3D texture parameters for wear scars after severe regime

Purpose This paper aims to present an analysis of several 3 D texture parameters for the entire wear scars obtained in severe regime, on a four-ball tester. The aim of this analysis is to correlate the tribological parameter as wear scar diameter to texture parameters. Design/methodology/approach Tested lubricants were rapeseed oil, rapeseed oil additivated with 1% Wt nano TiO2 and rapeseed oil additivated with 1%Wt nano ZnO. The severe regime was applied for 1400 rpm and for loads increasing in steps of 50 N, from 500 to 900 N. Several analyzed roughness parameters (height parameters and functional ones) could be related to the evolution of a wear parameter, the wear scar diameter. Comparing the values for neat rapeseed oil and additivated variants, the texture parameters allow for evaluating if the additives protect or not the worn surfaces. Findings Measurements pointed out two groups of roughness parameters: one that has an evolution depending on wear scar diameter (WSD) and load (Sa, St, functional parameters) and one including Ssk that has shown no dependence on load and WSD. Also, the functional parameters Spk and Svk follow in a similar manner the wear parameter, WSD, but Sk is the least dependent on load. For the highest load, amplitude parameters such as Sa and St are following the tendency of WSD. Each lubricant has its particular correlation between wear parameters and texture quality, expressed by the help of a set of roughness parameters. Research limitations/implications Such studies help tribologists to rank lubricants based on a combined analysis with wear parameters and texture parameters. Practical implications The results allow for evaluating new formulated lubricants. Originality/value The study on the quality on worn surfaces introduces the original idea of analyzing the entire wear scar surface (approximated by an ellipse with the axes as those experimentally measured) by the help of a set of 3 D roughness parameters.

3D Texture Analysis of MRI Relaxation Time Maps for Assessment of Repair Cartilage with Treatment of Allogeneic Human Adipose-Derived Mesenchymal Progenitor Cells

Background: We used textural analysis matrix to examine the spatial distribution of pixel values and detect the compositional variation of repair cartilage with treatment of allogeneic human adipose-derived mesenchymal progenitor cells (haMPCs). Methods: Eighteen patients were divided randomly into three groups with intra-articular injections of haMPCs: the low-dose (1.0×107 cells), mid-dose (2.0×107), and high-dose (5.0×107) group with six patients each. 3D texture analyses based on gray level run-length matrix (GLRLM) of the segmented ROIs on MRI relaxation time maps including T1rho, T2, T2* and R2*. Five GLRLM parameters were analyzed, including run length non-uniformity (RLNonUni), grey level non-uniformity (GLevNonU), long run emphasis (LngREmph), short run emphasis (ShrtREmp) and fraction of image in runs (Fraction). We used the difference before and after treatment (D values) as the object to avoid errors caused by individual differences. Two-tailed Pearson linear correlation analysis was used to investigate correlations between texture parameters and the WOMAC scores. Results: The heterogeneity of spatial distribution of MRI relaxation time mapping pixels from three groups was decreased to varying degrees at 48 weeks after intra-articular injection of haMPCs. Spatial distribution of cartilage relaxation time maps pixels were uneven and layered, especially in T2 maps. Compared with base time, there were significant differences among three dose groups in GLRLM features for T1rho map including RLNonUni, GLevNonU, LngREmph, for T2 map including LngREmph, GLevNonU, ShrtREmp, for T2* map including RLNonUni, GLevNonU, and for R2* map including RLNonUni, GLevNonU. WOMAC pain scores were associated with RLNonUni of T1rho map, GLevNonU of T2 map, LngREmph of T2* map, LngREmph of R2* map and Fraction of T1rho map, whereas no significant correlations in other measurements.Conclusions: MRI texture analysis of cartilage may allow detection of the compositional variation of repair cartilage with treatment of allogeneic haMPCs. This has potential applications in understanding mechanism of stem cells repairing cartilage and assessing response to treatment.Trial registration: Clinicaltrials, NCT02641860. Registered 3 December 2015.https://www.clinicaltrials.gov/ct2/show/NCT02641860

Joint 3D facial shape reconstruction and texture completion from a single image

Recent years have witnessed significant progress in image-based 3D face reconstruction using deep convolutional neural networks. However, current reconstruction methods often perform improperly in self-occluded regions and can lead to inaccurate correspondences between a 2D input image and a 3D face template, hindering use in real applications. To address these problems, we propose a deep shape reconstruction and texture completion network, SRTC-Net, which jointly reconstructs 3D facial geometry and completes texture with correspondences from a single input face image. In SRTC-Net, we leverage the geometric cues from completed 3D texture to reconstruct detailed structures of 3D shapes. The SRTC-Net pipeline has three stages. The first introduces a correspondence network to identify pixel-wise correspondence between the input 2D image and a 3D template model, and transfers the input 2D image to a U-V texture map. Then we complete the invisible and occluded areas in the U-V texture map using an inpainting network. To get the 3D facial geometries, we predict coarse shape (U-V position maps) from the segmented face from the correspondence network using a shape network, and then refine the 3D coarse shape by regressing the U-V displacement map from the completed U-V texture map in a pixel-to-pixel way. We examine our methods on 3D reconstruction tasks as well as face frontalization and pose invariant face recognition tasks, using both in-the-lab datasets (MICC, MultiPIE) and in-the-wild datasets (CFP). The qualitative and quantitative results demonstrate the effectiveness of our methods on inferring 3D facial geometry and complete texture; they outperform or are comparable to the state-of-the-art.

Study of Pavement Micro- and Macro-Texture Evolution Due to Traffic Polishing Using 3D Areal Parameters

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.

Pavement Friction Prediction Using 3D Texture Parameters

This article deals with the possibility of predicting skid resistance based on non-contact scanning of the road surface. The study is based on comparing pavement texture parameters with coefficients of friction measured on a wide variety of road surfaces, while other test conditions were the same and constant. The measurements of the coefficient of friction were performed using a pendulum tester. The pavement texture was measured using a static road scanner, and 85 different 3D texture parameters were calculated. The study shows that the determination of the friction using only single texture parameters is not sufficient. Based on this statement, the next part of the research analyzed the influence of the mutual combination of surface texture parameters. A linear regression model was chosen to determine the friction coefficient prediction formula based on the combination of texture parameters. Statistically, the most significant parameters in the prediction model proved to be the valley material portion, characterizing the microtexture, and the arithmetic mean curvature, characterizing the pavement macrotexture. The obtained regression model proved to be statistically significant with R2 = 0.81 for Pendulum Test Value prediction.