Regular Texture Research Articles

Use of a Move-on Component to Increase Consumption for a Clinical Paediatric Feeding Case In-Home

AbstractResearch from specialised hospital feeding programmes in the United States has shown effectiveness of a variety of treatments for packing (not swallowing food or liquid in the mouth) to increase swallowing and consumption. One potential component used in clinical practice has not been evaluated in the literature to our knowledge. This component is move-on and involves moving on to the next bite presentation rather than waiting for swallowing (i.e., clean mouth). A 5-year-old female with autism spectrum disorder and avoidant/restrictive food intake disorder participated in a home setting in Australia. We used a withdrawal/reversal single-case experimental design for a move-on component added to a treatment package. With move-on added, latency to clean mouth decreased and consumption increased to 100%. After the treatment evaluation, additional procedures (interspersal, redistribution) were needed in full plate and portion meals. Food variety was increased to 116 regular texture foods across all food groups. All (100%) of admission goals were met. Parents were trained to high procedural integrity, and the protocol was generalised to the community. Gains maintained to 1-month follow-up.

The human visual system preserves the hierarchy of two-dimensional pattern regularity.

Symmetries are present at many scales in natural scenes. Humans and other animals are highly sensitive to visual symmetry, and symmetry contributes to numerous domains of visual perception. The four fundamental symmetries-reflection, rotation, translation and glide reflection-can be combined into exactly 17 distinct regular textures. These wallpaper groups represent the complete set of symmetries in two-dimensional images. The current study seeks to provide a more comprehensive description of responses to symmetry in the human visual system, by collecting both brain imaging (steady-state visual evoked potentials measured using high-density EEG) and behavioural (symmetry detection thresholds) data using the entire set of wallpaper groups. This allows us to probe the hierarchy of complexity among wallpaper groups, in which simpler groups are subgroups of more complex ones. We find that both behaviour and brain activity preserve the hierarchy almost perfectly: subgroups consistently produce lower-amplitude symmetry-specific responses in visual cortex and require longer presentation durations to be reliably detected. These findings expand our understanding of symmetry perception by showing that the human brain encodes symmetries with a high level of precision and detail. This opens new avenues for research on how fine-grained representations of regular textures contribute to natural vision.

Anomaly localization in regular textures based on deep convolutional generative adversarial networks

Anomaly localization in regular textures based on deep convolutional generative adversarial networks

Deep texture cartoonization via unsupervised appearance regularization

Texture plays an important role in cartoon images to represent materials of objects and enrich visual attractiveness. However, manually crafting a cartoon texture is not easy, so amateurs usually directly use cartoon textures downloaded from the Internet. Unfortunately, Internet resources are quite limited and often patented, which restrict the users from generating visually pleasant and personalized cartoon textures. In this paper, we propose a deep learning based method to generate cartoon textures from natural textures. Different from the existing photo cartoonization methods that only aim to generate cartoonic images, the key to our method is to generate cartoon textures that are both cartoonic and regular. To achieve this goal, we propose a regularization module to generate a regular natural texture with similar appearance as the input, and a cartoonization module to cartoffonize the regularized natural texture into a regular cartoon texture. Our method successfully produces cartoonic and regular textures from various natural textures.

Automated woven fabric texture periodicity extraction by spectral analysis and patch-DMF

Texture provides important visual information for object identification, and texture representation is still a challenging problem in texture analysis. Fabric texture is a typical structural texture, whose periodic behavior plays a vital role in texture discrimination. In this work, an approach to extract the periodicity of fabric texture is proposed, in which the classic spectral analysis method Fourier transform is applied to select underlying frequency peaks corresponding to the texture periodicities given its efficiency and global property; then the patch-DMF based on the distance match function is built to evaluate the best match texture periodicity. The experiments on near-regular textures (plain weave fabric) demonstrate that the performance of the proposed method shows accurate results with near and regular fabric textures compared to others, and being practical usefulness for texture description.

Laboratory and numerical experimentation for masonry in compression

In this paper, the initial part of a laboratory and numerical experimental campaign dedicated to historical masonry is described. One leaf masonry panels with regular texture are built in order to simulate a historical material characterised by strong resisting elements and weak mortar joints. Laboratory tests are first dedicated to masonry components and then to the behaviour in compression of masonry panels, which is applied both orthogonal and parallel to bed joints, in order to highlight the orthotropic behaviour of the material. First of all, the mechanical parameters of masonry constituents are calibrated and then a heterogeneous finite element model is introduced and calibrated for reproducing the orthotropic behaviour of masonry, together with the initial elastic response and the initial nonlinear behaviour due to the first level of damage.

An Adaptive Pyramid Graph and Variation Residual-Based Anomaly Detection Network for Rail Surface Defects

Anomaly detection is a crucial means to detect unbalanced and multi-class abnormal data in industrial products. The existing anomaly detection model is usually suitable for products with a simple or regular background texture. However, there is a lack of a universal surface defect anomaly detection framework for railway products when facing the challenges: the complex background texture, huge intra-class variation, uneven distribution of inter-class, and weak texture-featured defects. To fill this gap, an innovative generative adversarial network based on adaptive pyramid graph and variation residuals (APGVR-GAN) is proposed, aiming to improve the robustness of anomaly detection in railway products and other complex industrial supplies. First, the adaptive pyramid graph module is embedded in the encoder-decoder-encoder pattern, capturing the correlation description between neighbor regions, which is utilized to enhance the detection of abnormal defects with weak texture. Next, the variation residual module is employed to enhance the expression of various normal samples in the latent space, and improve the identification ability for abnormal samples. Then, the dual-probability prototype loss is proposed to make different normal samples have more concentrated expression and more similar probability distribution centers in latent space. Finally, an adaptive focal-gate loss and a regularized log-likelihood loss are designed to overcome the imbalance problem in training samples with different background information. The effectiveness of the model is verified on three new railway datasets and three other industrial public datasets. The results show that in anomaly detection, APGVR-GAN on the three railway data sets NEU-RSDD-1 dataset, NEU-RSDD-1 dataset and RSDD AUC results can reach 0.89, 0.81, and 0.87, and its detection accuracy reached 0.97, 0.96 and 0.92, which is much higher than other comparative anomaly detection algorithms. In the segmentation experiments on the RSDD dataset, compared with other known rail defect detection methods, the overall evaluation metric F-Score is 83.70 which also better than other methods.

Fast, Nonlocal and Neural: A Lightweight High Quality Solution to Image Denoising

With the widespread application of convolutional neural networks (CNNs), the traditional model based denoising algorithms are now outperformed. However, CNNs face two problems. First, they are computationally demanding, which makes their deployment especially difficult for mobile terminals. Second, experimental evidence shows that CNNs often over-smooth regular textures present in images, in contrast to traditional non-local models. In this letter, we propose a solution to both issues by combining a nonlocal algorithm with a lightweight residual CNN. s solution gives full latitude to the advantages of both models. We apply this framework to two GPU implementations of classic nonlocal algorithms (NLM and BM3D) and observe a substantial gain in both cases, performing better than the state-of-the-art with low computational requirements. Our solution is between 10 and 20 times faster than CNNs with equivalent performance and attains higher PSNR. In addition the final method shows a notable gain on images containing complex textures like the ones of the MIT Moiré dataset.

OCT-Based Angiography and Surface Topography in Burn-Damaged Skin.

There is a clinical need for an accurate, non-invasive imaging tool that can provide the objective assessment of burn wounds. The aims of this study are to demonstrate the potential of optical coherence tomography (OCT) in evaluating burn wound healing, as well as exploring the physiological basis of human wound healing. This was a retrospective study. Seven patients with severe burn wounds who were admitted to Harborview Medical Center were imaged using an in-house-built, clinical-prototype OCT system. OCT imaging was carried out at multiple scan sites on the burned skin across two time points (imaging session #1 and #2) with a field of view of ~9 × 9 mm. Due to pathological differences among burn zones, scan sites were classified into red sites (zone of hyperemia), white sites (zone of coagulation), and mixed sites. In addition to obtaining qualitative en face vascular and surface topography maps, we quantified vascular area density and surface roughness for comparative purposes. En face vascular and surface topography maps demonstrated numerous morphological changes over both imaging sessions associated with burn injury, such as altered blood flow and loss of regular texture. Quantitative analyses revealed that during imaging session #1, vascular area density was significantly increased within the red sites compared with that of a healthy control (P = 0.0130), while vascular area density was significantly decreased within the white sites compared with that of a healthy control (P < 0.0001). During imaging session #2, vascular area density was significantly reduced to a more normal range within the red sites compared with imaging session #1 (P = 0.0215); however, vascular area density was still significantly lower within the white sites compared with that of a healthy control (P < 0.0001). Furthermore, vascular area density and surface roughness were significantly increased within the white sites during imaging session #2 compared with imaging session #1 (both P < 0.0001). OCT is clinically feasible to monitor vascular changes and alterations in skin surface roughness during the process of burn wound healing. Variations in vascular area density and roughness measurements within the burn wounds revealed by OCT offer some key insights into the underlying pathophysiological mechanisms responsible for wound healing, which may become critical biological indicators in future clinical evaluation and monitoring of wound healing. Lasers Surg. Med. © 2020 Wiley Periodicals LLC.

Surface defect detection of machined parts based on machining texture direction

Compared with detecting the regular texture of fabrics and prints, the detection of the processed texture on the surface of mechanical parts is more difficult. To quickly and accurately detect defects caused by abnormal machining of the surface of metal parts, a one-shot machine-vision method based on a texture orientation histogram is proposed. An improved Mean-C local threshold method is proposed to solve the problem of difficulty in extracting surface texture. Using the minimum enclosing rectangle, the skeleton texture is extracted from the enhanced image obtained by the improved Mean-C local threshold. The statistical information from the histogram is used to pre-process the texture direction, and then a novel angle region growth method proposed in this paper is used to search the main texture cluster and the abnormal texture cluster of the part, so as to realize the product quality detection. Experimental results show that this method is highly targeted for the detection of surface texture defects caused by abnormal processing, which is equivalent to the average performance of a multi-angle illumination detection system, but much faster. This detection method has high detection efficiency, high accuracy, and strong robustness, and can meet the requirements of industrial detection.

Treatment for Younger Siblings of Participants in a Home-Based Intensive Paediatric Feeding Programme in Australia

AbstractEarly childhood feeding problems can be challenging. Children who limit their food consumption may significantly impact multiple critical areas of development. Effective treatment should be accessed as early as possible but has been limited to a handful of US hospital programmes. Feeding problems affect both children with and without disability, and families may struggle with multiple children having feeding difficulties. We provided short-term (less than 2 weeks), in-home, intensive, behaviour-analytic feeding intervention to two children with typical development who were younger siblings of children already in the programme. We used a withdrawal/reversal design to assess the effects of nonremoval of the spoon, re-presentation, contingent and noncontingent access to tangibles, differential attention, and response cost. This multi-component intervention was effective in increasing the consumption of a wide variety of foods at regular texture and self-feeding for both participants. Variety was increased to over 60 foods from all food groups. Admission goals were met (100%). We trained caregivers to high procedural integrity and generalised the protocol. We provided actual plate picture examples of family meals consumed where the brothers and parents ate the same meal. Caregiver satisfaction and social acceptability were high. Gains were maintained at 3-year follow-up where parents reported problems were fully resolved.

Are Medication Swallowing Lubricants Suitable for Use in Dysphagia? Consistency, Viscosity, Texture, and Application of the International Dysphagia Diet Standardization Initiative (IDDSI) Framework.

Medication lubricants are thick liquids or gels that are designed to aid swallowing of solid oral dosage forms. Tablets and capsules are placed within a spoonful of the product for swallowing. The aim of this study was to describe and compare commercially available medication lubricants in terms of textural suitability for patients with dysphagia. Twelve medication lubricants were characterised according to the International Dysphagia Diet Standardisation Initiative (IDDSI) framework. Apparent viscosity, yield stress, thickness consistency, and various texture features were compared. Gloup Forte was the only medication lubricant classified as IDDSI level 4 (pureed/extremely thick) at room (24 °C) temperature. Four other Gloup products were IDDSI level 3 (liquidised/moderately thick) at room temperature but testing at 4 °C or pouring from the container instead of using the pump dispenser resulted in classification as IDDSI level 4. The IDDSI Flow test would have classified MediSpend and Slo Tablets as IDDSI level 3, but their very low yield stress led to these fluids flowing too quickly through the prongs of a fork and so these were classified as <3. Severo was IDDSI level 2. Heyaxon and the two versions of Magic Jelly tested contained lumps, and Swallow Aid had exceptionally high viscosity, hardness, adhesiveness, and gumminess, classifying them as IDDSI Level 7 (“regular textures”) and therefore as unsuitable for people with dysphagia according to IDDSI. This study provides valuable information to help with the selection of a safe medication lubricant with appropriate thickness level suited to each individual with dysphagia.

A study about the impact of the topological arrangement of fibers on fiber-reinforced composites: Some guidelines aiming at the development of new ultra-stiff and ultra-soft metamaterials

Fiber-reinforced composites are materials that display a great potentiality in many applications for their multifaceted features. The employment of curved fibers contributes to enhancing this already wide range of possibilities further. In this paper, we propose two particular fiber arrangements with the intention of realizing ultra-stiff and ultra-soft composites. For this purpose, we explore the texture based on isostatic lines, which is bio-inspired to obtain a high strength composite and the pantographic pattern, which enables considerable displacement with very low resistance. To model these particular lattice plates, we consider an in-plane orthotropic lamina within the framework of the strain-gradient elasticity theory. Numerical simulations have been performed using the finite element method to investigate the mechanical behavior of the considered textures. A regular straight texture with fibers parallel to the edges of a rectangular sample has been taken into account as reference texture for comparison. Through some tests, we show that for the considered textures, namely the isostatic and the pantographic ones, the displacement force graphs differ by several orders of magnitude, justifying the characterization of their behavior.

Multiple texture cues are integrated for perception of 3D slant from texture.

The projected image of a textured surface contains multiple texture cues to three-dimensional (3D) surface orientation. Previous studies have reported conflicting findings about the roles of various texture cues. We tested the influence of texture compression relative to other texture cues using a cue conflict paradigm. Observers viewed images of textured planar surfaces with varied slants (0°–70°) and estimated 3D slant by aligning their hand with the virtual surface. Conflicts between texture cues were created by stretching or compression the texture along the surface, which selectively changes the slant specified by texture compression. The texture distortions were relatively small (±10% or ±20%) to limit the size of the cue conflicts. Across three experiments, we varied the field of view (10° vs. 20°), texture regularity (circles vs. Voronoi), and availability of binocular cues. In monocular conditions, slant estimates were strongly affected by texture distortions. Analyses of cue weighting found that texture compression had more influence on slant settings than other texture cues and the relative influence of texture compression decreased with larger field of view and less regular textures. In binocular conditions, we also observed effects of texture distortion, and the influence of texture compression relative to information from stereo and other texture cues increased with slant. Our results provide evidence that texture compression contributes to perceived slant, in addition to other texture cues such as texture scaling. The observed effects of simulated slant, field of view, and texture regularity on cue weighting were all consistent with a model that integrates multiple sources of information according to their reliability.

Increasing food texture and teaching chewing for a clinical case within the home setting in Australia

Children with paediatric feeding disorders may not naturally develop typical chewing skills to eat age-appropriate food textures. There are only a handful of studies on teaching chewing, and even less for children without any chewing history. Additional research is needed on increasing food texture and chewing skills, particularly internationally in settings outside of intensive specialised interdisciplinary hospitals. A 5-year-old male with avoidant/restrictive food intake disorder (ARFID), autism spectrum disorder, iron deficiency, low weight, and history of constipation who had never chewed (not even infant dissolvables) or eaten texture other than baby food in his life participated. He had failed treatment attempts by multiple disciplines for over 4 years, including a week-long inpatient interdisciplinary feeding hospitalisation. We used a modified multiple baseline probe across food textures design. After 3 weeks of solely behaviour-analytic treatment, Junot was eating (chewing and swallowing) a full plate of regular texture portions of a variety of 109 foods from all food groups. He learned to bite off, chew, lateralise, masticate, judge, and swallow a wide variety of regular texture foods, including some meats and raw fruit and vegetables. We trained his parents to implement the protocol with high integrity. He met all 100 % of goals. Parents reported high satisfaction and social acceptability and gains were maintained to 1-year follow-up.

Global-best optimization of ANN trained by PSO using the non-extensive cross-entropy with Gaussian gain

An efficient optimization of network weights has been the primary goal of the artificial neural network (ANN) research community since decades. The aim of every optimization problem is to minimize the network cost which is some form of error function between the desired and the actual network outputs, during the training phase. The conventional gradient-based optimization algorithms like backpropagation are likely to get trapped in local minima and are sensitive to choices of initial weights. The evolutionary algorithms have proved their usefulness in introducing randomness into the optimization procedure, since they work on a global search strategy and induce a globally minimum solution for the network weights. In this paper, we particularly focus on ANN trained by Particle Swarm Optimization (ANN-PSO), in which the local-best and global-best particle positions represent possible solutions to the set of network weights. The global-best position of the swarm, which corresponds to the minimum cost function over time, is determined in our work by minimizing a new non-extensive cross-entropy error cost function. The non-extensive cross-entropy is derived from the non-extensive entropy with Gaussian gain that has proven to give minimum values for regular textures containing periodic information represented by uneven probability distributions. The new cross-entropy is defined, and its utility for optimizing the network weights to a globally minimum solution is analyzed in this paper. Extensive experimentation on two different versions: the baseline ANN-PSO and one of its most recent variants IOPSO-BPA, on benchmark datasets from the UCI repository, with comparisons to the state of the art, validates the efficacy of our method.

Effects of high differential stress and mineral properties on deformation and failure mechanism of hard rocks

To study the deformation and failure mechanism of hard rocks, true triaxial compression tests were conducted on four types of hard rocks to obtain the complete stress–strain curves and failure modes. Under true triaxial compression conditions, the shapes of the complete stress–strain curves can be divided into three types: elastic–brittle (EB), elastic–plastic–brittle (EPB), and elastic–plastic–ductile (EPD) types. According to the different post-peak deformation behaviours, the stress–strain curves of EPB type can be subdivided into three sub-categories: post-peak instantaneous brittle (EPB-I), post-peak multi-stage brittle (EPB-M), and post-peak delayed brittle (EPB-D). The stress–strain curves change from EPD to EPB-D, EPB-M, EPB-I, and finally EB with increasing differential stress (σ2– σ3). The deformation characteristics are dependent on intermediate principal stress σ2, minimum principal stress σ3, mineral compositions, and mineral textures of rock samples. An increase in σ3leads to an increased ductility, while an increase in σ2leads to an increased brittleness. Moreover, rocks with regular mineral textures and low mineral hardness are more prone to ductility. When the deformation curve is transformed from EPD to EPB and then to EB, the tensile crack gradually predominates, and the macroscopic failure angle gradually becomes steeper.

Surface characteristics of dental resin nanoceramic in in vitro finishing with dental handpiece and rotary burs

Surface roughness and texture are critical characteristics of dental restorative materials in determining the clinical performance of dental prostheses. This study aimed to quantitatively assess the effects of dental rotary finishing process, necessarily involved in clinical practice, on surface roughness and texture of dental resin nanoceramic (RNC). In vitro dental finishing was conducted on a computer-assisted apparatus with a dental handpiece and diamond burs. Four clinically applied finishing parameters (diamond grit size, depth of cut, feed rate and rotational speed) were selected. The finished surface topographies were measured based on optimized sampling conditions. The 3D amplitude parameters of Sq and Sz, functional parameters of Sbi and Sci, and spatial texture parameters of Sal and Str were obtained using a 3D no-contact chromatic confocal profilometer, and then were calculated and statistically analyzed using ANOVA for surface characterization. The results show that diamond grit size had significant effects on the all surface parameters of Sq, Sz, Sbi (P < 0.01), and Sal, Str, Sci (P < 0.05), while bur rotational speed was irrelevant to the parameters (P > 0.05). The functional parameters of Sbi and Sci relied mainly on diamond grit size and feed rate (P < 0.05). Standard bur and medium bur easily caused deep valleys on the RNC surface, while finer burs or slower feed rates could improve bearing property but diminish core liquid remained property. Small girt size, slower feed rate and decreased rotational speed could generate more regular and finer surface texture. Implications concerning the relationship between surface roughness and finishing process parameters were provided to facilitate the process selection and quality control of RNC prostheses.

Dialectical Observation of Controllable Electrodeposited Ni Nanocones: the Unification of Local Disorder and Overall Order

Dense and ordered Ni nanocones with regular spiral textures had been successfully synthesized via a simple and inexpensive electrodeposition process in the solution containing sodium chloride (NaCl), nickel chloride hexahydrate (NiCl2·6H2O), and boric acid (H3BO3). After analyzing the microstructure, a more optimized possible growth mechanism of Ni nanocones was proposed, in which the growth process was divided into local and global aspects, named multi-dimensional growth mechanism of global order and local disorder. In an area small enough, any subtle state changes would cause disorder of Ni atom arrangement, which made the local microstructure appear disordered, but from a macro perspective, the difference between two adjacent disorders caused by different statuses was too small to be well reflected, only when the difference in state was large enough can the change be observed in the macroscopic appearance, so the global was orderly. Meanwhile, we found that the microstructure of Ni nanocones would be controlled in the electrodeposition solution by adjusting the experiment parameters such as the concentration of NaCl, NiCl2·6H2O, and H3BO3, which indirectly determined the microstructure in a large extent via controlling the generation of intermediate products and the pH.

Numerical homogenization‐based seismic assessment of an English‐bond masonry prototype: Structural level application

SummaryA multiscale strategy is evaluated at a structural level for the analysis of unreinforced masonry structures. The mechanical characterization of the masonry is deduced from homogenization‐based micro‐scale finite element (FE) models. The derived data are here employed at a structural level via a discrete FE model. The discrete FE model is composed of quadrilateral rigid plates interconnected through vertical and horizontal interfaces. On the interfaces, between adjoining discrete elements, a model that accounts for the in‐ and out‐of‐plane behavior of masonry, with damage and plasticity, is adopted. Such interfaces represent the material pre‐ and post‐peak regimes, its orthotropy, and, depending on the micro‐model assumed, account by three‐dimensional shear effects that are especially important for multi‐leaf walls and complex regular textures. The discrete model has been implemented in an advanced structural analysis software where powerful built‐in features as the arc‐length method, line‐search algorithm, and implicit or explicit solver schemes are available.The multi‐scale model is applied for the dynamic study of a small English‐bond masonry house prototype subjected to a series of consecutive earthquake records. Detailed comparisons between the experimental and numerical data are presented, including the results obtained through a continuous total strain rotating crack model. Quasi‐static and dynamic analyses are conducted. Results demonstrate that when enough experimental information is available on the masonry components under tension, shear, and compression regimes, the approach predicts well the seismic structural response in terms of time‐history displacements, seismic capacity, and damage patterns. The required computational cost (CPU time) is very attractive.