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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]

The Stockwell Transform has the potential to perform multi-resolution texture analysis in magnetic resonance imaging (MRI). However, it is computationally intensive and memory demanding. The polar Stockwell Transform (PST) is rotation-invariant and relatively memory efficient, but still computationally demanding. The new Discrete Orthogonal Stockwell Transform (DOST) appears to have addressed both the computation and storage challenges; however, its utility in localized texture analysis remains unclear. Our goal was to investigate the theory and texture analysis ability of the DOST versus PST using both synthetic and MR images, and explore the relative importance of the associated texture features using a simple classification example based on clinical brain MRI of six multiple sclerosis patients. MRI texture analysis focused on FLAIR images, and the classification used a machine learning algorithm, random forest, that differentiated regions of interest (ROIs) into 2 classes: white matter lesions, and the contralateral normal-appearing white matter (control). Our results showed that the PST features had a greater ability in detecting subtle changes in image structure than the DOST and polar-index DOST (PDOST). Quantitatively, based on 187 lesion and 187 control ROIs, both the PST and the rotation-invariant radial PST performed better in the classification than the DOST and PDOST, where the latter were no better than guessing (p = 0.65 and 0.98). Further analysis using a hierarchical random forest showed that combining MRI signal intensity with the PST or DOST predictions increased the classification performance, with the accuracy, sensitivity, and specificity all improved to >85% in the tests. Collectively, the DOST is less competitive than the PST in localized image texture analysis. The PST features may help with texture-based lesion classification in MS based on clinical brain MRI scans following further verification. [ABSTRACT FROM AUTHOR]

Highlights • An accurate method for diffraction imaging simulation has been validated and applied to generate unpolarized diffraction images of 1965 single particles, composed of single and double spheres, cylinders and ellipsoids, under coherent illumination. • A Gaussian mixture model (GMM) based clustering algorithm has been developed to classify these particles into 3 types of spheres, cylinders and ellipsoids with the texture parameters of the calculated diffraction image data. • GMM classifiers with selected diffraction image texture parameters and integrated forward scattered light intensity have been found to classify particles of same refractive index with accuracies ranging from 82.6% to 97.2%. • The effective texture parameters of diffraction images extracted with a gray-level co-occurrence matrix were identified as correlation, sum entropy, entropy, dissimilarity and inverse difference moment. Abstract Spatial distributions of light scattered by single particles correlate closely with their morphologies in terms of refractive index (RI) distribution. Diffraction imaging of scattered light under coherent excitation presents a unique approach to acquire and extract feature parameters for particle classification. A validated method has been applied in this study to accurately simulate diffraction imaging of light scattered by homogeneous particles and obtain calculated diffraction image (DI) data. The feature parameters of DI data have been extracted by the gray-level co-occurrence matrix (GLCM) algorithm. We have developed an unsupervised machine learning algorithm based on Gaussian mixture model (GMM) to classify 1965 particles made of single and double spheres, cylinders and ellipsoids with varied RI values in parameter space. It has been shown that selected GLCM parameters combined with integrated forward scatter intensity can provide effective markers for accurate and morphology based classification. For 1791 particles of the same RI, the mean accuracy values of classifying particles into 3 particle types range from 82.6% to 97.2%. These results demonstrate the strong potential of diffraction imaging method for rapid analysis and classification of nonspherical and homogeneous particles by the GMM classifiers that is very challenging in comparison to distinguishing biological cell types. [ABSTRACT FROM AUTHOR]

Plant phenology is one of the most reliable indicators of species responses to global climate change, motivating the development of new technologies for phenological monitoring. Digital cameras or near remote systems have been efficiently applied as multi-channel imaging sensors, where leaf color information is extracted from the RGB (Red, Green, and Blue) color channels, and the changes in green levels are used to infer leafing patterns of plant species. In this scenario, texture information is a great ally for image analysis that has been little used in phenology studies. We monitored leaf-changing patterns of Cerrado savanna vegetation by taking daily digital images. We extract RGB channels from the digital images and correlate them with phenological changes. Additionally, we benefit from the inclusion of textural metrics for quantifying spatial heterogeneity. Our first goals are: (1) to test if color change information is able to characterize the phenological pattern of a group of species; (2) to test if the temporal variation in image texture is useful to distinguish plant species; and (3) to test if individuals from the same species may be automatically identified using digital images. In this paper, we present a machine learning approach based on multiscale classifiers to detect phenological patterns in the digital images. Our results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; (2) different plant species present a different behavior with respect to the color change information; and (3) texture variation along temporal images is promising information for capturing phenological patterns. Based on those results, we suggest that individuals from the same species and functional group might be identified using digital images, and introduce a new tool to help phenology experts in the identification of new individuals from the same species in the image and their location on the ground. [ABSTRACT FROM AUTHOR]