Texture Contrast Research Articles

Fusing Ground-Penetrating Radar Images for Improving Image Characteristics Fidelity

The analysis of ground-penetrating radar (GPR) data is of vital importance for detecting various subsurface features that might manifest as hyperbolic peaks, which are indicators of a buried object or grayscale variation in the case of contrast in the soil texture. This method focuses on identifying exaggerated patterns through a series of image-processing steps. Two GPR images are initially read and preprocessed by extracting channels, flipping, and resizing. Then, specific regions of interest (ROIs) are cropped, and the Fourier transform is further applied to turn them into the frequency domain. With the help of their frequency signatures, these patterns are extracted from the images, and binary masks are constructed to obtain features of interest. These masked images were reconstructed and merged to make hyperbolic features visible. Finally, Local Binary Pattern (LBP) analysis is used to emphasize these hyperbolic peaks, thereby facilitating their recognition across the whole image. The proposed approach improves the detection of performance subsurface features in GPR data; hence, it is an important tool for geophysical surveys and other related applications. The results prove the high performance of the proposed procedure in improving GPR image characteristics.

Specifics of the embodiment of European genre models in Chinese piano music as the basis of performance stylistics

Statement of the problem. A performer who gives “new life" to a composer’s work lets the sound world of music pass through him. In this process, the genre-stylistic “inclination” of the work becomes a signpost on the way to the realization and improvement of its performance style. The article accents the significant of the relating the genre and performance stylistics. From this viewpoint, the Chinese piano works based on the European genre models are considered in the article. Objectives, methods, and novelty of the research. The relationship between the genre and the performance style of the piano works of Chinese composers has been out of the focus of musicologists’ attention until today. The purpose of the article is to consider the specifics of the embodiment of European genre models (such as prelude, sonata, variations) in Chinese piano music as the basis of their performance stylistics. Achieving the goal required a systematic musicological approach and the involvement of historical-typological, musicalaesthetic, genre-stylistic, structural-functional, and interpretative research methods. Research results and conclusion. The samples of Chinese piano music (preludes, sonatas, variations) demonstrate that the specified group of works preserved their constant genre features, while the nationally directed means of expression were adapted to the norms of the European genre matrix, which in many ways contradicted the national ideas and mental foundations of Chinese musical art. The piano prelude genre has become very popular in the works of Chinese composers. All elements of the national musical style, closely related to the deep roots of Chinese musical and, more broadly, artistic thinking, are involved in the process of “concentrating” energy in miniature form with special laconic and aphoristic expression. The complex large-scale structure of the sonata genre promotes the principle of “activity”, offering the performer various musical ideas and emotions. The mental foundations of Chinese art were significantly adapted, especially in the first part – the sonata allegro, where tonal, figurative and textural contrast is the basis of the interaction of themes. New methods of folklore implementation, in which the role of language-creating and form-creating attitudes is growing, have a perceptible influence on the sonata work of Chinese composers. The European genre model of variations reveals new prospects for its development in combination with a freer national model of variations inherent in Chinese music. There is a peculiar “rational” approach to free variation, “ordering” according to the textural, tonal, figurative principle. Thus, the ccomposers try to “inscribe” national mode and intonation means of expression and performance techniques into the structure of the European genre. At the same time, under the influence of the Chinese traditional system of thinking, European genre models receive a certain “national” interpretation, although the features of a specific sample of the European genre remain as dominant, which determines the style of performing these composition. Such works – diverse and interesting – serve to develop the pianist’s performance skills and gain the widest recognition in the repertoire of many contemporary performers.

Mining the interpretable prognostic features from pathological image of intrahepatic cholangiocarcinoma using multi-modal deep learning

BackgroundThe advances in deep learning-based pathological image analysis have invoked tremendous insights into cancer prognostication. Still, lack of interpretability remains a significant barrier to clinical application.MethodsWe established an integrative prognostic neural network for intrahepatic cholangiocarcinoma (iCCA), towards a comprehensive evaluation of both architectural and fine-grained information from whole-slide images. Then, leveraging on multi-modal data, we conducted extensive interrogative approaches to the models, to extract and visualize the morphological features that most correlated with clinical outcome and underlying molecular alterations.ResultsThe models were developed and optimized on 373 iCCA patients from our center and demonstrated consistent accuracy and robustness on both internal (n = 213) and external (n = 168) cohorts. The occlusion sensitivity map revealed that the distribution of tertiary lymphoid structures, the geometric traits of the invasive margin, the relative composition of tumor parenchyma and stroma, the extent of necrosis, the presence of the disseminated foci, and the tumor-adjacent micro-vessels were the determining architectural features that impacted on prognosis. Quantifiable morphological vector extracted by CellProfiler demonstrated that tumor nuclei from high-risk patients exhibited significant larger size, more distorted shape, with less prominent nuclear envelope and textural contrast. The multi-omics data (n = 187) further revealed key molecular alterations left morphological imprints that could be attended by the network, including glycolysis, hypoxia, apical junction, mTORC1 signaling, and immune infiltration.ConclusionsWe proposed an interpretable deep-learning framework to gain insights into the biological behavior of iCCA. Most of the significant morphological prognosticators perceived by the network are comprehensible to human minds.Graphical

Mode-locking and wavelength-tuning of a NPR fiber laser based on optical speckle.

Passively mode-locked fiber lasers based on nonlinear polarization rotation (NPR) have been widely used due to their ability to produce short pulses with high peak power. Nevertheless, environmental perturbations can influence the mode-locked state, making it a challenge for the practical implementation. Therefore, researchers are searching for assessment criteria to quickly assist and maintain mode-locking of NPR fiber lasers. Speckle patterns containing spectral information can be generated when the laser transmits through a scattering medium, which can serve as indicators of the mode-locked state. The mode-locked regions are confined to the area close to the minimum texture contrast of speckle patterns. Based on these characteristics, we manually simulate the automatic mode-locking (AML). In addition, we utilize convolutional neural networks (CNNs) to recognize speckle patterns of wavelength tunable lasers and determine the center wavelength.

Perceptual thresholds for differences in CT noise texture.

The average () or peak () noise power spectrum (NPS) frequency is often used as a one-parameter descriptor of the CT noise texture. Our study develops a more complete two-parameter model of the CT NPS and investigates the sensitivity of human observers to changes in it. A model of CT NPS was created based on its and a half-Gaussian fit () to the downslope. Two-alternative forced-choice staircase studies were used to determine perceptual thresholds for noise texture, defined as parameter differences with a predetermined level of discrimination performance (80% correct). Five imaging scientist observers performed the forced-choice studies for eight directions in the -space, for two reference NPSs (corresponding to body and lung kernels). The experiment was repeated with 32 radiologists, each evaluating a single direction in the -space. NPS differences were quantified by the noise texture contrast (), the integral of the absolute NPS difference. The two-parameter NPS model was found to be a good representation of various clinical CT reconstructions. Perception thresholds for alone are for body and for lung NPSs. For , these values are 0.15 and , respectively. Thresholds change if the other parameter also changes. Different NPSs with the same or can be discriminated. Nonradiologist observers did not need more than radiologists. or is insufficient to describe noise texture completely. The discrimination of noise texture changes depending on its frequency content. Radiologists do not discriminate noise texture changes better than nonradiologists.

Human activity recognition using a single-photon direct time-of-flight sensor

Single-Photon Avalanche Diode (SPAD) direct Time-of-Flight (dToF) sensors provide depth imaging over long distances, enabling the detection of objects even in the absence of contrast in colour or texture. However, distant objects are represented by just a few pixels and are subject to noise from solar interference, limiting the applicability of existing computer vision techniques for high-level scene interpretation. We present a new SPAD-based vision system for human activity recognition, based on convolutional and recurrent neural networks, which is trained entirely on synthetic data. In tests using real data from a 64×32 pixel SPAD, captured over a distance of 40 m, the scheme successfully overcomes the limited transverse resolution (in which human limbs are approximately one pixel across), achieving an average accuracy of 89% in distinguishing between seven different activities. The approach analyses continuous streams of video-rate depth data at a maximal rate of 66 FPS when executed on a GPU, making it well-suited for real-time applications such as surveillance or situational awareness in autonomous systems.

Distinct characteristics and severity of brain magnetic resonance imaging lesions in women and men with multiple sclerosis assessed using verified texture analysis measures.

In vivo characterization of brain lesion types in multiple sclerosis (MS) has been an ongoing challenge. Based on verified texture analysis measures from clinical magnetic resonance imaging (MRI), this study aimed to develop a method to identify two extremes of brain MS lesions that were approximately severely demyelinated (sDEM) and highly remyelinated (hREM), and compare them in terms of common clinical variables. Texture analysis used an optimized gray-level co-occurrence matrix (GLCM) method based on FLAIR MRI from 200 relapsing-remitting MS participants. Two top-performing metrics were calculated: texture contrast and dissimilarity. Lesion identification applied a percentile approach according to texture values calculated: ≤ 25 percentile for hREM and ≥75 percentile for sDEM. The sDEM had a greater total normalized volume yet smaller average size, and worse MRI texture than hREM. In lesion distribution mapping, the two lesion types appeared to overlap largely in location and were present the most in the corpus callosum and periventricular regions. Further, in sDEM, the normalized volume was greater and in hREM, the average size was smaller in men than women. There were no other significant results in clinical variable-associated analyses. Percentile statistics of competitive MRI texture measures may be a promising method for probing select types of brain MS lesion pathology. Associated findings can provide another useful dimension for improved measurement and monitoring of disease activity in MS. The different characteristics of sDEM and hREM between men and women likely adds new information to the literature, deserving further confirmation.

MRI Image Fusion Based on Sparse Representation with Measurement of Patch-Based Multiple Salient Features

Multimodal medical image fusion is a fundamental, but challenging, problem in the fields of brain science research and brain disease diagnosis, as it is challenging for sparse representation (SR)-based fusion to characterize activity levels with a single measurement and not lose effective information. In this study, the Kronecker-criterion-based SR framework was applied for medical image fusion with a patch-based activity level, integrating salient features of multiple domains. Inspired by the formation process of vision systems, the spatial saliency was characterized by textural contrast (TC), composed of luminance and orientation contrasts, to promote the participation of more highlighted textural information in the fusion process. As a substitute for the conventional l1-norm-based sparse saliency, the sum of sparse salient features (SSSF) was used as a metric for promoting the participation of more significant coefficients in the composition of the activity level measurement. The designed activity level measurement was verified to be more conducive to maintaining the integrity and sharpness of detailed information. Various experiments on multiple groups of clinical medical images verified the effectiveness of the proposed fusion method in terms of both visual quality and objective assessment. Furthermore, this study will be helpful for the further detection and segmentation of medical images.

Multi-level ultra-deep fault-controlled karst reservoirs characterization methods for the Shunbei field

Ultra-deep carbonate fault-controlled reservoirs are characterized by large burial depths, low dissolution degrees, strong heterogeneity, and limited effective well data. Accurate 3D characterization based on seismic data is essential for efficient development of this type of reservoir. However, traditional seismic prediction methods are insufficient to accurately characterize different reservoir levels in the exploration and development of fault-controlled ultra-deep reservoirs. We propose a set of improved multi-level characterization methods for fault-controlled reservoirs. The improved methods could recover seismic information obscured by strong reflections and reduce the uncertainty of seismic interpretation. This study combined seismic strong reflection suppression and sedimentary strata seismic reflection interference elimination, proposed improved inversion methods, and advanced attribute calculation methods to improve the identification accuracy of reservoirs. In particular, we proposed the karst cave carving method based on improved inversion method and karst cave enhancement algorithm, the dissolved pore zone identification method based on optimization energy envelope algorithm, and the fault-fracture zone characterization method based on optimized atomic decomposition texture contrast. These methods were thoroughly validated by theoretical 3D models and field data. The proposed multi-level characterization methods can effectively improve the identification accuracy of fault-controlled karst reservoirs, provide a benchmark for predicting similar strong heterogeneous carbonate reservoirs, and provide reliable support for further facies modeling.

Extracting high-precision full-field displacement from videos via pixel matching and optical flow

Accurate sensing of full-field displacements plays a significant role in dynamic testing for structural health monitoring (SHM). Recently, video camera has become a more promising tool in displacement sensing due to advantages such as low price, agility, simultaneous measurement, and high spatial sensing resolution. Traditional target tracking approaches (e.g., digital image correlation, template matching, etc.) typically require clear targets on the structure surface and thus only provide sparse displacement. Phase-based motion extraction enables full-field displacement measurement at subpixel precision without the need of markers as tracking targets, which has achieved great success in structural modal identification among many other SHM applications. However, the phase-based approach in a Eulerian framework fails to deal with large motions. To tackle these challenges, an enhanced phase-based method, that synergistically combines the developed local amplitude supplemented pixel matching algorithm and optical flow processing, is proposed for full-field displacement sensing with subpixel precision meanwhile capable of dealing with large motions. In particular, the pixel matching estimates the integer-pixel motion vectors of pixels which have sufficient texture contrast determined by local amplitude. The optical flow, represented by the first order Taylor series, is then employed to approximate the rest subpixel motion associated with the integer-pixel motion vectors. The proposed technique is validated by processing a field-test videos with LVDT reference and then applied to other several lab/field recorded videos of different vibrational objects for displacement extraction and modal identification.

Gray-Level Co-occurrence Matrix Analysis of Nuclear Textural Patterns in Laryngeal Squamous Cell Carcinoma: Focus on Artificial Intelligence Methods.

Gray-level co-occurrence matrix (GLCM) and discrete wavelet transform (DWT) analyses are two contemporary computational methods that can identify discrete changes in cell and tissue textural features. Previous research has indicated that these methods may be applicable in the pathology for identification and classification of various types of cancers. In this study, we present findings that squamous epithelial cells in laryngeal carcinoma, which appear morphologically intact during conventional pathohistological evaluation, have distinct nuclear GLCM and DWT features. The average values of nuclear GLCM indicators of these cells, such as angular second moment, inverse difference moment, and textural contrast, substantially differ when compared to those in noncancerous tissue. In this work, we also propose machine learning models based on random forests and support vector machine that can be successfully trained to separate the cells using GLCM and DWT quantifiers as input data. We show that, based on a limited cell sample, these models have relatively good classification accuracy and discriminatory power, which makes them suitable candidates for future development of AI-based sensors potentially applicable in laryngeal carcinoma diagnostic protocols.

Parametrically upscaled crack nucleation model (PUCNM) for fatigue nucleation in titanium alloys containing micro-texture regions (MTR)

Micro-texture regions (MTRs), delineated as the clusters of grains with similar crystallographic orientations in the polycrystalline microstructure, play a significant role in fatigue crack nucleation and life of structures of Ti alloys. This paper develops a parametrically upscaled constitutive and crack nucleation modeling (PUCM/PUCNM) platform for predicting structural-scale fatigue crack nucleation in α/β Ti–6Al–4V alloys, whose polycrystalline microstructures contain MTRs. The PUCM/PUCNM platform bridges micro and macro scales through thermodynamically consistent incorporation of representative aggregated microstructural parameters (RAMPs) in macroscopic constitutive relations. A novel RAMP kMTRθc that captures both the MTR size and contrast in the overall texture is proposed to quantitatively represent the MTR intensity in the microstructure. Geometric analysis and comparison with experimental data establish the effectiveness of kMTRθc in characterizing the MTR distributions. The impact of MTR characteristics on fatigue crack nucleation is evaluated through the support vector regression (SVR)-aided Sobol analysis, based on data from crystal plasticity FE simulations of polycrystalline microstructure volumes with a grain-scale crack nucleation model. The PUCNM is uniquely suitable for the incorporation of the MTR RAMP kMTRθc in its probabilistic framework. A novel functional form is derived using the genetic programming-based symbolic regression (GPSR). The PUCM/PUCNM tool is used to simulate an engine blade under dwell loading conditions. Results exhibit the reduction of nucleation life with a higher level of MTR intensity, despite the same overall textures.

Gray Level Co-Occurrence Matrix, Fractal and Wavelet Analyses of Discrete Changes in Cell Nuclear Structure following Osmotic Stress: Focus on Machine Learning Methods

In this work, we demonstrate that it is possible to create supervised machine-learning models using a support vector machine and random forest algorithms to separate yeast cells exposed to hyperosmotic stress from intact cells. We performed fractal, gray level co-occurrence matrix (GLCM), and discrete wavelet transform analyses on digital micrographs of nuclear regions of interest of a total of 2000 Saccharomyces cerevisiae cells: 1000 exposed to hyperosmotic environments and 1000 control cells. For each nucleus, we calculated values for fractal dimension, angular second moment, inverse difference moment, textural contrast, correlation feature, textural variance, and discrete wavelet coefficient energy. The support vector machine achieved an acceptable classification accuracy of 71.7% in predicting whether the cell belonged to the experimental or control group. The random forest model performed better than the support vector machine, with a classification accuracy of 79.8%. These findings can serve as a starting point for developing AI-based methods that use GLCM, fractal, and wavelet data to classify damaged and healthy cells and make predictions about various physiological and pathological phenomena associated with osmotic stress.

Identification of plinthite or saprolite residue in soils with high textural contrast in the southern Brazil

Soils classified as Alisols are very frequent in the sedimentary agricultural areas of southern Brazil. The presence of red mottles with morphology similar to plinthite and saprolite residue is very common in these soils, and its identification can be considered a difficult task, both in the field and in the laboratory. The incorrect identification of these redoximorphic features can affect soils’ taxonomic and technical classification. We aimed to compare morphological, physical, chemical and mineralogical data to identify reddish mottles, [...]

Image feature extraction and recognition model construction of coal and gangue based on image processing technology

Using image recognition technology to realize coal gangue recognition is one of the development directions of intelligent fully mechanized caving mining. Aiming at the problem of low accuracy of coal gangue recognition in fully mechanized caving mining, the extraction method of Coal and gangue images features is proposed, and the corresponding coal gangue recognition model is constructed. The illuminance value is an important factor affecting the imaging quality. Therefore, a multi-light source image acquisition system is designed, and the optimal illuminance value suitable for coal and gangue images acquisition is determined to be 17,130 Lux. There is a large amount of image noise in the gray-sc5ale image, so Gaussian filtering is used to eliminate the noise in the gray-scale image of coal and gangue. Then, six gray-scale features and four texture features are extracted from 900 coal and gangue images respectively. It is concluded that the three kinds of features of gray skewness, gray variance and texture contrast have the highest discrimination on coal and gangue images. Least squares vector machine has a strong ability to classify, so the use of least squares vector machine to achieve coal gangue identification, and build coal gangue identification model. The results show that the recognition accuracy of the model for coal gangue images is 92.2% and 91.5%, respectively, with gray skewness and texture contrast as indicators. This study provides a reliable theoretical support for solving the problem of low recognition rate of coal gangue in fully mechanized caving mining.

Soil arthropod responses to subsoil manuring in irrigated vegetable and rainfed grain production

Diverse soil biology is being increasingly recognised as important for productive soils. Subsoil manuring – the process of placing organic materials underground using a modified deep ripper – has been used to improve the structure and productivity of texture contrast soils. Using soil arthropods as indicator species, this study investigated the soil biology responses of the subsoil manuring approach. Different organic materials were placed in the upper zone of a clay subsoil at field sites used for irrigated vegetable and rainfed grain production. Soil samples were taken from the topsoil and from the depth of organic material placement and processed to extract soil arthropods. Of three suborders of soil arthropods recorded, mesostigmatid mites showed the most notable increases in abundance in response to subsoil manuring. The placement of organic materials at depth resulted in elevated abundance of soil arthropods at depths that are not the normal habitat of these organisms. Abundance and richness were both suppressed under irrigated production compared to rainfed, although it is acknowledged that a number of management practice differences between the two production systems could contribute to the differences observed.

Temporal drivers of textural complexity: A pilot study and analysis of simultaneous temporal dominance of sensations and temporal textural complexity data

AbstractThe aim of this study was to assess the applicability of simultaneous methodologies to describe dynamic textural complexity. In a simultaneous task, temporal dominance of sensations (TDS) and temporal textural complexity (TTC) were performed in one session (n = 17). A further 18 participants performed TDS and TTC on separate occasions; data from the simultaneous and separate session were then compared. A further aim was to investigate the temporal nature of textural complexity—temporal drivers of textural complexity were examined, as well as ratings of textural complexity across time. Regression modeling and multivariate analyses indicated little difference in how the participants in the simultaneous and separate tasks performed; only minor differences in the use of TDS attributes were noted. Positive drivers included texture contrast and crumbly/particles, while smooth was a negative driver. Significant differences in TTC data separated into three time intervals were found; a trend of increasing textural complexity during mastication until a midway point followed by a decline emerged. Further research with larger samples is needed to support these findings. However, for the first time this work supports the use of simultaneous tasks in sensory evaluation and indicates the importance of temporality in direct ratings of complexity.Practical applicationsSimultaneous tasks are not commonly used in sensory evaluation, this research suggests their applicability and value; ultimately they may save time and money in research and product development. Direct ratings of sensory complexity are often reported prior to or after descriptive temporal evaluation; this may underestimate true perceived complexity, indicating the importance of considering temporal measures. Sensory complexity is considered an important sensory construct given its arousal potential; thus, identification of drivers of complexity may inform product development.

Endogenous attention biases transformational apparent motion based on high-level shape representations.

When two pre-existing, separated squares are connected by the sudden onset of a bar between them, viewers do not perceive the bar to appear all at once. Instead, they see an illusory morphing of the original squares over time. The direction of this transformational apparent motion (TAM) can be influenced by endogenous attention deployed before the appearance of the connecting bar. Here, we investigated whether the influence of endogenous attention on TAM results from operations over high-level feature-independent shape representations, or instead over lower level shape representations defined by specific visual features. To do so, we tested the influence of endogenous attention on TAM in first- and second-order displays, which shared common shapes but had different shape-defining attributes (luminance and texture contrast, respectively). In terms of both the magnitude of directional bias and timing, we found that endogenous attention exerted a similar influence on both first- and second-order objects. These results imply that endogenous attention biases the perceived direction of TAM by operating on high-level shape representations that are invariant to the low-level visual features that define them. Our results support a four-stage model of TAM, where a feature encoding stage passes a features-specific layout to a parsing stage that forms discrete, high-level meta-featural shapes, which are then matched and visually interpolated over time.

DIVFusion: Darkness-free infrared and visible image fusion

As a vital image enhancement technology, infrared and visible image fusion aims to generate high-quality fused images with salient targets and abundant texture in extreme environments. However, current image fusion methods are all designed for infrared and visible images with normal illumination. In the night scene, existing methods suffer from weak texture details and poor visual perception due to the severe degradation in visible images, which affects subsequent visual applications. To this end, this paper advances a darkness-free infrared and visible image fusion method (DIVFusion), which reasonably lights up the darkness and facilitates complementary information aggregation. Specifically, to improve the fusion quality of nighttime images, which suffer from low illumination, texture concealment, and color distortion, we first design a scene-illumination disentangled network (SIDNet) to strip the illumination degradation in nighttime visible images while preserving informative features of source images. Then, a texture–contrast enhancement fusion network (TCEFNet) is devised to integrate complementary information and enhance the contrast and texture details of fused features. Moreover, a color consistency loss is designed to mitigate color distortion from enhancement and fusion. Finally, we fully consider the intrinsic relationship between low-light image enhancement and image fusion, achieving effective coupling and reciprocity. In this way, the proposed method is able to generate fused images with real color and significant contrast in an end-to-end manner. Extensive experiments demonstrate that DIVFusion is superior to state-of-the-art algorithms in terms of visual quality and quantitative evaluations. Particularly, low-light enhancement and dual-modal fusion provide more effective information to the fused image and boost high-level vision tasks. Our code is publicly available at https://github.com/Xinyu-Xiang/DIVFusion.

Subsoil manuring produces no measurable change in soil or yield in irrigated vegetable production in the first year after treatment

The expansion of irrigation infrastructure in Tasmania, Australia, has led to intensified irrigated crop production, including for vegetables. This has resulted in increased irrigation of texture contrast soils, and other soils with high clay content in the subsoil. Widely used for rain-fed grain production in other parts of Australia, these soils have been subject to a range of soil amelioration approaches in an effort to improve their soil physical characteristics and capacity to produce crops. Subsoil manuring – the process of placing organic soil ameliorants at a target depth using a specially modified deep ripper – has proven to be successful in increasing yields of rain-fed grain crops. Locally available organic materials (chicken manure and poppy seed meal) were placed in the upper zone of a clay subsoil at three sites used for irrigated vegetable production. The crops grown were green peas for processing and carrots for seed production. Normal grower management practices were the reference against which treatments were compared. Subsequent measurements showed no consistent or significant benefit from subsoil manuring one year after treatment under the soil and crop management conditions used in this research.