Texture Identification Research Articles

Crystalline texture of cobalt nanowire arrays probed by the switching field distribution and FORC diagrams

The crystalline texture of arrays of low diameter Co nanowires (NWs) synthesized by electrodeposition using electrolytes with different acidities (pH in the range 2.0–6.6) was studied by the switching field distribution (SFD) and first order reversal curve (FORC) diagrams. Particularly, the SFD determined as the derivative of the descending part of the major hysteresis loop has proven to be a reliable and powerful method for the identification of different crystalline textures in the NWs and the quantification of their corresponding texture percentages. The presence of the face centered cubic-like texture at low pH values and hexagonal close packed textures with the c-axis perpendicular and parallel to the NWs axis at higher pH values have been identified by performing multiple Gaussian fits to the SFD by virtue of their different magnetic behavior observed during reversal of the magnetization. The field position and size of each curve in the multiple Gaussian fit provide information about the corresponding magnetic contribution and volumetric texture percentage of each crystalline texture in the NWs, respectively. The analysis of the SFD has been complemented and validated with FORC diagram measurements, showing that the width of the coercive field distribution is in good agreement with the width of the SFD. Also, it has been found that the different branches observed in the FORC diagrams along the interaction axis provide further insight on the interaction between magnetocrystalline fields. This work provides a novel methodology for the use of magnetometry as a reliable technique for the study of the interplay between the microstructure and magnetic behavior of arrays of NWs.

Термометричні дослідження флюїдних включень у баденському галіті Карпатського регіону в контексті встановлення глибини солеродного басейну

It was established that in order to avoid errors in the interpretation of paleotectonic conditions of salt formation based on fluid inclusions in halite, the primary stage of the research should be the genetic identification of the sedimentation textures of halite and fluid inclusions in this mineral. For the thermometric study of inclusions and to determine the depth of the sedimentation basin based on the obtained data, only thermal test chambers are suitable which provide the possibility of observing groups of inclusions in different zones of sedimentary halite, as, for example, in the micro thermal test chamber designed by Prof. V. A. Kalyuzhny. In the course of the research, the equipment of the thermometric method, which is based on the use of a microthermal test chamber designed by V. A. Kalyuzhny, was modernized. In particular, the material of the thermal chamber (stainless steel) was replaced with copper, which made it possible to avoid excessive thermal gradients into chamber and to increase the permissible heating rate by 20 times due to the higher thermal conductivity of copper. For the same purpose, the glass optical windows of the camera were replaced with leukosapphire windows, which have a much higher thermal conductivity. The measuring system of the installation is made on a miniature platinum resistance thermometer with an electronic measuring unit. These improvements made it possible to achieve high system stability and good reproducibility of measurement results. Using the thermometric method, it was established that the temperature of sedimentation at the bottom of the Badenian salt basin of the Carpathian region was 19.5–20.5; 20.0–22.0; 24.0–26.0 °C, and on the surface of the brine was 34.0–36.0 °C. On this basis, a model of the basin with a pronounced thermocline and a total thickness of the water column of up to 30 meters was built, which is the most likely to establish the features of sedimentation. Crystallization of halite at different depths in basins with a thermocline can explain the presence of so-called “low-temperature” (24.0–25.0 °C) and “high-temperature” (37.8–42.6 °C) bottom halite in a number of ancient salt-bearing basins.

SKIN TYPE CLASSIFIER

Variations in skin diseases largely fall into the subset of skin types. Classification of Skin Types plays an essential part in the classification of skin diseases. The paper proposes an end to-end machine learning model used to classify skin type as dry, oily, or normal using Python Libraries. Skin type classification is done using Gray-Level Co-Occurrence Matrix (GLCM) for texture identification and Light Gradient Boosting Machine (LGBM). Red Green Blue (RGB) images are first resized and then converted into Gray-Scale images and then features like energy, correlation, dissimilarity, homogeneity, contrast, and entropy are extracted from a given image using GLCM. Such features give us a basic understanding of the type and texture of the image. The GLCM of the image is calculated by changing the distance and angle of the pixels within the image in a regular interval. These features after extraction are fed to the LGBM algorithm. The trained model is then deployed using Flask. Key Words: features extraction, GLCM, LGBM, skin type

Rapid and Accurate Prediction of Soil Texture Using an Image-Based Deep Learning Autoencoder Convolutional Neural Network Random Forest (DLAC-CNN-RF) Algorithm

Soil determines the degree of water infiltration, crop nutrient absorption, and germination, which in turn affects crop yield and quality. For the efficient planting of agricultural products, the accurate identification of soil texture is necessary. This study proposed a flexible smartphone-based machine vision system using a deep learning autoencoder convolutional neural network random forest (DLAC-CNN-RF) model for soil texture identification. Different image features (color, particle, and texture) were extracted and randomly combined to predict sand, clay, and silt content via RF and DLAC-CNN-RF algorithms. The results show that the proposed DLAC-CNN-RF model has good performance. When the full features were extracted, a very high prediction accuracy for sand (R2 = 0.99), clay (R2 = 0.98), and silt (R2 = 0.98) was realized, which was higher than those frequently obtained by the KNN and VGG16-RF models. The possible mechanism was further discussed. Finally, a graphical user interface was designed and used to accurately predict soil types. This investigation showed that the proposed DLAC-CNN-RF model could be a promising solution to costly and time-consuming laboratory methods.

Tactile Texture Display Combining Vibrotactile and Electrostatic-friction Stimuli: Substantial Effects on Realism and Moderate Effects on Behavioral Responses

There is increasing demand for tactile feedback functions for touch panels. We investigated whether virtual roughness texture quality can be improved through simultaneous use of vibrotactile and electrostatic-friction stimuli. This conjunctive use is expected to improve the perceptual quality of texture stimuli, because vibrotactile and electrostatic-friction stimuli have complementary characteristics. Our previous studies confirmed that these conjunct stimuli yield enhanced realism for simple grating roughness. In this study, we conducted experiments using simple and complex sinusoidal surface profiles consisting of one or two spatial wave components. Three different evaluation criteria were employed. The first criterion concerned the subjective realism, i.e., similarity with actual roughness textures, of virtual roughness textures. Participants compared the following three stimulus conditions: vibrotactile stimuli only, electrostatic-friction stimuli only, and their conjunct stimuli. The conjunct stimuli yielded the greatest realism. The second criterion concerned roughness texture identification under each of the three stimulus conditions for five different roughness textures. The highest identification accuracy rate was achieved under the conjunct stimulus condition; however, the performance difference was marginal. The third criterion concerned the discrimination threshold of the grating-scale spatial wavelength. There were no marked differences among the results for the three conditions. The findings of this study will improve virtual texture quality for touch-panel-type surface tactile displays.

Theme as point of departure in English and Spanish casual conversation

Abstract This paper uses a trinocular approach – i.e. from below, from above and from roundabout – to describe Theme as point of departure (POD) in English and Spanish casual conversation (thus avoiding the more controversial ‘what-the-clause-is-about’ aspect of Theme). This research is part of a long-term contrastive study which includes the description of POD in academic writing and a crosslinguistic comparison of POD in academic writing and casual conversation. Among the main findings in this paper, the following stand out: (a) the identification of a conversational texture characterised by the interplay of textual and interpersonal strands of meaning; and (b) different POD arrangements of English and Spanish clauses in casual conversation, motivated by different ways of enacting communicative exchange.

Ferroelectricity controlled chiral spin textures and anomalous valley Hall effect in the Janus magnet-based multiferroic heterostructure

Realizing effective manipulation and explicit identification of topological spin textures are two crucial ingredients to make them as information carrier in spintronic devices with high storage density, high data handling speed and low energy consumption. Electric-field manipulation of magnetism has been achieved as a dissipationless method compared with traditional regulations. However, the magnetization is normally insensitive to the electric field since it does not break time-reversal symmetry directly, and distribution of topological magnetic quasiparticles is difficult to maintain due to the drift arising from external fluctuation, which could result in ambiguous recognition between quasiparticles and uniform magnetic background. Here, we demonstrate that electric polarization-driven skyrmionic and uniform ferromagnetic states can be easily and explicitly distinguished by transverse voltage arising from anomalous valley Hall effect in the Janus magnet-based multiferroic heterostructure LaClBr/In2Se3. Our work provides an alternative approach for data encoding, in which data are encoded by combing topological spin textures with detectable electronic transport.

Mineral Texture Identification Using Local Binary Patterns Equipped with a Classification and Recognition Updating System (CARUS)

In this paper, a rotation-invariant local binary pattern operator equipped with a local contrast measure (riLBPc) is employed to characterize the type of mineral twinning by inspecting the texture properties of crystals. The proposed method uses photomicrographs of minerals and produces LBP histograms, which might be compared with those included in a predefined database using the Kullback–Leibler divergence-based metric. The paper proposes a new LBP-based scheme for concurrent classification and recognition tasks, followed by a novel online updating routine to enhance the locally developed mineral LBP database. The discriminatory power of the proposed Classification and Recognition Updating System (CARUS) for texture identification scheme is verified for plagioclase, orthoclase, microcline, and quartz minerals with sensitivity (TPR) near 99.9%, 87%, 99.9%, and 96%, and accuracy (ACC) equal to about 99%, 97%, 99%, and 99%, respectively. According to the results, the introduced CARUS system is a promising approach that can be applied in a variety of different fields dealing with classification and feature recognition tasks.

Utilizing Piezo Acoustic Sensors for the Identification of Surface Roughness and Textures.

This study examines surface roughness measurements via piezo acoustic disks and appropriate signal processing. Surface roughness is one characteristic of surface texture that can have various irregularities inherent to manufacturing methods. The surface roughness parameters and corresponding surface profiles are acquired by a stylus profilometer. Simultaneously, elastic waves propagated along metal surfaces caused by the friction of a diamond tip are obtained in the form of raw sound via piezo acoustic disks. Frequency spectrum analysis showed apparent correlations between the traditionally obtained measurement parameters and the piezo acoustic measurement data. Thus, it is concluded that acoustic friction measurement shows promising results as a novel measurement method for the surface roughness states of certain materials.

Swarm optimization based texture classification in extreme scale variations

Texture analysis has remained a remarkably trendsetting and productive field of research in the last two decades. There has been much progress, but the impact of illumination changes on automated texture classification and segmentation has gained very less focus. Research work carried out in the field of texture identification frequently focuses on the identification of textures with intraclass changes including illumination, rotation, viewpoint and small scale variations. Consequently, variations in texture owing to modifications in scale constitute among the ones that are difficult to manage. In this research work, as the first step, the classification of textures due to vast changes in scale is studied. In order to deal with this problem, first the solution is introduced and then the scale changes are reduced based on the predominant patterns in the texture. Inspired by the challenges imposed by this issue, a novel swarm intelligence approach known as Ant Colony Optimization (ACO) algorithm is introduced for modifying the components in the hidden layers used during the network training, for the extraction of more useful semantic texture patterns.

Texture Identification and Object Recognition Using a Soft Robotic Hand Innervated Bio-Inspired Proprioception

In this study, we innervated bio-inspired proprioception into a soft hand, facilitating a robust perception of textures and object shapes. The tendon-driven soft finger with three joints, inspired by the human finger, was detailed. With tension sensors embedded in the tendon that simulate the Golgi tendon organ of the human body, 17 types of textures can be identified under uncertain rotation angles and actuator displacements. Four classifiers were used and the highest identification accuracy was 98.3%. A three-fingered soft hand based on the bionic finger was developed. Its basic grasp capability was tested experimentally. The soft hand can distinguish 10 types of objects that vary in shape with top grasp and side grasp, with the highest accuracies of 96.33% and 96.00%, respectively. Additionally, for six objects with close shapes, the soft hand obtained an identification accuracy of 97.69% with a scan-grasp method. This study offers a novel bionic solution for the texture identification and object recognition of soft manipulators.

Paleoenvironmental Reconstruction of Glaciovolcanism in the Cascades of the Pacific Northwest: Implications of Potential Habitable Environments on Mars

The interaction between volcanic processes and glacial ice is termed glaciovolcanism and is a process that is prevalent on Earth and potentially once prevalent on Mars. Such processes are dynamic, forming potentially habitable environments under glacial ice. This work will attempt to describe the glaciovolcanic deposits at the Crazy Hills and Lone Butte in central Washington and evaluate the potential habitability of these glaciovolcanoes. The identification of pillow lavas and thick hyaloclastite beds point to the hosting glacier being at least 300m thick, although precise estimation is not possible at this time. X-ray fluorescence results show that the two provinces shared a magmatic source deep in the mantle and evolved separately to form the Crazy Hills basalt and Lone Butte basaltic andesite. The identification of putative biogenic alteration textures in the pillow rims of the Crazy Hills basalt indicates a stronger potential for habitable conditions in a glaciovolcanic environment.

Circumferential Local Ternary Pattern: New and Efficient Feature Descriptors for Anti-Counterfeiting Pattern Identification

An important aspect of querying whether a product is likely to be forged is to identify its anti-counterfeiting label. However, the use of image processing technology for label-specific texture analysis to quickly and effectively identify the anti-counterfeiting label has been widely studied. Aiming at the defects of the local binary pattern (LBP) and its variants in texture identification, this paper proposes a new texture model for anti-counterfeiting identification, that is, the circular local ternary pattern (CLTP). The highlight of our technology is that it extracts the effective local texture descriptors by using the random features of inkjet printing. This allows for the technology to not only resist the interference of noise and illumination in images of anti-counterfeiting patterns but also to encode and reorganize the fine linear shape structure. Specifically, this paper extracts the CLTP texture feature in the corresponding key areas and forms the final feature histogram vector for comparison through the one-to-one correspondence between the sample image and the inspected image of anti-counterfeiting pattern. Experiments prove that our method not only has high discrimination, stability and effectiveness but also provides a convenient and practical idea for anti-counterfeiting technology.

Comparative study on classification of machined surfaces using ML techniques applied to GLCM based image features

The identification of the machined surface texture is very crucial in modern manufacturing industries. The surface texture analysis using machine vision, image processing, and classification using ML is a well-known domain of research in the last many years. This manuscript addresses the classification of machined surfaces (turned, ground, and shaped) using image processing and ML techniques. The machined surface images are captured using a DSLR camera, pre-processed, and partitioned into sixteen equal, nonoverlapping regions. The partitioned images are processed to extract the GLCM based features. The extracted features are fed to the ML classifiers such as decision tree, K-nearest neighbour, logistic regression, Naïve Bayes classifier, random forest, and support vector machine. All the ML techniques can be used for the classification of machined surface images. For this work, the random forest technique was found to provide the best performance in image classification.

Segmentation and identification of spectral and statistical textures for computer medical diagnostics in dermatology.

An important component of the computer systems of medical diagnostics in dermatology is the device for recognition of visual images (DRVI), which includes identification and segmentation procedures to build the image of the object for recognition. In this study, the peculiarities of the application of detection, classification and vector-difference approaches for the segmentation of textures of different types in images of dermatological diseases were considered. To increase the quality of segmented images in dermatologic diagnostic systems using a DRVI, an improved vector-difference method for spectral-statistical texture segmentation has been developed. The method is based on the estimation of the number of features and subsequent calculation of a specific texture feature, and it uses wavelets obtained by transforming the graph of the power function at the stage of contour segmentation. Based on the above, the authors developed a modulus for spectral-statistical texture segmentation, which they applied to segment images of psoriatic disease; the Pratt's criterion was used to assess the quality of segmentation. The reliability of the classification of the spectral-statistical texture images was confirmed by using the True Positive Rate (TPR) and False Positive Rate (FPR) metrics calculated on the basis of the confusion matrix. The results of the experimental research confirmed the advantage of the proposed vector-difference method for the segmentation of spectral-statistical textures. The method enables further supplementation of the vector of features at the stage of identification through the use of the most informative features based on characteristic points for different degrees and types of psoriatic disease.

Sensitivity of neural responses in the inferior colliculus to statistical features of sound textures

Previous psychophysical studies have identified a hierarchy of time-averaged statistics which determine the identity of natural sound textures. However, it is unclear whether the neurons in the inferior colliculus (IC) are sensitive to each of these statistical features in the natural sound textures. We used 13 representative sound textures spanning the space of 3 statistics extracted from over 200 natural textures. The synthetic textures were generated by incorporating the statistical features in a step-by-step manner, in which a particular statistical feature was changed while the other statistical features remain unchanged. The extracellular activity in response to the synthetic texture stimuli was recorded in the IC of anesthetized rats. Analysis of the transient and sustained multiunit activity after each transition of statistical feature showed that the IC units were sensitive to the changes of all types of statistics, although to a varying extent. For example, we found that more neurons were sensitive to the changes in variance than that in the modulation correlations. Our results suggest that the sensitivity of the statistical features in the subcortical levels contributes to the identification and discrimination of natural sound textures.

A new feature extraction approach based on non linear source separation

A new feature extraction approach is proposed in this paper to improve the classification performance in remotely sensed data. The proposed method is based on a primary sources subset (PSS) obtained by nonlinear transform that provides lower space for land pattern recognition. First, the underlying sources are approximated using multilayer neural networks. Given that, Bayesian inferences update unknown sources’ knowledge and model parameters with information’s data. Then, a source dimension minimizing technique is adopted to provide more efficient land cover description. The support vector machine (SVM) scheme is developed by using feature extraction. The experimental results on real multispectral imagery demonstrates that the proposed approach ensures efficient feature extraction by using several descriptors for texture identification and multiscale analysis. In a pixel based approach, the reduced PSS space improved the overall classification accuracy by 13% and reaches 82%. Using texture and multi resolution descriptors, the overall accuracy is 75.87% for the original observations, while using the reduced source space the overall accuracy reaches 81.67% when using jointly wavelet and Gabor transform and 86.67% when using Gabor transform. Thus, the source space enhanced the feature extraction process and allow more land use discrimination than the multispectral observations.

Unveiling the Invisible in Uffizi Gallery’s Drawing 8P by Leonardo with Non-Invasive Optical Techniques

Until recently, the study of drawings by old masters has been confined to the art history conservation field. More specifically, scientific investigations of Leonardo’s drawings are still very few, possibly due to the latter’s extreme fragility and artistic value. However, analytical data are crucial to develop a solid knowledge base of the drawing materials and techniques used by artists in the past. In this work, we report on the application of non-invasive optical techniques on a double-sided drawing by Leonardo belonging to the Uffizi Gallery (8P). We used multispectral reflectography in the visible (Vis) and near-infrared (NIR) regions to obtain a spectral mapping of the drawing materials, to be subsequently integrated with technical information provided by art historians and conservators. Morphological analysis by microprofilometry allowed for the identification of the typical wave-like texture impressed in the paper during the sheet’s manufacture, as well as of further paper-impressed traits attributable to the drawing transfer method used by Leonardo. Optical coherence tomography revealed a set of micrometric engraved details in the blank background, which lack any trace of colored material, nor display any apparent relation to the drawn landscape. The disclosure of hidden technical features allowed us to offer new insights into Leonardo’s still under-investigated graphic practices.

Identification and genetic characterization of extra soft kernel texture in soft kernel durum wheat (Triticum turgidum ssp. durum)

AbstractBackground and objectivesKernel texture (grain hardness) is a key determinant of wheat quality and utilization. Among soft wheats, softer kernels possess some advantages in processing and end‐product quality. Here, an extra soft kernel phenotype was identified and characterized in soft kernel durum wheat.FindingsA RIL population was developed by crossing “Soft Strongfield” with Kamut Khorasan wheat. Progeny kernels were selected for chalky appearance in the F2, F3, and F4. Kernel texture phenotypes measured by the SKCS in the F5, F6, and F7 were all less than 20 and ranged down to −12.6. Among F5:7 full‐sib RILs, there was indication of a single segregating genetic locus that was associated with a difference of about 7 SKCS hardness index (HI) units. Single marker–trait association and composite interval mapping both identified significant QTLs on chromosomes 4B and 1B with phenotypic effects of about 5 and 4 HI, respectively.ConclusionsQTL detected in the present study may be used to reduce kernel texture in soft durum to below zero, creating “extra soft” durum. These QTLs should also be available to be used in hard kernel durum to reduce kernel texture and may be transferrable to hexaploid wheat.Significance and noveltyThe creation of soft kernel durum wheat expanded the processing, end‐product quality, and utilization of durum wheat. A further reduction in kernel softness may expand further durum wheat's importance as a global food.

Identification of Topological Spin Textures in Frustrated Fe3Sn2 Magnetic System

Identification of Topological Spin Textures in Frustrated Fe₃Sn₂ Magnetic System