Understanding the influence of surface properties (roughness, grooves, discrete textures/dimples) on the performance of hydrodynamically lubricated contacts has been the aim of numerous studies. A ...variety of different numerical models have been employed by many researchers in order to find optimal texturing parameters (shape, size, distribution) for best performance enhancement in terms of load carrying capacity, film thickness, friction and wear. However, the large number of different modeling techniques and complexity in the patterns make finding the optimum texture a challenging task and have led to contrary conclusions. This article outlines the research effort on surface texturing worldwide, reviews the key findings and, in particular, provides a comparative summary of different modeling techniques for fluid flow, cavitation and micro-hydrodynamic effects.
•This article facilitates the application of textures for hydrodynamic contacts.•Functions of surface textures, texture design and modeling techniques are discussed.•Textures must be designed for a given application and range of operating conditions.•Robust models allow the evaluation of texture designs prior to being manufactured.•The three major challenges in numerical modeling are discussed.
Nonlocal texture similarity and local intensity smoothness are both essential for solving most image inpainting problems. In this paper, we propose a novel image inpainting algorithm that is capable ...of reproducing the underlying textural details using a nonlocal texture measure and also smoothing pixel intensity seamlessly in order to achieve natural-looking inpainted images. For matching texture, we propose a Gaussian-weighted nonlocal texture similarity measure to obtain multiple candidate patches for each target patch. To compute the pixel intensity, we apply the α-trimmed mean filter to the candidate patches to inpaint the target patch pixel-by-pixel. The proposed algorithm is compared with four current image inpainting algorithms under different scenarios, including object removal, texture synthesis, and error concealment. Experimental results show that the proposed algorithm outperforms the existing algorithms when inpainting large missing regions in images with texture and geometric structures.
•This paper proposes a novel global refined local binary pattern (GRLBP) by analyzing the nature of the distribution of pixel intensity in local neighborhoods.•GRLBP consists of two descriptors ...termed as magnitude refined local sign binary pattern (MRLBP_S) and center refined local magnitude binary pattern (CRLBP_M).•MRLBP_S distinguishes local neighborhoods with contrast differences by using global magnitude anchors to refine local sign patterns.•CRLBP_M identifies local neighborhoods with gray differences by employing global central gray anchors to refine local magnitude patterns.•RLBP has obvious advantages in classification performance, computational complexity, and feature dimension.
Local binary pattern (LBP) and its variants have been successfully applied in texture feature extraction. However, it is hard for most LBP-based methods to effectively describe and distinguish the local neighborhoods with similar structures (that is, the calculated feature patterns are identical) but different contrasts or grayscales. To alleviate such problems, we propose a novel global refined local binary pattern (GRLBP) by analyzing the nature of pixel intensity distribution in local neighborhoods. GRLBP consists of two descriptors called magnitude refined local sign binary pattern (MRLBP_S) and center refined local magnitude binary pattern (CRLBP_M). MRLBP_S distinguishes local neighborhoods with contrast differences by using global magnitude anchors to refine local sign patterns. And CRLBP_M identifies local neighborhoods with grayscale differences by employing global central grayscale anchors to refine local magnitude patterns. Finally, frequency histograms of MRLBP_S and CRLBP_M from each image are cascaded to generate the GRLBP. Extensive experimental results on seven benchmark texture databases: Outex, CUReT, KTH-TIPS, UMD, UIUC, KTH-T2b, and DTD demonstrate that the proposed GRLBP can represent the detailed information of texture images. Furthermore, compared with state-of-the-art LBP variants, GRLBP has competitive advantages in classification accuracy, feature dimension, and computational complexity, respectively.
Solid textures are essential for modeling virtual internal materials. Existing approaches either generate raster solid textures or only focus on vector representation. To facilitate efficient ...synthesis and intuitive editing of vector solid texture, we propose the novel solid texture representation, named
radial basis function (RBF) solid texture
. An RBF solid texture consists of a set of spatially distributed RBF instances. Each RBF instance encapsulates a 3D position, an RGB color and a signed distance field (SDF) value. Such a representation is resolution independent, compact in storage and capable of supporting efficient random access with an indexing uniform grid. We directly synthesize RBF solid texture from raster exemplar by minimizing an energy function, which encodes the position, color and SDF difference between output volumetric RBF instances and input example planar RBF instances. The minimization process iteratively updates output RBF instances with an EM algorithm. Our experiments show that our algorithm can produce RBF solid textures in high efficiency and compact storage for a variety of exemplars, including stochastic patterns or more structured patterns. Furthermore, RBF solid textures we proposed benefit intuitive editing for either region-based and RBF-based effects.
Classifying texture images, especially those with significant rotation, illumination, scale, and viewpoint changes, is a fundamental and challenging problem in computer vision. This paper proposes a ...simple yet effective image descriptor, called Locally Encoded TRansform feature hISTogram (LETRIST), for texture classification. LETRIST is a histogram representation that explicitly encodes the joint information within an image across feature and scale spaces. The proposed representation is training-free, low-dimensional, yet discriminative and robust for texture description. It consists of the following major steps. First, a set of transform features is constructed to characterize local texture structures and their correlation by applying linear and non-linear operators on the extremum responses of directional Gaussian derivative filters in scale space. Established on the basis of steerable filters, the constructed transform features are exactly rotationally invariant as well as computationally efficient. Second, the scalar quantization via binary or multi-level thresholding is adopted to quantize these transform features into texture codes. Two quantization schemes are designed, both of which are robust to image rotation and illumination changes. Third, the cross-scale joint coding is explored to aggregate the discrete texture codes into a compact histogram representation, i.e., LETRIST. Experimental results on the Outex, CUReT, KTH-TIPS, and UIUC texture data sets show that LETRIST consistently produces better or comparable classification results than the state-of-the-art approaches. Impressively, recognition rates of 100.00% and 99.00% have been achieved on the Outex and KTH-TIPS data sets, respectively. In addition, the noise robustness is evaluated on the Outex and CUReT data sets. The source code is publicly available at https://github.com/stc-cqupt/letrist .
Aim
Cancer treatments with radiation present a challenging physical toll for patients, which can be justified by the potential reduction in cancerous tissue with treatment. However, there remain ...patients for whom treatments do not yield desired outcomes. Radiomics involves using biomedical images to determine imaging features which, when used in tandem with retrospective treatment outcomes, can train machine learning (ML) classifiers to create predictive models. In this study we investigated whether pre-treatment imaging features from index lymph node (LN) quantitative ultrasound (QUS) scans parametric maps of head & neck (H&N) cancer patients can provide predictive information about treatment outcomes.
Methods
72 H&N cancer patients with bulky metastatic LN involvement were recruited for study. Involved bulky neck nodes were scanned with ultrasound prior to the start of treatment for each patient. QUS parametric maps and related radiomics texture-based features were determined and used to train two ML classifiers (support vector machines (SVM) and
k
-nearest neighbour (
k-NN
)) for predictive modeling using retrospectively labelled binary treatment outcomes, as determined clinically 3-months after completion of treatment. Additionally, novel higher-order texture-of-texture (TOT) features were incorporated and evaluated in regards to improved predictive model performance.
Results
It was found that a 7-feature multivariable model of QUS texture features using a support vector machine (SVM) classifier demonstrated 81% sensitivity, 76% specificity, 79% accuracy, 86% precision and an area under the curve (AUC) of 0.82 in separating responding from non-responding patients. All performance metrics improved after implementation of TOT features to 85% sensitivity, 80% specificity, 83% accuracy, 89% precision and AUC of 0.85. Similar trends were found with
k
-NN classifier.
Conclusion
Binary H&N cancer treatment outcomes can be predicted with QUS texture features acquired from index LNs. Prediction efficacy improved by implementing TOT features following methodology outlined in this work.
Pavement micro- and macro-texture have significant effects on roadway friction and driving safety. The influence of traffic polish on pavement texture has been investigated in many laboratory ...studies. This paper conducts field evaluation of pavement micro- and macro-texture under actual traffic polishing using three-dimensional (3D) areal parameters. A portable high-resolution 3D laser scanner measured pavement texture from a field site in 2018, 2019, and 2020. Then, the 3D texture data was decomposed to micro- and macro-texture using Fourier transform and Butterworth filter methods. Twenty 3D areal parameters from five categories, including height, spatial, hybrid, function, and feature parameters, were calculated to characterize pavement micro- and macro-texture. The results demonstrate that the 3D areal parameters provide an alternative to comprehensively characterize the evolution of pavement texture under traffic polish from different aspects.
Image texture description and analysis technology are the basis of many practical applications in pattern recognition. This paper presents a novel and simple, yet powerful method, namely multiple ...channels local binary pattern (MCLBP), which is the natural extension and development of local binary pattern (LBP) algorithm for color texture representation and classification. MCLBP combines single-channel texture characteristics with multi-channel color information, which reflects the correlations and dependency among different channels. Furthermore, we decompose local color differences into color-difference signs and color-difference magnitudes and MCLBP is extended to MCLBP+M. Then, the resulted image descriptor is a histogram representation, which fuses rich features including color difference sign and color difference magnitude. Comprehensive experiments conducted on five benchmark databases, including Outex, KTH-TIPS, CUReT, STex and KTH-TIPS2-b clearly demonstrate that our proposed method outperforms most of the existed color texture features in terms of classification accuracy. Particularly, our method achieves the best classification performance in CUReT and STex databases.
•A multi-channels LBP encodes inner- and inter-channel features of color images.•MCLBP extracts local features from three channels at once.•MCLBP+M characterizes both color difference signs and color difference magnitudes.•Comprehensive experiments are conducted on five benchmark datasets.
Texture similarity plays important roles in texture analysis and material recognition. However, perceptually-consistent fine-grained texture similarity prediction is still challenging. The ...discrepancy between the texture similarity data obtained using algorithms and human visual perception has been demonstrated. This dilemma is normally attributed to the texture representation and similarity metric utilised by the algorithms, which are inconsistent with human perception. To address this challenge, we introduce a Perception-Aware Texture Similarity Prediction Network (PATSP-Net). This network comprises a Bilinear Lateral Attention Transformer network (BiLAViT) and a novel loss function, namely, RSLoss. The BiLAViT contains a Siamese Feature Extraction Subnetwork (SFEN) and a Metric Learning Subnetwork (MLN), designed on top of the mechanisms of human perception. On the other hand, the RSLoss measures both the ranking and the scaling differences. To our knowledge, either the BiLAViT or the RSLoss has not been explored for texture similarity tasks. The PATSP-Net performs better than, or at least comparably to, its counterparts on three data sets for different fine-grained texture similarity prediction tasks. We believe that this promising result should be due to the joint utilization of the BiLAViT and RSLoss, which is able to learn the perception-aware texture representation and similarity metric.
We present a systematic approach for training and testing structural texture similarity metrics (STSIMs) so that they can be used to exploit texture redundancy for structurally lossless image ...compression. The training and testing is based on a set of image distortions that reflect the characteristics of the perturbations present in natural texture images. We conduct empirical studies to determine the perceived similarity scale across all pairs of original and distorted textures. We then introduce a data-driven approach for training the Mahalanobis formulation of STSIM based on the resulting annotated texture pairs. Experimental results demonstrate that training results in significant improvements in metric performance. We also show that the performance of the trained STSIM metrics is competitive with state of the art metrics based on convolutional neural networks, at substantially lower computational cost.
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