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Texture Analysis of Cold-Worked 304L Stainless Steel
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Recrystallization Texture Analysis of FeCoNiCrMnAl[subscript 0.5] High-Entropy Alloy Investigated by
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Instrument and method for X-ray diffraction, fluorescence, and crystal texture analysis without sample preparation
An X-ray diffraction and X-ray fluorescence instrument for analyzing samples having no sample preparation includes a X-ray source configured to output a collimated X-ray beam comprising a continuum spectrum of X-rays to a predetermined coordinate and a photon-counting X-ray imaging spectrometer disposed to receive X-rays output from an unprepared sample disposed at the predetermined coordinate upon exposure of the unprepared sample to the collimated X-ray beam. The X-ray source and the photon-counting X-ray imaging spectrometer are arranged in a reflection geometry relative to the predetermined coordinate.
A Study of Feature Extraction Using Divergence Analysis of Texture Features
An empirical study of texture analysis for feature extraction and classification of high spatial resolution remotely sensed imagery (10 meters) is presented in terms of specific land cover types. The principal method examined is the use of spatial gray tone dependence (SGTD). The SGTD method reduces the gray levels within a moving window into a two-dimensional spatial gray tone dependence matrix which can be interpreted as a probability matrix of gray tone pairs. Haralick et al (1973) used a number of information theory measures to extract texture features from these matrices, including angular second moment (inertia), correlation, entropy, homogeneity, and energy. The derivation of the SGTD matrix is a function of: (1) the number of gray tones in an image; (2) the angle along which the frequency of SGTD is calculated; (3) the size of the moving window; and (4) the distance between gray tone pairs. The first three parameters were varied and tested on a 10 meter resolution panchromatic image of Maryville, Tennessee using the five SGTD measures. A transformed divergence measure was used to determine the statistical separability between four land cover categories forest, new residential, old residential, and industrial for each variation in texture parameters.
Textural features for radar image analysis
Texture is seen as an important spatial feature useful for identifying objects or regions of interest in an image. While textural features have been widely used in analyzing a variety of photographic images, they have not been used in processing radar images. A procedure for extracting a set of textural features for characterizing small areas in radar images is presented, and it is shown that these features can be used in classifying segments of radar images corresponding to different geological formations.
Correlations between Texture Profile Analysis and Sensory Evaluation of Cured Largemouth Bass Meat (Micropterus salmoides)
Texture is an important factor in evaluating the quality of aquatic products. To evaluate the texture properties of cured large mouth bass, edible sodium chloride (0, 1.0, 2.0, and 3.0%) was smeared to the bass meat. Texture profile analysis (TPA) and sensory evaluation were performed to evaluate the quality of the cured samples, and the correlations of the indexes in the two methods were analyzed. Two principal components were obtained from the TPA indexes and sensory evaluation indexes, and the cumulative variance contribution rates were 73.87% and 72.99%, respectively. Results from the principal component analysis showed that the main indicators that affected the TPA were gumminess and springiness, while those that affected sensory evaluation were chewiness and adhesiveness. The TPA index and sensory evaluation could be effectively improved when the sodium chloride added to the bass meat was 1%. In the correlation analysis, sensory springiness was negatively correlated with TPA hardness ( P < 0.05 , r = −0.553) but positively correlated with TPA chewiness ( P < 0.05 , r = 0.596). After stepwise regression analysis, the prediction equation between the sensory springiness and TPA hardness was obtained as SSp=5.770−0.002Ha. These results provide a basis for predicting the quality of large mouth bass cured products.
Fast Image Texture Classification Using Decision Trees
Texture analysis would permit improved autonomous, onboard science data interpretation for adaptive navigation, sampling, and downlink decisions. These analyses would assist with terrain analysis and instrument placement in both macroscopic and microscopic image data products. Unfortunately, most state-of-the-art texture analysis demands computationally expensive convolutions of filters involving many floating-point operations. This makes them infeasible for radiation- hardened computers and spaceflight hardware. A new method approximates traditional texture classification of each image pixel with a fast decision-tree classifier. The classifier uses image features derived from simple filtering operations involving integer arithmetic. The texture analysis method is therefore amenable to implementation on FPGA (field-programmable gate array) hardware. Image features based on the "integral image" transform produce descriptive and efficient texture descriptors. Training the decision tree on a set of training data yields a classification scheme that produces reasonable approximations of optimal "texton" analysis at a fraction of the computational cost. A decision-tree learning algorithm employing the traditional k-means criterion of inter-cluster variance is used to learn tree structure from training data. The result is an efficient and accurate summary of surface morphology in images. This work is an evolutionary advance that unites several previous algorithms (k-means clustering, integral images, decision trees) and applies them to a new problem domain (morphology analysis for autonomous science during remote exploration). Advantages include order-of-magnitude improvements in runtime, feasibility for FPGA hardware, and significant improvements in texture classification accuracy.
Discrete spherical harmonic functions for texture representation and analysis
A basis of discrete harmonic functions for efficient representation and analysis of crystallographic texture is presented. Discrete harmonics are a numerical representation of the harmonics on the sphere. A finite element formulation is utilized to calculate these orthonormal basis functions, which provides several advantageous features for quantitative texture analysis. These include high-precision numerical integration, a simple implementation of the non-negativity constraint and computational efficiency. Simple examples of pole figure and texture interpolation and of Fourier filtering using these basis sets are presented.
Independent Component Analysis of Textures
A common method for texture representation is to use the marginal probability densities over the outputs of a set of multi-orientation, multi-scale filters as a description of the texture. We propose a technique, based on Independent Components Analysis, for choosing the set of filters that yield the most informative marginals, meaning that the product over the marginals most closely approximates the joint probability density function of the filter outputs. The algorithm is implemented using a steerable filter space. Experiments involving both texture classification and synthesis show that compared to Principal Components Analysis, ICA provides superior performance for modeling of natural and synthetic textures.
Mechanical behavior and texture evolution of WE43 magnesium-rare earth alloy in Split-Hopkinson Pressure Bar and Taylor Impact Cylinder Testing
Mechanical behavior and texture evolution of Mg rare-earth alloy WE43 is investigated for strain-rates 10 -3 /s to upwards of 10 5 /s for the two material conditions - as-cast (AC) and T6 age hardened, rolled plate (RT6). The high strain-rate behavior is tested using both Taylor cylinder impact tests (TC) and split Hopkinson pressure bar tests (SHB) and bulk textures are obtained using neutron diffraction. Unlike the quasi-static strained material, AC and RT6 SHB retained high hardening rates throughout the test, even up to 30% true strain. Moreover, the high strain-rate data revealed that the RT6 material has a much higher strength than the AC material, but similar hardening rates despite significantly different initial texture. The flow stress near yield increased up to I 0% for RT6 and up to 30% for AC as the strain-rate increased six orders of magnitude from quasi-static rates 10 -3 /s to 10 3 /s. Neither material exhibited significant plastic anisotropy over the broad range of strain rates, despite the fact that the RT6 material had a moderately strong initial texture. In the TC tests, the geometric cross-sectional changes and texture along the cylinder from the cylindrical sample foot to head are measured and from the neutron diffraction texture analysis, upper-bound estimates of twin volume fraction are obtained as well as dislocation density from analyzing diffraction peak broadening. Recorded geometrical changes along several loading directions show that the material has deformed remarkably homogeneously under impact. Analysis of deformed textures indicates that {101¯2} extension deformation twinning occurred in the RT6 condition over the range of strain rates, with an upper bound estimate of 40% twin volume fraction for approximately 0.10-0.25 true strain. The peak texture components after the impact have their c-axes closely aligned with the impact direction. As a result, these observations are presented and rationalized in the paper.
Image processing in remote sensing data analysis - The state of the art
Image analysis techniques applicable to remote sensing data and covering image models, feature detection, segmentation and classification, texture analysis, and matching are studied. Model types for characterizing images examined include random-field, mosaic, and facet models. Edge and corner detection as well as global extraction of linear features are discussed. Pixel clustering and classification are covered in addition to the regional approach to segmentation. Autocorrelation, second-order gray level probability density, and the use of primitive element statistics are discussed in relation to texture analysis. Finally, reducing the cost of (sub)imaging matching methods (e.g., pixelwise comparison of gray levels and normalized cross-correlation between two images) as well as improving match sharpness is considered.
Visualization of texture components using MTEX
Knowledge of the appearance of texture components and fibres in pole figures, in inverse pole figures and in Euler space is fundamental for texture analysis. For cubic crystal systems, such as steels, an extensive literature exists and, for example, the book by Matthies, Vinel & Helming [Standard Distributions in Texture Analysis: Maps for the Case of Cubic Orthorhomic Symmetry, (1987), Akademie-Verlag Berlin] provides an atlas to identify texture components. For lower crystal symmetries, however, equivalent comprehensive overviews that can serve as guidance for the interpretation of experimental textures do not exist. Here, this paper closes this gap by providing a set of scripts for the MTEX package [Bachmann, Hielscher & Schaeben (2010). Solid State Phenom.160, 63–68] that allow the texture practitioner to compile such an atlas for a given material system, thus aiding orientation distribution function analysis also for non-cubic systems.
PRISMS-Plasticity TM: An Open-Source Rapid Texture Evolution Analysis Pipeline
Not provided.
An Analysis of Texture on Lunar Ground Photos
An experiment was performed in an attempt to evaluate autocorrelation as an indicator of texture using five small patches representing varying distances on each of four lunar photos. Each patch was scanned at 50 micron increments on a 64-level gray scale. Several problems were involved in using the ordinary autocorrelation value but a rougher autocorrelation measure, the percentage of neighboring pairs (at a given lag distance) which fell within one graylevel of one another, yielded very encouraging results. As the distance to the feature decreased, initial slope of the graph of percentage against lag increased, and the general level of the graph decreased. When a crestline was present, the graph tended to continue to decrease at higher lags as well, rather than leveling off after the initial steep slope.
Topographic studies through texture image analysis of LANDSAT data
There are no author-identified significant results in this report.
Analysis of Texture Using the Fractal Model
Properties such as the fractal dimension (FD) can be used for feature extraction and classification of regions within an image. The FD measures the degree of roughness of a surface, so this number is used to characterize a particular region, in order to differentiate it from another. There are two basic approaches discussed in the literature to measure FD: the blanket method, and the box counting method. Both attempt to measure FD by estimating the change in surface area with respect to the change in resolution. We tested both methods but box counting resulted computationally faster and gave better results. Differential Box Counting (DBC) was used to segment a collage containing three textures. The FD is independent of directionality and brightness so five features were used derived from the original image to account for directionality and gray level biases. FD can not be measured on a point, so we use a window that slides across the image giving values of FD to the pixel on the center of the window. Windowing blurs the boundaries of adjacent classes, so an edge-preserving, feature-smoothing algorithm is used to improve classification within segments and to make the boundaries sharper. Segmentation using DBC was 90.8910 accurate.
Step-loaded creep testing of Zircaloy-4 cladding at higher temperatures in the α-phase
A refined understanding of zirconium-based cladding thermomechanical performance during rapid transients is essential for enhancing the safety and operation of light-water reactors. Traditional models for zirconium alloys under accident conditions generally assume that creep dominates fuel cladding performance. Here, these historic models have largely remained unchanged and serve as the basis for safety criteria development. As the U.S. nuclear industry pursues higher burnup levels, the increased release of fission gases during transients raises the risk of cladding failure in the low-temperature hcp α-phase, making the fidelity of these models of greater importance. Creep testing was conducted from 550–700°C with 25–120 MPa applied hoop stresses to investigate Zircaloy-4 deformation at accident-relevant temperatures in the α-phase. Step-loading was employed to capture creep behavior across a wide stress range from a single sample. The stress-strain rate data at higher temperatures (650 and 700°C) were well-described by isotropic versions of the Erbacher and Kaddour models, while the lower temperature data (550 and 600°C) were underpredicted by both anisotropic and isotropic model variants. Greater strain rates during the initial loading step at 650 and 700°C were attributed to recrystallization and grain growth of sub-micron crystallites. Yet, texture analysis revealed the basal split texture remained after testing. These observations produced results suggesting Zircaloy-4 claddings experience higher creep rates across the α-phase than previously thought, possibly related to dynamic anisotropy due to temperature dependent activation of deformation mechanisms, effects of biaxial loading, and variation in material condition between the current testing used in previous model development.