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The microtexture of the asphalt pavement, which is critical for the skid resistance, is dictated by the microtexture of the coarse aggregates used in the surface courses. The existing standard test methods can only capture the combined measure of coarse aggregate surface texture which can be influenced by other aggregate characteristics such as shape and angularity. Consequently, the surface texture of aggregates cannot be quantified explicitly using these tests, and therefore image analysis methods that can extract textural features are gaining more significance. In the present study, various image texture analysis methods are evaluated to identify a reliable texture indicator. Methods include traditional statistical approaches such as histogram method, Grey Level Co-occurrence Matrix (GLCM) methods, and transform-based wavelet texture analysis. The image analysis was carried out on the grey-scale scanning electron microscope (SEM) images before and after accelerated polishing. To compare the image texture parameters, the average surface roughness (Sa) measured from aggregate surface profile data obtained from a 3D Optical Surface Profilometer (3D OSP) was considered as a direct measure of surface texture. A detailed correlation analysis with Sa indicates that the surface texture index (STI) from the wavelet analysis quantifies the microtexture more accurately compared to statistical methods. [ABSTRACT FROM AUTHOR]

Equine odontoclastic tooth resorption and hypercementosis (EOTRH) is one of the horses' dental diseases, mainly affecting the incisor teeth. An increase in the incidence of aged horses and a painful progressive course of the disease create the need for improved early diagnosis. Besides clinical findings, EOTRH recognition is based on the typical radiographic findings, including levels of dental resorption and hypercementosis. This study aimed to introduce digital processing methods to equine dental radiographic images and identify texture features changing with disease progression. The radiographs of maxillary incisor teeth from 80 horses were obtained. Each incisor was annotated by separate masks and clinically classified as 0, 1, 2, or 3 EOTRH degrees. Images were filtered by Mean, Median, Normalize, Bilateral, Binomial, CurvatureFlow, LaplacianSharpening, DiscreteGaussian, and SmoothingRecursiveGaussian filters independently, and 93 features of image texture were extracted using First Order Statistics (FOS), Gray Level Co-occurrence Matrix (GLCM), Neighbouring Gray Tone Difference Matrix (NGTDM), Gray Level Dependence Matrix (GLDM), Gray Level Run Length Matrix (GLRLM), and Gray Level Size Zone Matrix (GLSZM) approaches. The most informative processing was selected. GLCM and GLRLM return the most favorable features for the quantitative evaluation of radiographic signs of the EOTRH syndrome, which may be supported by filtering by filters improving the edge delimitation. [ABSTRACT FROM AUTHOR]

Land cover mapping is an important part of resource management, planning, and economic predictions. Improvements in remote sensing, machine learning, image processing, and object based image analysis (OBIA) has made the process of identifying land cover types increasingly faster and reliable but these advances have not been able to utilize all of the information encompassed within ultra-high (sub-meter) resolution imagery. There have been few known attempts to try and maximize this detailed information in high resolution imagery using advanced textural components. Hierarchical land classes are also rarely used as an attribute within the machine learning step of object-based image analysis. In this study we try to circumnavigate the inherent problems associated with high resolution imagery by combining well researched data transformations that aid the OBIA process with a seldom used texture transformation in Geographic Object Based Image Analyses (GEOBIA/OBIA) known as the Gabor Transform and the hierarchal organization of landscapes. We will observe the difference made in segmentation and classification accuracy of a random forest classifier when we fuse a Gabor transformed image to a Normalized Difference Vegetation Index (NDVI), high resolution multi-spectral imagery (RGB and NIR) and Light Detection and Ranging (LiDAR) derived canopy height model (CHM) within a riparian area in Southeast Iowa, United States. Additionally, we will observe the effects on classification accuracy when adding multi-scale land cover data to objects. Both, the addition of hierarchical information and Gabor textural information, could aid the GEOBIA process in delineating and classifying the same objects that human experts would delineate within this riparian landscape. [ABSTRACT FROM AUTHOR]

In order to improve the retrieval ability of super-resolution multi-space block images, a texture retrieval method of block images based on depth hash is proposed. A texture feature analysis model of super-resolution multi-dimensional partitioned images is constructed, which combines texture spatial structure mapping method to realize depth information fusion of partitioned images, adopts edge feature detection and texture sparse feature clustering to realize texture hierarchical structure feature decomposition of super-resolution multi-dimensional partitioned images, and adopts deep image parameter analysis method to construct pixel structure recombination model of multi-dimensional partitioned images. Multi-dimensional texture parameter structure analysis and information clustering are realized for the collected partitioned images in multi-dimensional space. According to the information clustering results, the texture retrieval and extraction of partitioned images are realized by using the deep hash fusion algorithm, and the information detection and feature recognition capabilities of partitioned images in multi-dimensional space are improved. Simulation results show that this method has higher precision and better feature resolution in texture retrieval of partitioned images in multidimensional space, which improves the texture retrieval and recognition ability of partitioned images. [ABSTRACT FROM AUTHOR]