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• Propose a computer vision method for detecting asphalt pavement segregation. • Employ image texture analysis for characterizing pavement surface condition. • Extreme gradient boosting machine and deep neural network are used for classification. • Attractive repulsive center-symmetric local binary pattern is used for texture computation. • XGBoost has achieved the best detection accuracy with accuracy rate = 0.95. Aggregate segregation is a major form of defect that accelerates the pavement deterioration rate. Therefore, asphalt pavement segregation needs to be detected accurately and early during the quality survey process. This study proposes and verifies a computer vision based method for automatic identification of aggregate segregation. The new method includes Extreme Gradient Boosting Machine integrated with Attractive Repulsive Center-Symmetric Local Binary Pattern (ARCSLBP-XGBoost) and Deep Convolutional Neural Network (DCNN). Experimental results obtained from a repetitive random data sampling process with 20 runs show that the ARCSLBP-XGBoost is a capable approach for detecting asphalt pavement segregation with outstanding performance measurement metrics (classification accuracy rate = 0.95, precision = 0.93, recall = 0.98, and F1 score = 0.95). [ABSTRACT FROM AUTHOR]

Recent asteroid missions have revealed that many sub-kilometer asteroids are rubble piles. Large parts of their surfaces are covered with boulders larger than tens of centimeters. An evaluation of the abundance and size distribution of boulders provides clues to understand surface processes on boulder-covered asteroids. Here we report a new method that automatically measures the abundance of small boulders (sub-pixel to a few pixels), whose boundaries cannot be recognized with visual inspection, by quantifying the surface radiance variation that occurs during the spinning of the asteroid. After validating our approach with previous boulder counting data, we apply this method to images of the asteroids Ryugu and Itokawa, which were visited by JAXA's Hayabusa and Hayabusa2, and obtain a global distribution of the boulders larger than 0.75–3 ​m, which corresponds to 1.5–6 pixels. We find that the boulder number density of this size range is smaller (1) on the western bulge than on the eastern hemisphere and (2) on the equatorial ridge than on the higher latitudes, both of which exceed the number density of boulders ​> ​5 ​m by an order of magnitude. The boulder size distribution at 1.25–20 ​m shows that the boulders smaller than 1 ​m are more abundant at the equator than at mid-latitudes, while those larger than 1 ​m in diameter are more abundant at mid-latitudes than at the equator. This contrast suggests size-dependent migration of boulders in the latitudinal direction. We also find that the typical boulder size (the size reaching the cumulative areal coverage of 50%) is 1.9 ​m at the equatorial region (10°S-10°N) while it is 2.6 ​m at mid-latitudes (40°S-50°S, 40°N-50°N). The typical boulder size is also smaller in the western bulge (2.0–2.2 ​m). We construct global maps of the power-law index of the size frequency distribution of boulders and find minor variations over the entire surface of Ryugu (−2.53 ​± ​0.03) for boulders larger than 1.25 ​m. This small variation suggests homogeneous size sorting processes on Ryugu. Surface roughness does not show a significant correlation with the v-band albedo but shows a high anti-correlation (R ​= ​−0.73) with the current geological slope on the eastern hemisphere. Our method is useful enhancement of smooth area detection and boulder distribution characterization that will be applicable to other planetary explorations in the future, including those of Phobos and other asteroids. • Automatic analysis of pixel-scale boulders based on pixel radiance variation as asteroid spins. • Distribution of sub-meter boulders on Ryugu indistinguishable from visual counting. • Method detects the east-west dichotomy in the distribution of small boulders (several meters in diameter). • Uniform power-law index of boulder size frequency distribution on Ryugu suggests homogeneous size sorting. • Boulder distribution is correlated with current geologic slope. [ABSTRACT FROM AUTHOR]

Superficial fibromatosis exhibits variable MR signal intensity due to collagenous and fibroproliferative components. Quantifying this signal heterogeneity using image texture analysis and T2-mapping could have prognostic and therapeutic implications. This IRB-approved retrospective study included 13 patients with superficial fibromatosis, managed by observation, electron beam radiotherapy (EBT), or pentoxifylline/vitamin E. Two-dimensional regions of interest (ROIs) were drawn on proton-density or T2-weighted MRI for radiomics feature analysis, and corresponding T2-maps. Comparisons were made between baseline and follow-up T2 relaxation times and radiomics features: Shannon's entropy, kurtosis, skewness, mean of positive pixels (MPP), and uniformity of distribution of positive gray-level pixel values (UPP). There were 19 nodules in 13 subjects. Mean patient age was 60 years; 62% (8/13) were female; mean follow-up was 9.7 months. Nodule diameter at baseline averaged 18.2 mm (std dev 16.2 mm) and decreased almost 10% to 16.6 mm (p = 0.1, paired t -test). Normalized T2 signal intensity decreased 23% from 0.71 to 0.55 (p = 0.03, paired t -test). T2 relaxation time decreased 16% from 46.5 to 39.1 ms (p < 0.001, paired t -test). Among radiomics features, skewness increased to 0.71 from 0.41 (p = 0.03, paired t -test), and entropy decreased from 8.37 to 8.03 (p = 0.05, paired t-test); differences in other radiomics features were not significant. Radiomics analysis and T2-mapping of superficial fibromatosis is feasible; robust decreases in absolute T2 relaxation time, and changes in image textural features (increased skewness and decreased entropy) offer novel imaging biomarkers of nodule collagenization and maturation. • T2 mapping, image texture analysis show longitudinal changes in superficial fibromatoses. • Treated nodules show decreased T2 relaxation times, reflecting increased collagenization. • Increased skewness and decreased entropy indicate homogeneity accompanies response. [ABSTRACT FROM AUTHOR]

• Multi-block data analysis was used for beef quality assessment. • Different parts of beef were evaluated by HSI, 1H NMR and quality parameters datasets. • ComDim shows Clustering results, contribution of data blocks and their relationships. Several factors affect the quality of beef. In the field of chemometrics, multi-block data analysis methods are useful for examining multiple sources of information from a sample. This study focuses on the application of ComDim, a multi-block data analysis method, to evaluate beef from different parts of hyperspectral spectrum and image texture information, 1H NMR fingerprints, quality parameters and electronic nose. Compared to principal component analysis (PCA) methods based on low-level data fusion, ComDim is more efficient and powerful, because it reveals the relationships between the methods and techniques studied, as well as the variability of beef quality across multiple metrics. The quality and metabolite composition of beef tenderloin and hindquarters were differentiated, with low L* value and high shear tenderloin distinguished from hindquarters with opposite characteristics. The proposed strategy demonstrates that ComDim approach can be used to characterize samples when different techniques describe the same set of samples. [ABSTRACT FROM AUTHOR]

Multifractal analysis was applied to evidence spatial properties, in particular scaling behaviour, of Sentinel-2 surface reflectances and optical indexes acquired over the Southwest region of France. Considering the significant impact of clouds on the spatial properties of optical products, no study to our knowledge has focused on the multifractal analysis of a series of images. Images with minimal cloud cover were identified for the whole year 2016 based on the MACCS-ATCOR Joint Algorithm (MAJA) cloud and shadow detection process. This allowed us to access the dynamics of the scaling properties of surface reflectances, contributing to demonstrate the potential of multifractals for spatial characterization. Different scaling regimes, separated by significant scale breaks, were evident in these data. Through simulations from the Universal Multifractal model, we could explain the scaling properties and especially the position of scale breaks observed in the images studied by two types of phenomena. First, a transition scale located between hundreds of meters and one kilometre was detected on power spectra, structure functions and statistical moments. This specific scale was related to the average size of the agricultural plots of the studied region, revealing the direct link between the scaling behaviour observed and the physical properties of the surface. Second, an instrumental phenomenon was detected on the power spectra, resulting in a systematic transition scale observed near the resolution of the sensor and revealing the impact of the acquisition system on the high frequencies of optical images. In this research work, theoretical and/or empirical validation arguments were provided as to the origin of the scales detected, contributing to demonstrate the value of multifractals to extract image texture from surface reflectance products. • Particular scale breaks were detected on Sentinel-2/MAJA products. • Time variations in scaling regimes were noticed in series of surface reflectances. • The large-scale break was related to the landscape spatial structure. • The fine-scale break was explained by the effect of the optical acquisition system. [ABSTRACT FROM AUTHOR]

• Surface roughness is a complex multiscale characteristic and multiple roughness indexes can be defined. • The simplified geostatistical-based algorithm presented describes key aspects of short-range roughness. • Roughness anisotropy is a key morphological characteristic. Surface roughness, i.e. local variations in elevation which indicate less and less local smoothness, is an important concept for the analysis of solid earth/planetary surfaces and of the related surface processes. Surface roughness is a general concept covering multiple aspects of the surface spatial variability structure, that can be characterized at different scales and with different metrics. Accordingly, it is not surprising the wide range of approaches and algorithms adopted for the analysis of surface roughness and the lack of an accepted standard for its evaluation. In this context, geostatistical-based roughness indexes are a valid solution, providing a good balance between flexibility of algorithms and interpretability of the results. However, despite the long record of applications and the well-known and robust theoretical framework, geostatistical-based surface roughness tools have still not gained momentum in the context of geomorphometric analysis. Many geomorphometric studies concerning with the evaluation of roughness indexes are based on popular approaches, such as the ones based on measures of dispersion of normal vectors to surface. Unfortunately, these indexes present some drawbacks in the capability to represent specific aspects of surface roughness and from the perspective of their interpretability. Accordingly, in this paper some of the key aspects and advantages of geostatistical-based approaches for roughness analysis are highlighted, including the relevance of roughness anisotropy. Then, a new approach tailored to the analysis of short-range roughness, requiring a minimum intervention by the user, is introduced discussing the advantages compared to the vector dispersion-based roughness. The new approach is implemented as open-source code both in R language using the functions of the "Terra" package as well as in Python for Esri ArcMap GIS. The methodological considerations and algorithms presented can be applied also in the broader context of image analysis. [ABSTRACT FROM AUTHOR]