro-scale. The texture evolution along three different cutting–stacking paths was traced and investigated. The patterns of texture transition between the rolling-type, shear-type, and random-type textures were studied by using area fractions of texture components, the distribution of textures, and the distribution of crystal rotation angles.

3D textured mesh scene data fusion method considering multiple scales and resolutions

ABSTRACT The fusion of 3D textured mesh scene data can effectively and seamlessly integrate multiple spatially related 3D geographical scene models into larger and more comprehensive datasets. Existing methods usually have different degrees of texture distortion and unreduced texture problems in the splicing area, and these methods do not consider the current situation of Level of Detail(LoD) data organisation in the 3D textured mesh model. We propose a 3D textured mesh scene data fusion method considering multiple scales and resolutions. First, seamless extraction of splicing areas was achieved based on spatial relationship constraints and orthogonal inverse projection principles. Second, the sparse scene structure concept was employed to generate sparse summary scene images. Finally, a dual simplification operator for both mesh and texture was proposed to construct multi-resolution 3D textured mesh models, simultaneously reconstructing triangular facets and textures within the splicing regions. We used multiple 3D textured mesh data constructed from aerial obliques to conduct data fusion experimental verification. The results indicate that our method exhibits minimal texture distortion during grid reconstruction in splicing areas and model simplification, effectively reducing the amount of simplified texture data. Additionally, our method adeptly addresses multilevel detailed 3D textured mesh models, demonstrating good feasibility and advancement.

Estimation of the Structure of Hydrophobic Surfaces Using the Cassie-Baxter Equation.

The effect of extreme water repellency, called the lotus effect, is caused by the formation of a Cassie-Baxter state in which only a small portion of the wetting liquid droplet is in contact with the surface. The rest of the bottom of the droplet is in contact with air pockets. Instrumental methods are often used to determine the textural features that cause this effect-scanning electron and atomic force microscopies, profilometry, etc. However, this result provides only an accurate texture model, not the actual information about the part of the surface that is wetted by the liquid. Here, we show a practical method for estimating the surface fraction of texture that has contact with liquid in a Cassie-Baxter wetting state. The method is performed using a set of ethanol-water mixtures to determine the contact angle of the textured and chemically equivalent flat surfaces of AlSI 304 steel, 7500 aluminum, and siloxane elastomer. We showed that the system of Cassie-Baxter equations can be solved graphically by the wetting diagrams introduced in this paper, returning a value for the texture surface fraction in contact with a liquid. We anticipate that the demonstrated method will be useful for a direct evaluation of the ability of textures to repel liquids, particularly superhydrophobic and superoleophobic materials, slippery liquid-infused porous surfaces, etc.

Stochastic geometry models for texture synthesis of machined metallic surfaces: sandblasting and milling

Training defect detection algorithms for visual surface inspection systems requires a large and representative set of training data. Often there is not enough real data available which additionally cannot cover the variety of possible defects. Synthetic data generated by a synthetic visual surface inspection environment can overcome this problem. Therefore, a digital twin of the object is needed, whose micro-scale surface topography is modeled by texture synthesis models. We develop stochastic texture models for sandblasted and milled surfaces based on topography measurements of such surfaces. As the surface patterns differ significantly, we use separate modeling approaches for the two cases. Sandblasted surfaces are modeled by a combination of data-based texture synthesis methods that rely entirely on the measurements. In contrast, the model for milled surfaces is procedural and includes all process-related parameters known from the machine settings.