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Tactile devices provide an intuitive way for texture display, which allows users to perceive the detailed surface properties of virtual or remote objects. For existing tactile devices that create actual topographical transformations, it remains challenging to render surface textures with high spatial resolution due to the limitation of the size of actuators. To address this challenge, a magnetically responsive soft actuator with a diameter of 1 mm is proposed. Integrating the high-density soft actuator array and a soft membrane bracket, we develop a magnetically responsive soft tactile device (named MRS-Tex) for texture display. When a magnetic field is applied, the magnetic units will sink to form pits, and the soft membrane bracket resists the finger pressure so that users can perceive the rendered surface texture. Thanks to the proposed sunken solution, MRS-Tex is capable of rendering surface textures with millimeter-level resolution while providing the sufficient upward holding force. Moreover, we conduct quantitative experiments and two user studies to evaluate the performance of MRS-Tex. Experimental results show that MRS-Tex can generate pits with a depth of more than 0.20 mm, which is sufficient for rendering perceivable surface textures.

In addition to 3D geometry, accurate representation of texture is important when digitizing real objects in virtual worlds. Based on a single consumer RGBD sensor, accurate texture representation for static objects can be realized by fusing multi-frame information; however, extending the process to dynamic objects, which typically have time-varying textures, is difficult. Thus, to address this problem, we propose a compact keyframe-based representation that decouples a dynamic texture into a basic static texture and a set of multiplicative changing maps. With this representation, the proposed method first aligns textures recorded from multiple keyframes with the reconstructed dynamic geometry of the object. Errors in the alignment and geometry are then compensated in an innovative iterative linear optimization framework. With the reconstructed texture, we then employ a scheme to synthesize the dynamic object from arbitrary viewpoints. By considering temporal and local pose similarities jointly, dynamic textures in all keyframes are fused to guarantee high-quality image generation. Experimental results demonstrate that the proposed method handles various dynamic objects, including faces, bodies, cloth, and toys. In addition, qualitative and quantitative comparisons demonstrate that the proposed method outperforms state-of-the-art solutions.

Detection of abnormal crowd behavior is one of the important tasks in real-time video surveillance systems for public safety in public places such as subway, shopping malls, sport complexes and various other public gatherings. Due to high density crowded scenes, the detection of crowd behavior becomes a tedious task. Hence, crowd behavior analysis becomes a hot topic of research and requires an approach with higher rate of detection. In this work, the focus is on the crowd management and present an end-to-end model for crowd behavior analysis. A feature extraction-based model using contrast, entropy, homogeneity, and uniformity features to determine the threshold on normal and abnormal activity has been proposed in this paper. The crowd behavior analysis is measured in terms of receiver operating characteristic curve (ROC) & area under curve (AUC) for UMN dataset for the proposed model and compared with other crowd analysis methods in literature to prove its worthiness. YouTube video sequences also used for anomaly detection.

Abundant rodingite was found within serpentinized peridotite and spatially close to gabbro and diabase dykes in the Xigaze ophiolite of the Yarlung Zangbo suture zone. Two types of rodingite are identified based on field- and textural relationships as well as on mineral- and bulk-rock compositions. Type I rodingite with porphyroblastic texture occurs along extensional fractures within massive serpentinized peridotite and contains two subtypes representing the early stage of rodingitization. Type II rodingite is associated with a foliated serpentinized host rock and contains three subtypes, characterized by prehnite-absent mineral assemblages with fine-grained porphyritic to aphanitic texture. All rodingite II subtypes experienced a higher degree of rodingitization than Type I. Type I rodingite was formed by metasomatism of CaO-rich fluids from serpentinized peridotite during ocean floor metamorphism at ~120–130 Ma. Thermodynamic modeling shows that Type I rodingite originated at low P-T conditions of

Modulating tactile texture perception for the surface of real objects is a promising way to artificially present various tactile textures. Here, we propose a simple method of modulating tactile textures for various materials, which is named the rotating-frame method. In the method, one touches an arbitrary material's surface through a hole in a cardboard frame. When the frame is rotated between the hand and material, the tactile texture of the material is perceived as if it has turned into another material. We investigated the qualitative and quantitative characteristics of the illusory modulation created by the method in a series of psychophysical experiments. We found that the method altered the tactile textures of the surfaces of touched materials such as glass and carpet to seem softer, smoother, slipperier, and warmer than they actually are. The illusory texture change occurred robustly when the method was applied with different categories of materials. Our method paves the way for the development of simple techniques for texture augmentation that can be applied to a wide range of materials and do not disrupt stable direct contact between the hand and the materials.

In this study statics-based textural analysis GLCM (Grey Level Co-occurrence Matrix) of high resolution L band SAR data from ALOS-2 of JAXA has been used to discriminate between lithological facies of the fold-thrust belt of Nagaland, India. Sandstones, shales, siltstones and clays have been separated in the analysis. Importantly, the lithological facies with second-order compositional variance have been distinguished from each other. The study of 27 lithological pairs indicates a Transformed Divergence index greater than 1.7 except one pair which shows an index of 1.6. The average GLCM texture values of different stratigraphic units in VV and VH polarization shows appreciable differences. Coefficient of variation of the texture samples from each descriptor show uniform distribution around their mean. The mean has thus been used as a lithological index in this study. Further, the medium scaled geological lineaments have been extracted and mapped at 1: 25,000 scale. The Compass Edge Detector on the first Principal Component of full polarized ALOS-2 scene has been used for this purpose. Fracture network, outcrop length, density, and orientation have also been analyzed. The study is successful in rock characterization within vegetation-covered landscape, using L band SAR remote sensing. It may provide an important tool in geological mapping in inaccessible terrains with limited ground data control. The fracture map and its study can provide some useful insights about reservoir characterization and reduce exploration risk in this terrain that has a proven petroleum system.

Postharvest water loss in pepper fruit (Capsicum annuum L.) reduces its shelf life. Fruit texture is one of the most important components of fruit quality for consumers. In this study, the anatomical traits of pepper fruit related to postharvest water loss and texture were assessed. There was a strong positive relationship between postharvest water loss and the thickness of the cuticular membrane, cuticular weight, total cutin weight, and polysaccharide-cutan weight. An amorphous fibrous structure that forms a path for diffusion and increases water loss was observed in the thick cuticle of the pericarp. In addition, positive correlations between the hardness of the exocarp and the weight of cuticular membrane, cutin content, and polysaccharide-cutan content were found. These results indicate that the thickness of the cuticular membrane wedged between subepidermal cells may influence water loss through the pericarp of pepper fruit and fruit with a high cutin and polysaccharide content have a hard tough texture.

Images obtained in dim light do not clearly represent the target scene, limiting information transmission on the image carrier. This study proposes a texture‐preserving image enhancement method, i.e. ETPR. The proposed method draws the illumination map of a low light image by Max‐RGB, and then applies the weighted median filter algorithm and Retinex to improve the illumination map further. Then, the enhanced image is changed from RGB mode to YCbCr mode and the texture of the luminance component Y is described. It is achieved by denoising the texture of the image to be enhanced, and then the final enhanced image is obtained by the method of sub‐region image fusion denoising. The effectiveness of ETPR is established by comparing it with existing enhancement technologies using public data and real images.

We present a sufficient condition for recovering unique texture and viewpoints from unknown orthographic projections of a flat texture process. We show that four observations are sufficient in general, and we characterize the ambiguous cases. The results are applicable to shape from texture and texture-based structure from motion.

Rutile from a wide range of orogenic gold deposits and districts, including representative world-class deposits, was investigated for its texture and trace element composition using scanning electron microscopy, electron probe microanalysis, and laser ablation-inductively coupled plasma-mass spectrometry. Deposits are hosted in various country rocks including felsic to ultramafic igneous rocks and sedimentary rocks, which were metamorphosed from lower greenschist to middle amphibolite facies and with ages of mineralization that range from Archean to Phanerozoic. Rutile presents a wide range of size, texture, and chemical zoning. Rutile is the dominant TiO2 polymorph in orogenic gold mineralization. Elemental plots and partial least square-discriminant analysis suggest that the composition of the country rocks exerts a strong control on concentrations of V, Nb, Ta, and Cr in rutile, whereas the metamorphic facies of the country rocks controls concentrations of V, Zr, Sc, U, rare earth elements, Y, Ca, Th, and Ba in rutile. The trace element composition of rutile in orogenic gold deposits can be distinguished from rutile in other deposit types and geologic settings. Elemental ratios Nb/V, Nb/Sb, and Sn/V differentiate the rutile trace element composition of orogenic gold deposits compared with those from other geologic settings and environments. A binary plot of Nb/V vs. W enables distinction of rutile in metamorphic-hydrothermal and hydrothermal deposits from rutile in magmatic-hydrothermal deposits and magmatic environments. The binary plot Nb/Sb vs. Sn/V distinguishes rutile in orogenic gold deposits from other geologic settings and environments. Results are used to establish geochemical criteria to constrain the source of rutile for indicator mineral surveys and potentially guide mineral exploration.

This paper proposes an effective infrared and visible image fusion method based on a texture conditional generative adversarial network (TC-GAN). The constructed TC-GAN generates a combined texture map for capturing gradient changes in image fusion. The generator in the TC-GAN is designed as a codec structure for extracting more details, and a squeeze-and-excitation module is applied to this codec structure to increase the weight of significant texture information in the combined texture map. The generator loss function is designed by combing the gradient loss and adversarial loss to retain the texture information of the source images. The discriminator brings the texture of the generated image closer to the visible image. To obtain significant texture information from the source images, a multiple decision map-based fusion strategy is proposed using a combined texture map and an adaptive guided filter. Extensive experiments on the public TNO and RoadScene datasets demonstrate that the proposed method is superior to other state-of-the-art algorithms in terms of a subjective evaluation and quantitative indicators.

To improve the detection for different fabric types and defect kinds, an approach based on K-Singular Value Decomposition (K-SVD) dictionary learning method is designed to detect fabric defects, wh...

Lung cancer is the most common cancer throughout the world and identification of malignant tumors at an early stage is needed for diagnosis and treatment of patient thus avoiding the progression to a later stage. In recent times, deep learning architectures such as CNN have shown promising results in effectively identifying malignant tumors in CT scans. In this paper, we combine the CNN features with texture features such as Haralick and Gray level run length matrix features to gather benefits of high level and spatial features extracted from the lung nodules to improve the accuracy of classification. These features are further classified using SVM classifier instead of softmax classifier in order to reduce the overfitting problem. Our model was validated on LUNA dataset and achieved an accuracy of 93.53%, sensitivity of 86.62%, the specificity of 96.55%, and positive predictive value of 94.02%.

The retrieval of images captured by different sensors or under different conditions is a desirable work. For this task, local image descriptor is an important tool. Nevertheless, most of local descriptors are proposed for similar images, which are usually captured by the same sensor in similar condition so they are same in radiation, or there are only linear radiation changes. In this paper, we propose a novel local feature descriptor, Histogram of Orientated Edge and Texture (HOET), for the retrieval of images with radiometric changes, especially for nonlinear radiation changes. In order to increase the similarity of the images captured from the same scene with nonlinear radiation changes, we combine edge and texture features to describe structural information of the images. First, we use Sobel operators in different directions to construct edge response maps and extract edge description in spatial domain. Second, we use log-Gabor filters to extract texture feature, then construct maximum index map (MIM) and extract texture description in frequency domain. Finally, HOET is developed by combining edge and texture description. We evaluate our descriptor in two heterogeneous image datasets and experiments show that HOET outperforms the state-of-the-art methods, including hand-designed and CNN-based descriptors.

Facial expression plays an important role in communicating emotions. In this paper, a robust method for recognizing facial expressions is proposed using the combination of appearance features. Traditionally, appearance features mainly divide any face image into regular matrices for the computation of facial expression recognition. However, in this paper, we have computed appearance features in specific regions by extracting facial components such as eyes, nose, mouth, and forehead, etc. The proposed approach mainly has five stages to detect facial expression viz. face detection and regions of interest extraction, feature extraction, pattern analysis using a local descriptor, the fusion of appearance features and finally classification using a Multiclass Support Vector Machine (MSVM). Results of the proposed method are compared with the earlier holistic representations for recognizing facial expressions, and it is found that the proposed method outperforms state-of-the-art methods.

Texture recognition remains a challenging visual task due to the complex appearance variations caused by scale changes in the real world. In most existing texture recognition methods, textures are represented at a single scale; thus, multi-scale texture information is not fully utilized, resulting in insufficient representation and inaccurate recognition. In this study, with the goal of addressing the challenge of scale changes, we propose a novel multi-scale boosting feature encoding network (MSBFEN) for accurate texture recognition. MSBFEN first extracts multi-scale features with multi-scale texture structure information under the guidance of texture priors using a novel prior-guided feature extraction (PFE) method. Then, a multi-scale texture encoding (MSTE) method is devised to capture discriminative multi-scale texture representations by encoding the extracted features. Finally, to fully utilize the multi-scale texture representations for accurate texture recognition, a novel multi-scale boosting learning (MSBL) method is proposed. In MSBL, the learning procedure for multi-scale texture recognition is boosted in a hierarchical, progressively reinforced manner, significantly addressing the challenge of scale changes and greatly enhancing the recognition accuracy. In addition, a novel outlier-aware texture encoding (OTE) method is proposed for robust texture encoding at each scale of MSTE. OTE can resist the influence of background interference and can further enhance the robustness of MSBFEN. In extensive experiments conducted on six challenging texture recognition datasets, namely, KTH-TIPS2b, FMD, DTD, MINC, GTOS and GTOS-mobile, MSBFEN achieves accuracies of 86.2%, 86.4%, 77.8%, 85.3%, 86.4% and 87.57%, respectively, representing state-of-the-art texture recognition performance.

The surface of cylinder liner is tribologically important. In most cases, it has a two-process cross-hatched structure created by the plateau honing process. Because the surface topography of cylinder liners is complicated, its analysis is challenging. Special methods have been used to analyse the textures. In this article, first, the parameters in the standard are reviewed. Next, the methods for selecting the reference elements are presented. Other comprehensive methods of cylinder surface analysis are described. Finally, methods of cylinder texture modelling are analysed.

Soil application of herbicides for pre-emergence (PRE) weed control is vital for grain sorghum production. Many soil-applied herbicides can be adsorbed or bound to the soil reducing the amount available for weed control. Extent of adsorption is strongly correlated to soil physical properties of organic matter (SOM) and texture. To overcome adsorption, the rate of herbicide to be applied is dependent on SOM and texture levels. These properties can vary within one field, making it difficult to follow label recommendations and achieve adequate weed control with a uniform rate. Variable rate applications (VRA) can be utilized to maximize herbicide effectiveness by applying the right rate in the right place. In 2016 and 2017, herbicide algorithms were developed for two different tank-mixes to be applied at five locations across Kansas. A Veris MSP3 system was utilized to collect and develop interpolated maps of SOM and of EC values that were correlated with soil texture classes to develop soil texture maps. Three algorithms were evaluated in the field for each tank-mix: based only on SOM (alg-SOM), on SOM and texture (alg-SOMtex), or on a flat rate. Rates for each tank-mix were based on the maximum usage rate (MUR) allowed for each herbicide. VRA based on SOM reduced the amount of herbicide applied while resulting in similar weed control compared to the flat rate. VRA based on SOM and texture greatly reduced the amount of herbicide applied but also resulted in reduced weed control compared to the flat rate at several locations. These findings prove that accurate SOM and texture data at high resolution combined with herbicide algorithms can be utilized by producers for effective VRA of soil-applied herbicides.

The Computer-aided diagnosis (CAD) tools are needed to address the human limitation in the assessment of the severity and classification of psoriasis. Dermatologists need these kinds of systems to ...

The 3D CAD shapes in current 3D benchmarks are mostly collected from online model repositories. Thus, they typically have insufficient geometric details and less informative textures, making them less attractive for comprehensive and subtle research in areas such as high-quality 3D mesh and texture recovery. This paper presents 3D Furniture shape with TextURE (3D-FUTURE): a richly-annotated and large-scale repository of 3D furniture shapes in the household scenario. At the time of this technical report, 3D-FUTURE contains 20,240 clean and realistic synthetic images of 5,000 different rooms. There are 9,992 unique detailed 3D instances of furniture with high-resolution textures. Experienced designers developed the room scenes, and the 3D CAD shapes in the scene are used for industrial production. Given the well-organized 3D-FUTURE, we provide baseline experiments on several widely studied tasks, such as joint 2D instance segmentation and 3D object pose estimation, image-based 3D shape retrieval, 3D object reconstruction from a single image, and texture recovery for 3D shapes, to facilitate related future researches on our database., Project Page: https://tianchi.aliyun.com/specials/promotion/alibaba-3d-future

Rock hardness and texture are two dominant factors controlling such engineering characteristics of rocks as drillability, blastability, and machinability. They are also directly responsible for tool wear and operational costs of the drilling operation. In this paper, an attempt has been made to develop some experimental equations for the prediction of these factors using vibration measurement while drilling in the laboratory scale. For this purpose, 10 different rock types were studied. The texture coefficient, Vickers hardness, and Schmidt hammer rebound number were tested and assigned to each rock type. After that, a set of laboratory drilling tests were carried out on each sample using a new test rig developed for this purpose. As a standard method for vibration analysis, the “root mean square (RMS)” value was applied to present the vibration level in each rock type. The regression analysis revealed that, totally, the RMS values of vibration did have linear correlations with the texture coefficient, Vickers hardness, and Schmidt hammer rebound number. The most reliable correlation was found between vibration and rock texture, while the weakest one was between vibration while drilling and Schmidt hammer rebound number. Therefore, vibration while drilling can be measured based on the texture coefficient.

Aims: Here in this experiment, the investigation was done for the relationship among the various soil health parameters i.e., soil organic carbon (SOC), soil texture, and wet aggregate stability (WAS). Place and Duration of Study: Sample: Collection of soil samples were done from 0-15 cm depth from East Champaran is situated in Bihar and is located at 26038’N and 84054’E in the year 2019-2020. Methodology: Soil texture: 14g (+/- 0.1g) of sieved soil was added to a 50 ml centrifuge tube holding 42 ml of a dispersant 3% sodium hexametaphosphate solution follwed by 2 hr shaking and 0.053 mm sieved. Water stable aggregates: Each 0.25-mm sieve contained 4g of air-dried, 2-mm aggregate soil. Each sample's precise weight was recorded. The soil samples were dispersed for 3 minutes with 100 mL distilled water and then for 10 minutes with a 2 g/L sodium hexametaphosphate solution. Pre-weighed filter sheets were used to filter both solutions. Each filter paper was weighed after being oven-dried at 105°C. Soil organic carbon: The amount of soil organic carbon (SOC) was calculated using the Walkley and Black technique (1934). Results: Wet aggregate stability and soil organic carbon storage were shown to have a strong positive connection. Soil carbon stock in soils of East Champaran varied between 5.27-19.60 Mg ha-1 with an average of 12.98 Mg ha-1. WAS ranged from 3.82 to 36.43% with a mean of 16.11%. The results revealed that WAS increased with increase in SOC stock. This experiment also revealed that clay (%) and silt (%) directly affect WAS and hence enhance SOC storage. Conclusion: So, it can be concluded that WAS and soil texture directly and positively impact SOC storage in soils of East Champaran, Bihar.

This article examines the costumes of Claire Denis’s Chocolat. A highly aestheticized drama of pressed khaki uniforms and pith helmets in colonial Cameroon, clothing is, as this article argues, enfolded into a far broader topography of material power; it is an apparatus through which racialized and gendered difference is actively ‘fashioned’ for the screen. In Chocolat, it becomes clear that fashion and celluloid are intimately intertwined (‘sutured’ together, through Denis’s editing), with the bio-political containment of white femininity, historically underwritten by French society’s anxieties concerning racial miscegenation and sexual excesses in the colonies. Concurrently, however, dress also grants expression to transgressive currents of desire, which, in Denis’s provocative portrait of interracial attraction, intersect vividly with the sexual politics of fetishism. In the very fabrics of its material content – costumes, the pliable ‘skin’ of its images and the tissue of human relationships – Chocolat portrays colonial identity as deeply conflicted, and Denis affirms the material world as an agile and highly transgressive force in the theatre of colonial power.

Aero‐engine blades crack detection is one of the important tasks in daily ground maintenance, crack is a kind of texture feature, due to the random distribution, irregular shape and vague characteristics, which is still a challenging task to realize automatic detection in working environment. A detection model based on the Yolov4‐tiny is proposed that is universal and focuses more on the characteristics of cracks, and it is implemented in embedded device. First, in order to distinguish the cracks and noises, an improved attention module is introduced into the backbone of Yolov4‐tiny to enhance the model's capability to focus on crack areas; second, in order to improve the effect of multi‐scale feature fusion, the bicubic interpolation is implemented in upsampling module; finally, in order to solve the redundant detection results of bounding‐boxes in crack areas, the optimized non‐maximum suppression method is proposed to make the detection results better corresponding to the groundTruth. The robustness of proposed detection model was demonstrated by evaluating varying lighting and noise images. The average precision on integrated datasets is 81.6%, which outperforms the original Yolov4‐tiny by an increase of 12.3%.

Spurred on by Colson Whitehead’s Pulitzer Prize-winning novel The Nickel Boys (2019), which is set in Tallahassee, FL, during the 1950s and 1960s, this essay presents a close-up look at James Baldwin’s visit to Tallahassee in May 1960. Moving between Baldwin’s writings about the South, especially “They Can’t Turn Back,” published by Mademoiselle magazine in August 1960, and subsequent writing about the movement in Tallahassee, and checking off against Whitehead’s fictional treatment, we find a lattice of silences obscuring the names and contributions of Black women. Most importantly, we find that the historic case of the rape of Betty Jean Owens in May 1959, and the subsequent trial that summer, appears neither in Baldwin’s nor Whitehead’s writing about Tallahassee at the time. This essay establishes the missing names of Black women in the places marked and unmarked by Baldwin in his work at the time, and puts the case of Betty Jean Owens on the historical map where it belongs. In so doing, we figure issues of race, gender, sex, and violence for the ways they twist together, ways suppressed in historical (and even some contemporary) writing, ways crucial to our deepening consideration of Baldwin’s work and the history which he drew upon and to which he contributed so profoundly.

Due to increase in energy catastrophe and accompanying environmental issues, the search for renewable and alternative energy resources is a fiercely disputed problem in the scientific world. Biodiesel has proven to be the finest sustainable alternative to gasoline. Because of their environment-friendly character and cost-effectiveness, nonedible oil-bearing seeds could be the possible resource for biodiesel production. Therefore, the present study uses scanning electron microscopy (SEM) to investigate and identify micromorphological features of six novel, nonedible oil-bearing seeds of Verbenaceae as potential biodiesel feed stocks. Selected seeds of Verbenaceae range in size from 1 to 5 mm in width and 1.8 to 6.4 mm in length, according to light microscopic studies. The oil content of seeds ranges from14 to 19%. Seed texture, color, shape, size, periclinal and anticlinal wall patterns, and cell outline are all more variable in SEM-mediated seed ultrastructure analyses. The findings of this work imply that SEM could be a useful method for revealing hidden micromorphological features in a variety of nonedible oil-producing seeds, which could aid to future investigation, proper identification, seed categorization, and authenticity. These seeds were hard to be identified by the native people due to their resemblance in seed color and shape, so there is a need to give a detailed description of seed characters for proper identification of these plants. This work will determine the new features of the seeds, which will be helpful in near future.

The primary goal in this paper is to automate radiological measurements of Vertebral Body (VB) in Magnetic Resonance Imaging (MRI) spinal scans. It starts by preprocessing the images, then detect and localize the VB regions, next segment and label VBs and finally classify each VB into three cases as being normal or fractured in case 1, benign or malignant in case 2 and normal, benign or malignant in case 3. The task is accomplished by extracting and combining distinct features of VB such as boundary, gray levels, shape and texture features using various Machine Learning techniques. The class balance deficit dataset towards normal and fractures is balanced by data augmentation which provides an enriched dataset for the learning system to perform precise differentiation between classes. On a clinical spine dataset, the method is tested and validated on 535 VBs for segmentation attaining an average accuracy 94.59% and on 315 VBs for classification with an average accuracy of 96.07% for case 1, 93.23% for case 2 and 92.3% for case 3.

A wide range of heterogeneous geological units composed of strong rock blocks enclosed in a bonded matrix of fine texture exists worldwide. Such geomaterials belong to geotechnically complex formations and are often referred to as bimrocks (block-in-matrix rocks) or bimsoils (block-in-matrix soils), as a function of their matrix characteristics and the interface strength between the matrix and blocks. Stability problems occurring in such complex geomaterials have been analysed almost exclusively by means of deterministic approaches and with the aim of investigating the effects of variable block contents on their mechanical behaviour. However, bimrocks and bimsoils can present very different internal block-in-matrix arrangements and properties according to their forming process and, consequently, significantly dissimilar mechanical behaviours. Therefore, the aim of this paper was to statistically investigate and compare the stability of theoretical slopes in the most widespread bimrock formations, i.e. sedimentary and tectonic melanges. These formations are characterised by substantial differences in their rock inclusion geometry. To this aim, a great number of 2D slope models were generated to enclose blocks with variable shapes, dimensions, arrangements, inclinations and contents. To obtain statistically based results, fifteen configurations were analysed for each block content and geometrical configuration considered. The results obtained indicate that block shapes and orientations significantly affect the stability of slopes in bimrocks only when the block contents are greater than 40%. Moreover, it is demonstrated that blocks inclined 0° to the horizontal provide the most tortuous and irregular failure surfaces and, consequently, the highest safety factors.

The aim of the present study was to reveal the textural profile changes like hardness, cohesiveness, springiness, and adhesion supported by total mesophilic aerobic bacteria count (TMABc), pH, and some visual sensory characteristics in squid samples stored at 4 ºC. Three different Brookfield Texture Analyzer tools, named TA7, TA9, and TA18 were used to observe the textural changes more clearly. The difference of TMABc between the storage days reached from 4.32 log CFU/g to 6.32 log CFU/g. The hardness value of the squid samples, detected by TA18 and TA9 tools, increased while the hardness value obtained from TA7 was higher. The highest change in cohesiveness value in the squid samples was defined by the TA9 tool as ~63%. Once springiness values detected by TA9 were sharply decreased from 4.9 mm to 2.1 mm, those of TA7 and TA18 were slowly decreased. The most increase in adhesion value (0.08 mJ to 0.21 mJ) was obtained in the TA7 tool. Depending on the textural quality changes, the pH value was increased, and as visual and sensory, dark, or yellow spots were observed. The present study results revealed that especially the TA18 tool could be effectively used to determine the quality changes of the squid samples.

The article presents experimental data on the study of the processes of oxidative spoilage of fat in sterilized canned meat when stored under temperature conditions of +37 °C. The intensity of oxidative processes is estimated indirectly by the accumulation of primary and secondary products of fat oxidation. The dynamics of such physical and chemical indicators as thiobarbituric acid, acid, peroxide numbers, active and titratable acidity is considered.

Rock salt caverns have been extensively used as reliable repositories for hazardous waste such as nuclear waste, oil or compressed gases. Undisturbed rock salt deposits in nature are usually impermeable and have very low porosity. However, rock salt formations under excavation stresses can develop crack networks, which increase their porosities; and in the case of a connected crack network within the media, rock salt may become permeable. Although the relationship between the permeability of rock salt and the applied stresses has been reported in the literature, a microscopic study that investigates the properties influencing this relationship, such as the evolution of texture and internal stresses, has yet to be conducted. This study employs in situ 3D synchrotron micro-computed tomography and 3D X-ray diffraction (3DXRD) on two small-scale polycrystalline rock salt specimens to investigate the evolution of the texture and internal stresses within the specimens. The 3DXRD technique measures the 3D crystal structure and lattice strains within rock salt grains. The specimens were prepared under 1D compression conditions and have shown an initial {111} preferred texture, a dominant {110}〈110〉 slip system and no fully connected crack network. The {111} preferred texture under the unconfined compression experiment became stronger, while the {111}〈110〉 slip system became more prominent. The specimens did not have a fully connected crack network until applied axial stresses reached about 30 MPa, at a point where the impermeability of the material becomes compromised due to the development of multiple major cracks.

Geologists infer many issues associated with the formation of the Earth, depositional history and weathering processes based on rock assessment. Preparation of the thin sections of rocks and investigating these sections is one of the common methods in studying rocks. Since all further studies on the sections require the segmentation of existing minerals in thin sections, the accurate and automatic identification of minerals is very important. In this research, for the intelligent identification of minerals, digital images were prepared in ordinary and then polarized light of the minerals. The minerals were then classified by color features, and texture using support vector machine classification techniques. The result of applying the proposed algorithm on 75 thin sections consisting of 42 minerals show an accuracy of 99.25% in minerals segmentation using the polynomial-based support vector machine classification. Also, our proposed algorithm shows the best performance with other techniques by fivefold and tenfold cross-validation. The results indicate that the proposed method outperforms the state-of-the-art method in mineral thin section identification. In addition to the high accuracy, the proper speed of this method also shows the significant efficiency of the proposed method for the automatic identification of minerals.

Bromelain enzyme is a protease enzyme that is abundantly found in pineapples. Pineapple crown is a part of pineapple that contains bromelain enzyme which its content remains unknown. This study was aimed to discover the effect of drying on the moisture content, yield, and characteristics of the enzyme of cayenne pineapple crown, namely protein content, activity units and specific activities. This study also aimed to discover its effect on the beef texture with a difference in duration in the immersion process. The method used was different drying temperatures of 55oC, 50oC, 45oC, 40oC, 35oC, and 30oC which was analysed descriptively and the difference of immersion duration in beef using the Completely Randomized Design (CRD) method with four treatments, namely immersion duration of 0-hour, 2-hrs, 4-hrs, 6-hrs with 5 repetitions. The results obtained was the optimal drying temperature was 55oC due to its best moisture content, protein content, and best enzyme characteristics and it was known that there was a significant effect (p

The popularity of fat-free fermented concentrated milk products, such as fresh cheeses and high-protein yogurt, has increased over the recent years, attributed to greater availability and improvements in taste and texture. These improvements have been achieved through modifications and new developments in processing technologies, for example, higher heat treatment intensities and incorporating different membrane filtration technologies. Though numerous processing parameters are discussed in the literature, as well as reasons behind the developments, detailed examinations of how process modifications affect the final textural attributes of these products are lacking. To draw links between processing parameters and texture, we review the literature on fat-free fermented concentrated milk products from the perspective of fermented milk protein-based microgel particles as the basic structural unit. At each main processing step, relationships between process parameters, micro- and macrostructural and sensory (textural) properties are discussed.An overview of particle characteristics that drive structural changes at each processing step is developed in relation to textural characteristics. Using this approach of assessing relationships between structural characteristics of concentrated dispersions of fat-free fermented milk protein-based microgel particles and processing parameters provides a basic context for the selection of optimal parameters to achieve a desired texture.

Skeletal metastasis can be visualised as changes in bone texture. This investigation aimed to develop an automated method to segment spinal osteoblastic metastases in μCT images using radiomic-base...

The production of plant-based fish alternatives that look, feel, taste, and cook like real fish is critical for widespread consumer acceptance. Consequently, a thorough understanding of the properties of real fish is critical for the successful design of high-performance plant-based fish analogs. In this study, we investigated changes in the optical, mechanical, and water holding properties of Atlantic salmon during simulated cooking at different temperatures. Differential scanning calorimetry and diffusive wave spectroscopy exhibited characteristic peaks around 40 °C (myosin and collagen) and 72 °C (actin), which caused appreciable changes in the salmons’ physicochemical properties. The textural properties of salmon during simulated cooking were characterized using a dynamic shear rheometer and texture analyzer. Changes in color, cook loss, and surface area were related to the cooking temperature. This study provides valuable insights into the relationship between protein denaturation, protein aggregation, microstructural changes, and texture of real fish. This information will be useful for creating higher quality plant-based fish analogs.

High‐dynamic‐range image (HDRI) displays scenes as vivid as the real scenes. HDRI can be reconstructed by fusing a set of bracketed‐exposure low‐dynamic‐range images (LDRI). For the reconstruction, many works succeed in removing the ghost artefacts caused by moving objects. The critical issue is reconstructing the areas which are saturated due to bad exposure and occluded due to motion with no ghost artefacts. To overcome this issue, this paper proposes texture and exposure awareness based refill. The proposed work first locates the saturated and occluded areas existing in input image set, then refills background textures or patches containing rough exposure and colour information into located areas. Proposed work can be integrated with multiple existing ghost removal works to improve the reconstruction result. Experimental results show that proposed work removes the ghost artefacts caused by saturated and occluded areas in subjective evaluation. For the objective evaluation, the proposed work improves the HDR‐VDP‐2 evaluation result for multiple conventional works by 1.33% on average.

Commercial forest species discrimination is valuable for optimal management of commercial forests. Therefore, second-order image texture combinations computed from a 0.5 m WorldView-2 pan-sharpened...

The disease in tomato plants, especially on tomato leaves will have an impact on the quality and quantity of tomatoes produced. Handling disease on tomato leaves that must be done is to detect the type of disease as early as possible, then determine the treatment that must be done. Detection of its types of tomato plant diseases requires sufficient knowledge and experience. The problem is that many beginner farmers in growing tomatoes do not have much knowledge, so they have failed in growing tomatoes. Based on these cases, this study proposes a model for the early detection of disease in tomato leaves based on image processing. The research method used is divided into 5 stages, namely preprocessing, segmentation, feature extraction, classification, and performance evaluation. The feature extraction stage used is texture-based with Gabor filters and color-based filters. The final decision is determined by the Support Vector Machine (SVM) classification algorithm with the Radial Basis Function (RBF) kernel. The test results of the tomato leaf disease detection system produced an average performance parameter of 98.83% specificity, 90.37% sensitivity, 90.34% F1-score, 90.37% accuracy, and 94.60% area under the curve (AUC). Referring to the resulting of the AUC performance, the tomato leaf disease detection system is in the very good category.

Soil mantles in the Western Ghat regions of South India are so thick that their properties cannot be investigated with the common soil exploration programs. The present work has been accomplished using data from boreholes dug for the purposes of a hydro-power project and from the samples collected at deep road cuttings in an upland area in Karnataka. While the data from boreholes have furnished information on soil depth and permeability, field studies have made known soil texture and density. Ring infiltration tests have been carried out to cross-check the coefficient of permeability values measured in the laboratory. It has been inferred that the soil depth generally varies from a couple of meters near the stream sides to about 20 m near hill tops and to over 50 m sometimes in the mid-slope region. No other factor seems to influence the soil thickness. Texture is found to vary between Silty sand and Sandy silt. Silt being the predominant component and clay content being low, the soil mantles are found to be highly stable. Measured values of permeability being very low compared to the measured infiltration rates, even on the forest floors, it is concluded that the size of the core samples is insufficient to capture the preferential flow paths that characterize this land.

Image Fusion is a process of adding information obtained from various sensors and intelligent systems. This provides an image which containing complete information. In this process we fuse images of the same scene one is Infrared Image and other is visible image to produce an image that contains more information. In this the Infrared which are low resolution and noisy nature of image and visual image fused using Stationary wavelet transfer algorithm. In this we have used Gabor filter and GLCM to extract feature and compared the two feature extraction method using quality matrix parameters and found which method is the best method of fusion. The fusion is used in satellite image fusion which mostly used in maps also for decision-making process involved in interpreting images from multi sensor data. Images of several different targets (a military vehicle, a wood chipper, a pickup truck, and people) were used to assess how human subjects view and interpret different types of images. The premise is that combining complementing data from several sensors will result in more accurate findings for data processing difficulties. Although significant progress has been achieved in this sector, complete modeling of the human brain remains a distant objective.