Quantitative petrography: approaches and applications

Quantitative petrography is a scientific and industrial direction of geology, which made huge progress due to developments and inventions in information technology and optics in the last decade. This article is introducing the modern and scientific directions of quantitative petrography and describes their current state of art as well as methodical approaches and their application. The research objects of quantitative macropetrography are hand specimens, borehole cores and polished tiles, and of micropetrography are thin and polished sections of rocks samples, splitted rock surfaces and immersion preparations. The goal of the research is to develop and present new methodological approaches of digital microscopy for the analysis of ores, rocks and minerals, as well as to investigate the morphological image analysis capabilities for the transforming from the classical description methods to quantitative petrography.

A Methodology for the Automated Delineation of Crop Tree Crowns from UAV-Based Aerial Imagery by Means of Morphological Image Analysis

The popularisation of aerial remote sensing using unmanned aerial vehicles (UAV), has boosted the capacities of agronomists and researchers to offer farmers valuable data regarding the status of their crops. This paper describes a methodology for the automated detection and individual delineation of tree crowns in aerial representations of crop fields by means of image processing and analysis techniques, providing accurate information about plant population and canopy coverage in intensive-farming orchards with a row-based plant arrangement. To that end, after pre-processing initial aerial captures by means of photogrammetry and morphological image analysis, a resulting binary representation of the land plot surveyed is treated at connected component-level in order to separate overlapping tree crown projections. Then, those components are morphologically transformed into a set of seeds with which tree crowns are finally delineated, establishing the boundaries between them when they appear overlapped. This solution was tested on images from three different orchards, achieving semantic segmentations in which more than 94% of tree canopy-belonging pixels were correctly classified, and more than 98% of trees were successfully detected when assessing the methodology capacities for estimating the overall plant population. According to these results, the methodology represents a promising tool for automating the inventorying of plants and estimating individual tree-canopy coverage in intensive tree-based orchards.

Contour Fitting of Fused Filaments Cross-Section Images by Lemniscates of Booth: Application to Viscous Sintering Kinetics Modeling

A method for image analysis was implemented to determine the edge pixels of two biopolymer-based thermoplastic filaments during their hot melt isothermal sintering at 120 °C. Successive inverted ellipses are adjusted to the contour of the sintered filaments and lead to the identification of the parameters of the corresponding lemniscates of Booth. The different steps of the morphological image analysis are detailed, from 8-bit coded acquired images (1 frame/s), to the final fitting of the optimized mathematical functions describing the evolution of the filaments envelope. The complete sequence is composed of an initial pure viscous sintering step during the first minute, followed by viscoelastic swelling combined with melt spreading for a longer time, and then the stabilization of the sintered filaments shape for over 2 min at high temperatures. Using a master curve obtained from Hopper’s abacus, the characteristic viscous sintering time is assessed at tvs = 78 s, confirming the one previously found based on the measurement of the bonding neck length alone. Then, the full description of the evolution of the thermoplastic filaments envelope is assessable by image analysis during sintering trials as a result of its digital modeling as successive lemniscates of Booth, reflecting geometry changes in the molten state.

MORPHOLOGICAL IMAGE ANALYSIS BASED ON ATTRIBUTE AND RELATIONAL REPRESENTATIONS OF MOSAIC IMAGE MODELS

A unified scheme for morphological analysis based on attribute and relational representations of mosaic image models is proposed. We consider 4 main types of model representation: functional-attribute (2D feature map), functional-relational (4D relational map), structure-resource-attribute (an area list with resources and attributes), and structure-resource-relational (a graph, which nodes correspond to regions and edges – to relations and both having resource attributes). In this case, the forms of representation of the model are equivalent to each other, in the sense that they contain the same information, there is a one-to-one correspondence between them, and the formulas for the transition from one representation to another can be written out explicitly. In this scheme, the construction of specific morphological operator for some complete image model presumes the separation of this model into two parts: the guiding (modifying) part, which determines the transformation algorithm, and the guided (modifiable) part to be transformed. These two parts of the model can intersect, therefore cannot be called “variable” and “constant” components. As a basic sample, we consider the halftone Pyt’ev morphology. We explore the specifics of different-sort models, introduce the mutual models and propose different tools for creation of model-based morphological operators. Further, various other morphological systems can be described and explored using the proposed generalized approach.

IMAGE AND SHAPE COMPARISON VIA MORPHOLOGICAL CORRELATION

A lot of image matching applications require image comparison to be invariant relative to intensity values variations. The Pyt’ev theory for Morphological Image Analysis (MIA) was developed based on image-to-shape matching with mosaic shape models. Within the framework of this theory, the problem of shape-to-shape comparison was previously considered too. The most sophisticated and weakest part of MIA theory is the comparison of mosaic shapes with some arbitrary restrictions described by graphs or relations. In this paper we consider the possible options for comparing images and shapes using morphological projection and morphological correlation. Our contribution is a new scheme of morphological shape-to-image projection and, correspondingly, the new morphological correlation coefficient (MCC) for shape-to-image correlation with restricted mosaic models. We also refine the expressions for shape-to-shape comparison.

Multiscale Modeling of Asphalt Concrete and Validation through Instrumented Pavement Section

In this study, macroscale responses of asphalt concrete (AC) are predicted from the responses of its corresponding microscale representative volume element (RVE) within a finite element framework using quasi-static and dynamic analyses. Nanoindentation test was performed on the mastic and aggregate phase of an AC sample to determine the viscoelastic and elastic properties of RVE elements. Aggregate-mastic proportions in the RVE were obtained from the morphological image analysis. Macroscale model responses were compared with the AC pavement responses obtained from an instrumented pavement section subjected to falling weight deflectometer loading and a class 9 vehicle. Model responses are very close to the actual responses. The multiscale analyses show that tensile strain in microscale RVE is 5–10 times higher than that in a macroscale element. Furthermore, multiscale analyses also show that variations in the microscale RVE, such as the reduction in the aggregate-mastic ratio or increment in the voids, can increase the maximum tensile strain at the bottom of the AC in macroscale model by around 25%.

Machine Learning Real Space Microstructure Characteristics from Scattering Data

Using tools from morphological image analysis, we characterise spinodal decomposition microstructures by their Minkowski functionals, and search for a correlation between them and data from scattering experiments. To do this,...

Complex of morphological analysis of images for side-scan sonar on basis of Griffon hardware platform

The article describes the developed computational complex of morphological image analysis for interferometric side-scan sonars. Examples of modeling the operation of the side-scan sonar in cases where the structure of bottom sediments is not known in advance are given. The results of the analysis of experimental data are considered. A description is given of the structure for constructing algorithms for morphological image analysis used in the operation of the complex. Recommendations are given on the effective use of the computing capabilities of the Griffon hardware platform for organizing parallel-conveyor data processing of side-scan interference sonars in real time. Hardware and software modeling has been performed to assess the most important performance characteristics of a computing complex and determine the optimal organization of data processing. The obtained results of processing sonar images streams showed that the use of the Griffon hardware platform provides advantages in terms of organizing the computing process, including parallel operation of different sections of the computing pipeline, reducing the load on the transport bus and pipeline delay, as well as reducing the load and freeing up CPU resources for morphological processing.

Automated Identification of Crop Tree Crowns from UAV Multispectral Imagery by Means of Morphological Image Analysis

Within the context of precision agriculture, goods insurance, public subsidies, fire damage assessment, etc., accurate knowledge about the plant population in crops represents valuable information. In this regard, the use of Unmanned Aerial Vehicles (UAVs) has proliferated as an alternative to traditional plant counting methods, which are laborious, time demanding and prone to human error. Hence, a methodology for the automated detection, geolocation and counting of crop trees in intensive cultivation orchards from high resolution multispectral images, acquired by UAV-based aerial imaging, is proposed. After image acquisition, the captures are processed by means of photogrammetry to yield a 3D point cloud-based representation of the study plot. To exploit the elevation information contained in it and eventually identify the plants, the cloud is deterministically interpolated, and subsequently transformed into a greyscale image. This image is processed, by using mathematical morphology techniques, in such a way that the absolute height of the trees with respect to their local surroundings is exploited to segment the tree pixel-regions, by global statistical thresholding binarization. This approach makes the segmentation process robust against surfaces with elevation variations of any magnitude, or to possible distracting artefacts with heights lower than expected. Finally, the segmented image is analysed by means of an ad-hoc moment representation-based algorithm to estimate the location of the trees. The methodology was tested in an intensive olive orchard of 17.5 ha, with a population of 3919 trees. Because of the plot’s plant density and tree spacing pattern, typical of intensive plantations, many occurrences of intra-row tree aggregations were observed, increasing the complexity of the scenario under study. Notwithstanding, it was achieved a precision of 99.92%, a sensibility of 99.67% and an F-score of 99.75%, thus correctly identifying and geolocating 3906 plants. The generated 3D point cloud reported root-mean square errors (RMSE) in the X, Y and Z directions of 0.73 m, 0.39 m and 1.20 m, respectively. These results support the viability and robustness of this methodology as a phenotyping solution for the automated plant counting and geolocation in olive orchards.