Kinds Of Textures Research Articles

An Evaluation of the Productivity of Alagba Series for Upland Crops Production in the Humid Agro Ecological Zone Of Nigeria

Studies were carried out on Alagba series in the humid agro ecolocological zone of Nigeria to evaluate their productivity on the basis of their physicochemical properties. Soil constraints inherent in the soils which negatively impact on their productivity were scientifically identified. Thereafter, appropriate measures to ameliorate the constraints were proffered in order to enhance the productivity of the soils. Among the constraints identified were soil acidification, low effective cation exchange capacity and susceptibility to erosion due to the clayey texture throughout the profile. This kind of texture not only made the soils less productive for deep rooted crops but also caused difficulties in harvesting. Liming and the use of acid-tolerant species were appropriate measures to ameliorate the acidity constraints, while incorporation of organic materials into the soils in combination with liming were the appropriate measures to enhance the effective cation exchange capacity. Erosion control and maintenance of surface cover were to be given high priority, while tillage should be limited to only when soil was drier than the plastic limit. Soil organic carbon should be maintained or increased due to its role in many important soil properties that have a bearing on soil productivity.Keywords: Productivity, inherent soil constraints, Alagba Series, humid agroecological zone, Nigeria

Adaptive example-based super-resolution using kernel PCA with a novel classification approach

An adaptive example-based super-resolution (SR) using kernel principal component analysis (PCA) with a novel classification approach is presented in this paper. In order to enable estimation of missing high-frequency components for each kind of texture in target low-resolution (LR) images, the proposed method performs clustering of high-resolution (HR) patches clipped from training HR images in advance. Based on two nonlinear eigenspaces, respectively, generated from HR patches and their corresponding low-frequency components in each cluster, an inverse map, which can estimate missing high-frequency components from only the known low-frequency components, is derived. Furthermore, by monitoring errors caused in the above estimation process, the proposed method enables adaptive selection of the optimal cluster for each target local patch, and this corresponds to the novel classification approach in our method. Then, by combining the above two approaches, the proposed method can adaptively estimate the missing high-frequency components, and successful reconstruction of the HR image is realized.

The role of surface texture on friction and transfer layer formation during repeated sliding of Al–4Mg against steel

In the present investigation, various kinds of textures, namely, unidirectional, 8-ground, and random were attained on the die surfaces. Roughness of the textures was varied using different grits of emery papers or polishing powders. Then pins made of Al-4Mg alloys were slid against steel plates at various numbers of cycles, namely, 1, 3, 5, 10 and 20 using pin-on-plate reciprocating sliding tester. Tests were conducted at a sliding velocity of 2 minis in ambient conditions under both dry and lubricated conditions. A constant normal load of 35 N was applied in the tests. The morphologies of the worn surfaces of the pins and the formation of transfer layer on the counter surfaces were observed using a scanning electron microscope. Surface roughness parameters of the plates were measured using an optical profilometer. In the experiments, it was observed that the coefficient of friction and formation of the transfer layer depend on the die surface textures under both dry and lubricated conditions. More specifically, the coefficient of friction decreases for unidirectional and 8-ground surfaces while for random surfaces it increases with number of cycles. However, the coefficient of friction is highest for the sliding perpendicular to the unidirectional textures and least for the random textures under both dry and lubricated conditions. The difference in friction values between these two surfaces decreases with increasing number of cycles. The variation in the coefficient of friction under both dry and lubrication conditions is attributed to the change in texture of the surfaces during sliding. (C) 2011 Elsevier B.V. All rights reserved.

Evaluation of heavy metals pollution of Nokoue Lake

Toxic metals (cadmium, lead, copper and arsenic) assessment was carried out in water and sediments samples of Nokoue Lake. Except for water, a group of sediment samples was mineralized before the analysis with atomic absorption spectrophotometer. The second group of sediment samples was processed for texture analysis. The results revealed that for the physico-chemical characteristics, the pH is from 6.5 to 8.5. Four kinds of textures were identified: mud, sand, sandy mud and muddy sand. Heavy metals mean concentrations in water rose from 0.01 to 10.1 mg/L for lead and 5.84 to 8.47 for arsenic. In water, copper showed low concentrations in all sites and the values of cadmium were below the detectable limits. The mean concentrations in the sediments were: Cd (0.56 to 22.07 mg/L); Pb (3.2 to 13.94 mg/L); Cu (11.51 to 54.39 mg/L) and As (ND to 436.81 mg/L). Besides, the pattern of trace elements accumulation according to textures revealed that sandy mud and sand sediments recorded high concentrations of arsenic, mud registered high concentrations of cadmium and the sediment combining sand and mud registered high concentrations of copper. Besides, the correlation between heavy metal concentrations and pH except for arsenic. Key words: Nokoue Lake, pollution, heavy metal, texture.

Evaluation of heavy metals pollution of Nokoue Lake

Toxic metals (cadmium, lead, copper and arsenic) assessment was carried out in water and sediments samples of Nokoue Lake. Except for water, a group of sediment samples was mineralized before the analysis with atomic absorption spectrophotometer. The second group of sediment samples was processed for texture analysis. The results revealed that for the physico-chemical characteristics, the pH is from 6.5 to 8.5. Four kinds of textures were identified: mud, sand, sandy mud and muddy sand. Heavy metals mean concentrations in water rose from 0.01 to 10.1 mg/L for lead and 5.84 to 8.47 for arsenic. In water, copper showed low concentrations in all sites and the values of cadmium were below the detectable limits. The mean concentrations in the sediments were: Cd (0.56 to 22.07 mg/L); Pb (3.2 to 13.94 mg/L); Cu (11.51 to 54.39 mg/L) and As (ND to 436.81 mg/L). Besides, the pattern of trace elements accumulation according to textures revealed that sandy mud and sand sediments recorded high concentrations of arsenic, mud registered high concentrations of cadmium and the sediment combining sand and mud registered high concentrations of copper. Besides, the correlation between heavy metal concentrations and pH except for arsenic. Key words: Nokoue Lake, pollution, heavy metal, texture.

Missing Texture Reconstruction Method Based on Perceptually Optimized Algorithm

This paper presents a simple and effective missing texture reconstruction method based on a perceptually optimized algorithm. The proposed method utilizes the structural similarity (SSIM) index as a new visual quality measure for reconstructing missing areas. Furthermore, in order to adaptively reconstruct target images containing several kinds of textures, the following two novel approaches are introduced into the SSIM-based reconstruction algorithm. First, the proposed method performs SSIM-based selection of the optimal known local textures to adaptively obtain subspaces for reconstructing missing textures. Secondly, missing texture reconstruction that maximizes the SSIM index in the known neighboring areas is performed. In this approach, the nonconvex maximization problem is reformulated as a quasi convex problem, and adaptive reconstruction of the missing textures based on the perceptually optimized algorithm becomes feasible. Experimental results show impressive improvements of the proposed method over previously reported reconstruction methods.

Missing Intensity Interpolation Using a Kernel PCA-Based POCS Algorithm and its Applications

A missing intensity interpolation method using a kernel principal component analysis (PCA)-based projection onto convex sets (POCS) algorithm and its applications are presented in this paper. In order to interpolate missing intensities within a target image, the proposed method reconstructs local textures containing the missing pixels by using the POCS algorithm. In this reconstruction process, a nonlinear eigenspace is constructed from each kind of texture, and the optimal subspace for the target local texture is introduced into the constraint of the POCS algorithm. In the proposed method, the optimal subspace can be selected by monitoring errors converged in the reconstruction process. This approach provides a solution to the problem in conventional methods of not being able to effectively perform adaptive reconstruction of the target textures due to missing intensities, and successful interpolation of the missing intensities by the proposed method can be realized. Furthermore, since our method can restore any images including arbitrary-shaped missing areas, its potential in two image reconstruction tasks, image enlargement and missing area restoration, is also shown in this paper.

ChemInform Abstract: Ceramics and Nanostructures from Molecular Precursors

The elaboration of solids from the molecular scale by a kinetically controlled methodology is one of the main challenges of molecular chemistry. In the long term, this should permit the design of solids with desired properties. Here, some examples are given which show a few methods that have been used for the preparation of solids from molecular precursors. The one-pot synthesis of rheologically controlled SiC is described. Access to a new kind of ceramic is obtained by the same methodology using molecular precursors. Mixed ceramics with interpenetrating networks are not accessible by the chemical thermodynamic route. The chemistry of hybrid materials obtained from molecular precursors through inorganic polymerization is presented. This class of materials offers wide perspectives because of 1) the large possibilities opened by the organic unit, 2) the kinetic control, which permits any kind of texture for the solid, and 3) the aptitude of these solids to become nanostructured.

Linear Regression Model on Multiresolution Analysis for Texture Classification

Texture is a surface property which is used to identify and recognize the object. Texture analysis is important in many applications of computer image analysis for classification and segmentation of images based on local spatial patterns of intensity or color. In texture classification the goal is to assign an unknown sample image to one set of known texture classes. The proposed method is texture analysis and classification with linear regression model based on directional lifting based wavelet transform. In this method, texture classification is performed by analyzing the spatial correlation between some sample texture images belonging to the same kind of texture at different frequency regions, obtained by 2-D wavelet transform. The linear regression model is employed to analyze this correlation and extract texture features that characterize the samples. Therefore, this method not only considers the frequency regions but also the correlation between the frequency regions. So the classification rate is improved.

Unsupervised detection and localization of structural textures using projection profiles

The main goal of existing approaches for structural texture analysis has been the identification of repeating texture primitives and their placement patterns in images containing a single type of texture. We describe a novel unsupervised method for simultaneous detection and localization of multiple structural texture areas along with estimates of their orientations and scales in real images. First, multi-scale isotropic filters are used to enhance the potential texton locations. Then, regularity of the textons is quantified in terms of the periodicity of projection profiles of filter responses within sliding windows at multiple orientations. Next, a regularity index is computed for each pixel as the maximum regularity score together with its orientation and scale. Finally, thresholding of this regularity index produces accurate localization of structural textures in images containing different kinds of textures as well as non-textured areas. Experiments using three different data sets show the effectiveness of the proposed method in complex scenes.

Underwater Sustainability of the “Cassie” State of Wetting

A rough hydrophobic surface when immersed in water can result in a "Cassie" state of wetting in which the water is in contact with both the solid surface and the entrapped air. The sustainability of the entrapped air on such surfaces is important for underwater applications such as reduction of flow resistance in microchannels and drag reduction of submerged bodies such as hydrofoils. We utilize an optical technique based on total internal reflection of light at the water-air interface to quantify the spatial distribution of trapped air on such a surface and its variation with immersion time. With this technique, we evaluate the sustainability of the Cassie state on hydrophobic surfaces with four different kinds of textures. The textures studied are regular arrays of pillars, ridges, and holes that were created in silicon by a wet etching technique, and also a texture of random craters that was obtained through electrodischarge machining of aluminum. These surfaces were rendered hydrophobic with a self-assembled layer of fluorooctyl trichlorosilane. Depending on the texture, the size and shape of the trapped air pockets were found to vary. However, irrespective of the texture, both the size and the number of air pockets were found to decrease with time gradually and eventually disappear, suggesting that the sustainability of the "Cassie" state is finite for all the microstructures studied. This is possibly due to diffusion of air from the trapped air pockets into the water. The time scale for disappearance of air pockets was found to depend on the kind of microstructure and the hydrostatic pressure at the water-air interface. For the surface with a regular array of pillars, the air pockets were found to be in the form of a thin layer perched on top of the pillars with a large lateral extent compared to the spacing between pillars. For other surfaces studied, the air pockets are smaller and are of the same order as the characteristic length scale of the texture. Measurements for the surface with holes indicate that the time for air-pocket disappearance reduces as the hydrostatic pressure is increased.

Effect of the cooling speed through Tc on the formation of textures in superfluid 3He-A

The cw-NMR measurements have been performed for superfluid 3He A-phase in a single cylinder with 115 μm inner radius. We observed two peaks in NMR spectra, the main peak and the satellite peak. The satellite peak is attributed to textures which are induced by the flow due to temperature gradient by controlling the cooling speed of samples from normal fluid state to superfluid A-phase. We have observed that the satellite peak height of NMR spectrum increases continuously as the cooling speed increases. The number of possible texture types is restricted for our cylindrical cell, since the radius of the cell is about ten times larger than the dipole coherence length ξd ∼ 10 μm. Therefore, it is unlikely that the continuous change of NMR spectrum is attributed to the different kinds of textures. We discuss that these results are due to textural domain structure along cylindrical axis.

Adaptive Missing Texture Reconstruction Method Based on Kernel Canonical Correlation Analysis with a New Clustering Scheme

In this paper, a method for adaptive reconstruction of missing textures based on kernel canonical correlation analysis (CCA) with a new clustering scheme is presented. The proposed method estimates the correlation between two areas, which respectively correspond to a missing area and its neighboring area, from known parts within the target image and realizes reconstruction of the missing texture. In order to obtain this correlation, the kernel CCA is applied to each cluster containing the same kind of textures, and the optimal result is selected for the target missing area. Specifically, a new approach monitoring errors caused in the above kernel CCA-based reconstruction process enables selection of the optimal result. This approach provides a solution to the problem in traditional methods of not being able to perform adaptive reconstruction of the target textures due to missing intensities. Consequently, all of the missing textures are successfully estimated by the optimal cluster's correlation, which provides accurate reconstruction of the same kinds of textures. In addition, the proposed method can obtain the correlation more accurately than our previous works, and more successful reconstruction performance can be expected. Experimental results show impressive improvement of the proposed reconstruction technique over previously reported reconstruction techniques.

Is the CMB cold spot a gate to extra dimensions?

One of the most striking features found in the cosmic microwave background data is thepresence of an anomalous cold spot (CS) in the temperature maps made by the WilkinsonMicrowave Anisotropy Probe (WMAP). This CS has been interpreted as the result of thepresence of a collapsing texture, perhaps coming from some early universe grand unifiedtheory (GUT) phase transition. In this work we propose an alternative explanation basedon a completely different kind of texture which appears in a natural way in a broad class ofbrane-world models. This type of texture, known as a brane-skyrmion, can beunderstood as a hole in the brane which makes it possible to pass through themalong the extra-dimensional space. The typical scales needed for the proposedbrane-skyrmions to correctly describe the observed CS can be as low as the electroweakscale.

Periodic Pattern Formation and Its Applications in Cellular Neural Networks

In this paper, first, we investigate the CNN templates producing periodic patterns. Next, two image processing algorithms using periodic pattern formation in cellular neural networks are proposed. By applying one to a certain image, area having spatial uniformity is removed from the original image and only parts without spatial uniformity remain. Simulation results show that when this algorithm is applied to an image containing characters (texts) or a human face image, only characters or eyes and mouth are extracted. Also, by applying the other to texture image, periodicity is enhanced in area having spatial periodicity, and texture can be segmented. Simulation results show that when this algorithm is applied to image that consists of four kinds of textures, each texture area can be segmented.

Texture Analysis and Classification With Linear Regression Model Based on Wavelet Transform

The wavelet transform as an important multiresolution analysis tool has already been commonly applied to texture analysis and classification. Nevertheless, it ignores the structural information while capturing the spectral information of the texture image at different scales. In this paper, we propose a texture analysis and classification approach with the linear regression model based on the wavelet transform. This method is motivated by the observation that there exists a distinctive correlation between the sample images, belonging to the same kind of texture, at different frequency regions obtained by 2-D wavelet packet transform. Experimentally, it was observed that this correlation varies from texture to texture. The linear regression model is employed to analyze this correlation and extract texture features that characterize the samples. Therefore, our method considers not only the frequency regions but also the correlation between these regions. In contrast, the pyramid-structured wavelet transform (PSWT) and the tree-structured wavelet transform (TSWT) do not consider the correlation between different frequency regions. Experiments show that our method significantly improves the texture classification rate in comparison with the multiresolution methods, including PSWT, TSWT, the Gabor transform, and some recently proposed methods derived from these.

POCS-Based Texture Reconstruction Method Using Clustering Scheme by Kernel PCA

A new framework for reconstruction of missing textures in digital images is introduced in this paper. The framework is based on a projection onto convex sets (POCS) algorithm including a novel constraint. In the proposed method, a nonlinear eigenspace of each cluster obtained by classification of known textures within the target image is applied to the constraint. The main advantage of this approach is that the eigenspace can approximate the textures classified into the same cluster in the least-squares sense. Furthermore, by monitoring the errors converged by the POCS algorithm, a selection of the optimal cluster to reconstruct the target texture including missing intensities can be achieved. This POCS-based approach provides a solution to the problem in traditional methods of not being able to perform the selection of the optimal cluster due to the missing intensities within the target texture. Consequently, all of the missing textures are successfully reconstructed by the selected cluster's eigenspaces which correctly approximate the same kinds of textures. Experimental results show subjective and quantitative improvement of the proposed reconstruction technique over previously reported reconstruction techniques.

PHYSICOCHEMICAL AND SENSORY PROPERTIES OF TOFU PREPARED WITH HEAT‐TREATED SOYBEANS

ABSTRACT The sensory characteristics of tofu made from soybeans (var. TaeKwang) heat treated at 60C for 60, 120 and 180 min were investigated to determine the effects of heat shock on the texture and flavor of tofu, which play an important role in consumer preference. Hardness and chewiness increased with heat treatment, reaching maxima at 120 min, while cohesiveness decreased significantly with heat treatment in the 120 min sample. Sensory evaluation showed that the smoothness and homogeneity of the surface of the tofu increased with heat treatment from those of the control. Grassy aroma, which is one of the main undesirable flavors of tofu, was decreased by the heat treatment. Firmness and elasticity both had a maximum value of 8.4 by the 15‐cm line‐scale evaluation in the 120 min treated samples, showing the practical possibility of using mild heat treatment to produce firm and elastic tofu. Increases in the 7S globulin due to heat treatment are thought to be responsible for the textural changes observed. PRACTICAL APPLICATIONSTexture and flavor are major factors in determining the quality of tofu. Tofu has become popular as a healthy food and its consumption is increasing, hence the necessity of producing tofu with various kinds of texture. We found that heat treatment on soybean used in tofu preparation can be one of the methods to control the texture of tofu. The grassy flavor of soy products is the limiting factor that seriously affects their consumption. According to our sensory evaluation, the grassy aroma was decreased significantly by heat treatment, showing that controlled heat treatment has a positive effect on the overall quality of tofu. This study suggests that heat treatment of soybeans can be applied to produce tofu with less grassy flavor and firmer texture.

Neuronal response to texture- and contrast-defined boundaries in early visual cortex

Natural scenes contain a variety of visual cues that facilitate boundary perception (e.g., luminance, contrast, and texture). Here we explore whether single neurons in early visual cortex can process both contrast and texture cues. We recorded neural responses in cat A18 to both illusory contours formed by abutting gratings (ICs, texture-defined) and contrast-modulated gratings (CMs, contrast-defined). We found that if a neuron responded to one of the two stimuli, it also responded to the other. These neurons signaled similar contour orientation, spatial frequency, and movement direction of the two stimuli. A given neuron also exhibited similar selectivity for spatial frequency of the fine, stationary grating components (carriers) of the stimuli. These results suggest that the cue-invariance of early cortical neurons extends to different kinds of texture or contrast cues, and might arise from a common nonlinear mechanism.

An evolutionary system for near-regular texture synthesis

Near-regular texture is probably among the most difficult to handle in the texture synthesis area, because the synthesis must preserve the holistic structural property and the local randomness simultaneously. In this paper, motivated by the relationship between a near-regular texture image and an evolutionary system, we propose a novel texture synthesis algorithm. By defining individuals with appropriate attributes and behaviors, we convert the texture synthesis problem to an evolution process of an evolutionary system. It can achieve high-quality synthesized results on a large variety of near-regular textures without any extra overhead for memory and pretreatment, and the speed approaches real-time. Moreover, it can be easily generalized to deal with other kinds of textures.