Stochastic Textures Research Articles

Evaluation of surface texturing on chrome-coated cylinder liners via deterministic mixed lubrication simulation

This study investigates the mixed lubrication performance of various surface texture configurations in the piston ring/cylinder liner conjunction of a two-stroke internal combustion engine using a deterministic mixed lubrication model. The numerical model simultaneously solves the Reynolds equation with mass-conserving cavitation to calculate inter-asperity hydrodynamic pressures and an elastic, perfectly plastic, rough contact model to determine contact pressures at each asperity interaction. Gaussian Mixture Model clustering was employed to enhance surface characterization. The deterministic simulation approach considers the full-scale representation of the cylinder liner topography to accurately capture the influence of surface features on the hydrodynamic support and friction under mixed lubrication conditions. The investigated cylinder liners were initially hard-chrome-coated and honed, resulting in a stochastic arrangement of surface pores, and then deterministic patterns of surface pockets were created by micro electrodischarge machining (EDM). Surface measurements were performed using laser interferometry, providing input for the mixed lubrication simulations. The study also explored the virtual removal of ridges formed around the pockets by the EDM technique. Key findings indicate that the stochastic texture outperformed the hybrid texture (stochastic + deterministic) in the boundary and mixed lubrication regimes, showing higher hydrodynamic support at low separations but increased hydrodynamic shear stresses at higher speeds. Conversely, deterministic textures exhibited a significant decrease in average hydrodynamic shear stress at high velocities. These results highlight the critical role of surface texture in tribological behavior and suggest that localized textures on cylinder liners can potentially optimize engine performance. The study recommends further exploration of a broader range of texture geometries, densities, and distribution patterns to enhance engine design strategies.

Stochastic geometry models for texture synthesis of machined metallic surfaces: sandblasting and milling

Training defect detection algorithms for visual surface inspection systems requires a large and representative set of training data. Often there is not enough real data available which additionally cannot cover the variety of possible defects. Synthetic data generated by a synthetic visual surface inspection environment can overcome this problem. Therefore, a digital twin of the object is needed, whose micro-scale surface topography is modeled by texture synthesis models. We develop stochastic texture models for sandblasted and milled surfaces based on topography measurements of such surfaces. As the surface patterns differ significantly, we use separate modeling approaches for the two cases. Sandblasted surfaces are modeled by a combination of data-based texture synthesis methods that rely entirely on the measurements. In contrast, the model for milled surfaces is procedural and includes all process-related parameters known from the machine settings.

Filtering After Shading With Stochastic Texture Filtering

2D texture maps and 3D voxel arrays are widely used to add rich detail to the surfaces and volumes of rendered scenes, and filtered texture lookups are integral to producing high-quality imagery. We show that applying the texture filter after evaluating shading generally gives more accurate imagery than filtering textures before BSDF evaluation, as is current practice. These benefits are not merely theoretical, but are apparent in common cases. We demonstrate that practical and efficient filtering after shading is possible through the use of stochastic sampling of texture filters. Stochastic texture filtering offers additional benefits, including efficient implementation of high-quality texture filters and efficient filtering of textures stored in compressed and sparse data structures, including neural representations. We demonstrate applications in both real-time and offline rendering and show that the additional error from stochastic filtering is minimal. We find that this error is handled well by either spatiotemporal denoising or moderate pixel sampling rates.

Influence of bio-inspired surface texture of additively manufactured 17-4 PH stainless steel adherends on the strength of adhesively bonded joints

The bonding potential of as-printed additively manufactured stainless steel is largely unknown, and the use of additional hierarchical bio-inspired surface texture has not yet been explored or characterized. In this study, it is demonstrated with the use of Selective Laser Melting (SLM) that a hierarchy of three surface features at different length scales can greatly improve the shear strength of metal-metal adhesive joints. The intrinsic stochastic texture of the SLM as-printed surface showed a high contact angle and a mixed failure with a dominance of adhesive failure. However, enhancing the as-printed surface with the Tree Frog (TF) and Fish Scale (FS) bio-inspired textures produced super-wetting surfaces, significantly improving the shear strength due to the synergistic effect of mechanical interlocking and physical adsorption adhesion mechanisms, resulting in mixed failure. The results indicated that the interfacial bonding performance was in line with the super-wetting behavior and was higher for the samples with a hierarchy of three surface features. The highest increase in bond strength was found for the tree frog hierarchical hexagonal texture (+70% compared to the as-printed texture). In contrast, abrasion of the as-printed surface with P1000-grit paper significantly reduced the surface roughness and increased the water contact angle, resulting in the lowest shear strength.

Unpaired mesh-to-image translation for 3D fluorescent microscopy images of neurons.

While Generative Adversarial Networks (GANs) can now reliably produce realistic images in a multitude of imaging domains, they are ill-equipped to model thin, stochastic textures present in many large 3D fluorescent microscopy (FM) images acquired in biological research. This is especially problematic in neuroscience where the lack of ground truth data impedes the development of automated image analysis algorithms for neurons and neural populations. We therefore propose an unpaired mesh-to-image translation methodology for generating volumetric FM images of neurons from paired ground truths. We start by learning unique FM styles efficiently through a Gramian-based discriminator. Then, we stylize 3D voxelized meshes of previously reconstructed neurons by successively generating slices. As a result, we effectively create a synthetic microscope and can acquire realistic FM images of neurons with control over the image content and imaging configurations. We demonstrate the feasibility of our architecture and its superior performance compared to state-of-the-art image translation architectures through a variety of texture-based metrics, unsupervised segmentation accuracy, and an expert opinion test. In this study, we use 2 synthetic FM datasets and 2 newly acquired FM datasets of retinal neurons.

Longitudinal brain tumor segmentation prediction in MRI using feature and label fusion

This work proposes a novel framework for brain tumor segmentation prediction in longitudinal multimodal MRI scans, comprising two methods; feature fusion and joint label fusion (JLF). The first method fuses stochastic multi-resolution texture features with tumor cell density feature to obtain tumor segmentation predictions in follow-up timepoints using data from baseline pre-operative timepoint. The cell density feature is obtained by solving the 3D reaction-diffusion equation for biophysical tumor growth modelling using the Lattice-Boltzmann method. The second method utilizes JLF to combine segmentation labels obtained from (i) the stochastic texture feature-based and Random Forest (RF)-based tumor segmentation method; and (ii) another state-of-the-art tumor growth and segmentation method, known as boosted Glioma Image Segmentation and Registration (GLISTRboost, or GB). We quantitatively evaluate both proposed methods using the Dice Similarity Coefficient (DSC) in longitudinal scans of 9 patients from the public BraTS 2015 multi-institutional dataset. The evaluation results for the feature-based fusion method show improved tumor segmentation prediction for the whole tumor(DSCWT = 0.314, p = 0.1502), tumor core (DSCTC = 0.332, p = 0.0002), and enhancing tumor (DSCET = 0.448, p = 0.0002) regions. The feature-based fusion shows some improvement on tumor prediction of longitudinal brain tumor tracking, whereas the JLF offers statistically significant improvement on the actual segmentation of WT and ET (DSCWT = 0.85 ± 0.055, DSCET = 0.837 ± 0.074), and also improves the results of GB. The novelty of this work is two-fold: (a) exploit tumor cell density as a feature to predict brain tumor segmentation, using a stochastic multi-resolution RF-based method, and (b) improve the performance of another successful tumor segmentation method, GB, by fusing with the RF-based segmentation labels.

Texture Variation Adaptive Image Denoising With Nonlocal PCA

Image textures, as a kind of local variations, provide important information for the human visual system. Many image textures, especially the small-scale or stochastic textures, are rich in high-frequency variations, and are difficult to be preserved. Current state-of-the-art denoising algorithms typically adopt a nonlocal approach consisting of image patch grouping and group-wise denoising filtering. To achieve a better image denoising while preserving the variations in texture, we first adaptively group high correlated image patches with the same kinds of texture elements (texels) via an adaptive clustering method. This adaptive clustering method is applied in an over-clustering-and-iterative-merging approach, where its noise robustness is improved with a custom merging threshold relating to the noise level and cluster size. For texture-preserving denoising of each cluster, considering that the variations in texture are captured and wrapped in not only the between-dimension energy variations but also the within-dimension variations of PCA transform coefficients, we further propose a PCA-transform-domain variation adaptive filtering method to preserve the local variations in textures. Experiments on natural images show the superiority of the proposed transform-domain variation adaptive filtering to traditional PCA-based hard or soft threshold filtering. As a whole, the proposed denoising method achieves a favorable texture-preserving performance both quantitatively and visually, especially for irregular textures, which is further verified in camera raw image denoising.

Acquiring spatially varying appearance of printed holographic surfaces

We present two novel and complimentary approaches to measure diffraction effects in commonly found planar spatially varying holographic surfaces. Such surfaces are increasingly found in various decorative materials such as gift bags, holographic papers, clothing and security holograms, and produce impressive visual effects that have not been previously acquired for realistic rendering. Such holographic surfaces are usually manufactured with one dimensional diffraction gratings that are varying in periodicity and orientation over an entire sample in order to produce a wide range of diffraction effects such as gradients and kinematic (rotational) effects. Our proposed methods estimate these two parameters and allow an accurate reproduction of these effects in real-time. The first method simply uses a point light source to recover both the grating periodicity and orientation in the case of regular and stochastic textures. Under the assumption that the sample is made of the same repeated diffractive tile, good results can be obtained using just one to five photographs on a wide range of samples. The second method is based on polarization imaging and enables an independent high resolution measurement of the grating orientation and relative periodicity at each surface point. The method requires a minimum of four photographs for accurate results, does not assume repetition of an exemplar tile, and can even reveal minor fabrication defects. We present point light source renderings with both approaches that qualitatively match photographs, as well as real-time renderings under complex environmental illumination.

Bayesian Modeling of Motion Perception Using Dynamical Stochastic Textures.

A common practice to account for psychophysical biases in vision is to frame them as consequences of a dynamic process relying on optimal inference with respect to a generative model. The study presented here details the complete formulation of such a generative model intended to probe visual motion perception with a dynamic texture model. It is derived in a set of axiomatic steps constrained by biological plausibility. We extend previous contributions by detailing three equivalent formulations of this texture model. First, the composite dynamic textures are constructed by the random aggregation of warped patterns, which can be viewed as three-dimensional gaussian fields. Second, these textures are cast as solutions to a stochastic partial differential equation (sPDE). This essential step enables real-time, on-the-fly texture synthesis using time-discretized autoregressive processes. It also allows for the derivation of a local motion-energy model, which corresponds to the log likelihood of the probability density. The log likelihoods are essential for the construction of a Bayesian inference framework. We use the dynamic texture model to psychophysically probe speed perception in humans using zoom-like changes in the spatial frequency content of the stimulus. The human data replicate previous findings showing perceived speed to be positively biased by spatial frequency increments. A Bayesian observer who combines a gaussian likelihood centered at the true speed and a spatial frequency dependent width with a "slow-speed prior" successfully accounts for the perceptual bias. More precisely, the bias arises from a decrease in the observer's likelihood width estimated from the experiments as the spatial frequency increases. Such a trend is compatible with the trend of the dynamic texture likelihood width.

Blind deblurring of natural stochastic textures using an anisotropic fractal model and phase retrieval algorithm.

The challenging inverse problem of blind deblurring has been investigated thoroughly for natural images. Existing algorithms exploit edge-type structures, or similarity to smaller patches within the image, to estimate the correct blurring kernel. However, these methods do not perform well enough on natural stochastic textures (NST), which are mostly random and in general are not characterized by distinct edges and contours. In NST even small kernels cause severe degradation to images. Restoration poses therefore an outstanding challenge. In this work, we refine an existing method by implementing an anisotropic fractal model to estimate the blur kernel's power spectral density. The final kernel is then estimated via an adaptation of a phase retrieval algorithm, originally proposed for sparse signals. We further incorporate additional constraints that are specific to blur filters, to yield even better results. The latter are compared with results obtained by recently published blind deblurring methods.

High-Performance By-Example Noise using a Histogram-Preserving Blending Operator

We propose a new by-example noise algorithm that takes as input a small example of a stochastic texture and synthesizes an infinite output with the same appearance. It works on any kind of random-phase inputs as well as on many non-random-phase inputs that are stochastic and non-periodic, typically natural textures such as moss, granite, sand, bark, etc. Our algorithm achieves high-quality results comparable to state-of-the-art procedural-noise techniques but is more than 20 times faster. Our approach is conceptually simple: we partition the output texture space on a triangle grid and associate each vertex with a random patch from the input such that the evaluation inside a triangle is done by blending 3 patches. The key to this approach is the blending operation that usually produces visual artifacts such as ghosting, softened discontinuities and reduced contrast, or introduces new colors not present in the input. We analyze these problems by showing how linear blending impacts the histogram and show that a blending operator that preserves the histogram prevents these problems. The main requirement for a rendering application is to implement such an operator in a fragment shader without further post-processing, i.e. we need a histogram-preserving blending operator that operates only at the pixel level. Our insight for the design of this operator is that, with Gaussian inputs, histogram-preserving blending boils down to mean and variance preservation, which is simple to obtain analytically. We extend this idea to non-Gaussian inputs by "Gaussianizing" them with a histogram transformation and "de-Gaussianizing" them with the inverse transformation after the blending operation. We show how to precompute and store these histogram transformations such that our algorithm can be implemented in a fragment shader.

Quantitative MR Image Analysis for Brian Tumor.

This paper presents an integrated quantitative MR image analysis framework to include all necessary steps such as MRI inhomogeneity correction, feature extraction, multiclass feature selection and multimodality abnormal brain tissue segmentation respectively. We first obtain mathematical algorithm to compute a novel Generalized multifractional Brownian motion (GmBm) texture feature. We then demonstrate efficacy of multiple multiresolution texture features including regular fractal dimension (FD) texture, and stochastic texture such as multifractional Brownian motion (mBm) and GmBm features for robust tumor and other abnormal tissue segmentation in brain MRI. We evaluate these texture and associated intensity features to effectively delineate multiple abnormal tissues within and around the tumor core, and stroke lesions using large scale public and private datasets.

Statistics of Natural Stochastic Textures and Their Application in Image Denoising.

Natural stochastic textures (NSTs), characterized by their fine details, are prone to corruption by artifacts, introduced during the image acquisition process by the combined effect of blur and noise. While many successful algorithms exist for image restoration and enhancement, the restoration of natural textures and textured images based on suitable statistical models has yet to be further improved. We examine the statistical properties of NST using three image databases. We show that the Gaussian distribution is suitable for many NST, while other natural textures can be properly represented by a model that separates the image into two layers; one of these layers contains the structural elements of smooth areas and edges, while the other contains the statistically Gaussian textural details. Based on these statistical properties, an algorithm for the denoising of natural images containing NST is proposed, using patch-based fractional Brownian motion model and regularization by means of anisotropic diffusion. It is illustrated that this algorithm successfully recovers both missing textural details and structural attributes that characterize natural images. The algorithm is compared with classical as well as the state-of-the-art denoising algorithms.

Unsupervised Segmentation and Categorization of Skin Lesions Using Adaptative Thresholds and Stochastic Features

<p>This work presents a novel unsupervised method to segment skin<br />lesions in macroscopic images, grouping the pixels into three disjoint<br />categories, namely ’skin lesion’, ’suspicious region’ and ’healthy<br />skin’. These skin region categories are obtained by analyzing the<br />agreement of adaptative thresholds applied to the different skin image<br />color channels. In the sequence we use stochastic texture features<br />to refine the suspicious regions. Our preliminary results are<br />promising, and suggest that skin lesions can be segmented successfully<br />with the proposed approach. Also, ’suspicious regions’<br />are identified correctly, where it is uncertain if they belong to skin<br />lesions or to the surrounding healthy skin.</p>

Image segmentation via multi-scale stochastic regional texture appearance models

An ongoing challenge in the area of image segmentation is in dealing with scenes exhibiting complex textural characteristics. While many approaches have been proposed to tackle this particular challenge, a related topic of interest that has not been fully explored for dealing with this challenge is stochastic texture models, particularly for characterizing textural characteristics within regions of varying sizes and shapes. Therefore, this paper presents a novel method for image segmentation based on the concept of multi-scale stochastic regional texture appearance models. In the proposed method, a multi-scale representation of the image is constructed using an iterative bilateral scale space decomposition. Local texture features are then extracted via image patches and random projections to generate stochastic texture features. A texton dictionary is built from the stochastic features, and used to represent the global texture appearance model. Based on this global texture appearance model, a regional texture appearance model can then be obtained based on the texton occurrence probability given a region within an image. Finally, a stochastic region merging algorithm that allows the computation of complex features is presented to perform image segmentation based on proposed regional texture appearance model. Experimental results using the BDSD300 segmentation dataset showed that the proposed method achieves a Probabilistic Rand Index (PRI) of 0.83 and an F-measure of 0.77@(0.92, 0.68), and provides improved handling of color and luminance variation, as well as strong segmentation performance for images with highly textured regions when compared to a number of previous methods. These results suggest that the proposed stochastic regional texture appearance model is better suited for handling the texture variations of natural scenes, leading to more accurate segmentations, particularly in situations characterized by complex textural characteristics.

Light outcoupling enhancement from top-emitting organic light-emitting diodes made on a nano-sized stochastic texture surface

An effective method for enhancing the light outcoupling efficiency from top-emitting organic light-emitting diodes (TEOLEDs) with a nano-sized stochastic texture surface (NSTS) is suggested. The broadly distributed pitch and the randomly sized of islands in the NSTS enable the photons that are otherwise trapped to be emitted over the broad emission wavelength range. The NSTS-embedded TEOLEDs have wide angular-dependent emission characteristics and an enhanced external quantum efficiency (EQE). Theoretical and full-wave optical calculations were performed to understand the mechanisms of the efficiency enhancement. Optimized TEOLEDs achieved a 32% EQE enhancement compared with the reference devices without the NSTS.

Image Sensor Noise Parameter Estimation by Variance Stabilization and Normality Assessment.

High-quality image denoising requires taking into account the dependence of the noise distribution on the original image. The parameters of this dependence are often unknown and we propose a new method to estimate them here. Using an optimization procedure, we find a variance-stabilizing transformation, which transforms the input image into an image with signal-independent noise. Principal component analysis of blocks of the transformed image allows estimation of the variance of the signal-independent noise so that the parameters of the original noise model can be computed. The image blocks for processing are selected in such a way that they have low stochastic texture strength but preserve the noise distribution. The algorithm does not require the original image to have homogeneous areas and can accurately process images with regular textures. It has high computational efficiency and smaller maximum estimation error compared with the state of the art. Our experiments have also shown that denoising with the noise parameters estimated by this method leads to the same results as denoising with the true noise parameters.

Single-Image Superresolution of Natural Stochastic Textures Based on Fractional Brownian Motion

Texture enhancement presents an ongoing challenge, in spite of the considerable progress made in recent years. Whereas most of the effort has been devoted so far to enhancement of regular textures, stochastic textures that are encountered in most natural images, still pose an outstanding problem. The purpose of enhancement of stochastic textures is to recover details, which were lost during the acquisition of the image. In this paper, a texture model, based on fractional Brownian motion (fBm), is proposed. The model is global and does not entail using image patches. The fBm is a self-similar stochastic process. Self-similarity is known to characterize a large class of natural textures. The fBm-based model is evaluated and a single-image regularized superresolution algorithm is derived. The proposed algorithm is useful for enhancement of a wide range of textures. Its performance is compared with single-image superresolution methods and its advantages are highlighted.

Classifying scaled and rotated textures using a region-matched algorithm

A novel method to correct texture variations resulting from scale magnification, narrowing caused by cropping into the original size, or spatial rotation is discussed. The variations usually occur in images captured by a camera using different focal lengths. A representative region-matched algorithm is developed to improve texture classification after magnification, narrowing, and spatial rotation. By using a minimum ellipse, a representative region-matched algorithm encloses a specific region extracted by the J-image segmentation algorithm. After translating the coordinates, the equation of an ellipse in the rotated texture can be formulated as that of an ellipse in the original texture. The rotated invariant property of ellipse provides an efficient method to identify the rotated texture. Additionally, the scale-variant representative region can be classified by adopting scale-invariant parameters. Moreover, a hybrid texture filter is developed. In the hybrid texture filter, the scheme of texture feature extraction includes the Gabor wavelet and the representative region-matched algorithm. Support vector machines are introduced as the classifier. The proposed hybrid texture filter performs excellently with respect to classifying both the stochastic and structural textures. Furthermore, experimental results demonstrate that the proposed algorithm outperforms conventional design algorithms.

Ellipse Invariant Algorithm for Texture Classification

The classification of texture images, especially those with spatial rotation and region shift, is a challenge and important problem in image analysis and classification. This paper proposes a novel algorithm design, an ellipse invariant algorithm, to improve the capability of texture classification for spatial rotation and region shift. The principle of an ellipse invariant algorithm is to use a minimum ellipse to enclose specific representative pixels extracted by the subtracting clustering method. After translating the coordinates, the ellipse in the rotated texture would be formulated as the ellipse in original texture. Also in this paper a hybrid texture filter is proposed. In the hybrid texture filter the scheme of texture feature extraction include Gabor wavelet, neighboring grey level dependence matrix and the ellipse invariant algorithm. Support vector machines SVMs are introduced as the classifier. The proposed hybrid texture filter can classify both the stochastic textures and structural textures. Experimental results reveal that this proposed algorithm outperforms existing design algorithms.