Texture-based estimation of physical characteristics of sand grains

The common occurrence and transportability of quartz sand grains make them useful for forensic analysis, providing that grains can be accurately and consistently designated into prespecified types. Recent advances in the analysis of surface texture features found in scanning electron microscopy images of such grains have advanced this process. However, this requires expert knowledge that is not only time intensive, but also rare, meaning that automation is a highly attractive prospect if it were possible to achieve good levels of performance. Basic Image Feature Columns (BIF Columns), which use local symmetry type to produce a highly invariant yet distinctive encoding, have shown leading performance in standard texture recognition tasks used in computer vision. However, the system has not previously been tested on a real world problem. Here we demonstrate that the BIF Column system offers a simple yet effective solution to grain classification using surface texture. In a two class problem, where human level performance is expected to be perfect, the system classifies all but one grain from a sample of 88 correctly. In a harder task, where expert human performance is expected to be significantly less than perfect, our system achieves a correct classification rate of over 80%, with clear indications that performance can be improved if a larger dataset were available. Furthermore, very little tuning or adaptation has been necessary to achieve these results giving cause for optimism in the general applicability of this system to other texture classification problems in forensic analysis

Modeling of evolving textures using granulometries

This chapter describes a statistical approach to classification of dynamic texture images, called parallel evolution functions (PEFs). Traditional classification methods predict texture class membership using comparisons with a finite set of predefined texture classes and identify the closest class. However, where texture images arise from a dynamic texture evolving over time, estimation of a time state in a continuous evolutionary process is required instead. The PEF approach does this using regression modeling techniques to predict time state. It is a flexible approach which may be based on any suitable image features. Many textures are well suited to a morphological analysis and the PEF approach uses image texture features derived from a granulometric analysis of the image. The method is illustrated using both simulated images of Boolean processes and real images of corrosion. The PEF approach has particular advantages for training sets containing limited numbers of observations, which is the case in many real world industrial inspection scenarios and for which other methods can fail or perform badly. [41] G.W. Horgan, Mathematical morphology for analysing soil structure from images, European Journal of Soil Science, vol. 49, pp. 161–173, 1998. [42] G.W. Horgan, C.A. Reid and C.A. Glasbey, Biological image processing and enhancement, Image Processing and Analysis, A Practical Approach, R. Baldock and J. Graham, eds., Oxford University Press, Oxford, UK, pp. 37–67, 2000. [43] B.B. Hubbard, The World According to Wavelets: The Story of a Mathematical Technique in the Making, A.K. Peters Ltd., Wellesley, MA, 1995. [44] H. Iversen and T. Lonnestad. An evaluation of stochastic models for analysis and synthesis of gray-scale texture, Pattern Recognition Letters, vol. 15, pp. 575–585, 1994. [45] A.K. Jain and F. Farrokhnia, Unsupervised texture segmentation using Gabor filters, Pattern Recognition, vol. 24(12), pp. 1167–1186, 1991. [46] T. Jossang and F. Feder, The fractal characterization of rough surfaces, Physica Scripta, vol. T44, pp. 9–14, 1992. [47] A.K. Katsaggelos and T. Chun-Jen, Iterative image restoration, Handbook of Image and Video Processing, A. Bovik, ed., Academic Press, London, pp. 208–209, 2000. [48] M. K¨oppen, C.H. Nowack and G. R¨osel, Pareto-morphology for color image processing, Proceedings of SCIA99, 11th Scandinavian Conference on Image Analysis 1, Kangerlussuaq, Greenland, pp. 195–202, 1999. [49] S. Krishnamachari and R. Chellappa, Multiresolution Gauss-Markov random field models for texture segmentation, IEEE Transactions on Image Processing, vol. 6(2), pp. 251–267, 1997. [50] T. Kurita and N. Otsu, Texture classification by higher order local autocorrelation features, Proceedings of ACCV93, Asian Conference on Computer Vision, Osaka, pp. 175–178, 1993. [51] S.T. Kyvelidis, L. Lykouropoulos and N. Kouloumbi, Digital system for detecting, classifying, and fast retrieving corrosion generated defects, Journal of Coatings Technology, vol. 73(915), pp. 67–73, 2001. [52] Y. Liu, T. Zhao and J. Zhang, Learning multispectral texture features for cervical cancer detection, Proceedings of 2002 IEEE International Symposium on Biomedical Imaging: Macro to Nano, pp. 169–172, 2002. [53] G. McGunnigle and M.J. Chantler, Modeling deposition of surface texture, Electronics Letters, vol. 37(12), pp. 749–750, 2001. [54] J. McKenzie, S. Marshall, A.J. Gray and E.R. Dougherty, Morphological texture analysis using the texture evolution function, International Journal of Pattern Recognition and Artificial Intelligence, vol. 17(2), pp. 167–185, 2003. [55] J. McKenzie, Classification of dynamically evolving textures using evolution functions, Ph.D. Thesis, University of Strathclyde, UK, 2004. [56] S.G. Mallat, Multiresolution approximations and wavelet orthonormal bases of L2(R), Transactions of the American Mathematical Society, vol. 315, pp. 69–87, 1989. [57] S.G. Mallat, A theory for multiresolution signal decomposition: the wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, pp. 674–693, 1989. [58] B.S. Manjunath and W.Y. Ma, Texture features for browsing and retrieval of image data, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, pp. 837–842, 1996. [59] B.S. Manjunath, G.M. Haley and W.Y. Ma, Multiband techniques for texture classification and segmentation, Handbook of Image and Video Processing, A. Bovik, ed., Academic Press, London, pp. 367–381, 2000. [60] G. Matheron, Random Sets and Integral Geometry, Wiley Series in Probability and Mathematical Statistics, John Wiley and Sons, New York, 1975

On the stabilizing influence of silt on sand beds

In marine environments, sediments from different sources are stirred and dispersed, generating beds that are composed of mixed and layered sediments of differing grain sizes. Traditional engineering formulations used to predict erosion thresholds are however, generally for unimodal sediment distributions, and so may be inadequate for commonly occurring coastal sediments. We tested the transport behavior of deposited and mixed sediment beds consisting of a simplified two-grain fraction (silt (D50  =  55 µm) and sand (D50 =  300 µm)) in a laboratory-based annular flume with the objective of investigating the parameters controlling the stability of a sediment bed. To mimic recent deposition of particles following large storm events and the longer-term result of the incorporation of fines in coarse sediment, we designed two suites of experiments: (1) “the layering experiment”: in which a sandy bed was covered by a thin layer of silt of varying thickness (0.2–3 mm; 0.5–3.7 wt %, dry weight in a layer 10 cm deep); and (2) “the mixing experiment” where the bed was composed of sand homogeneously mixed with small amounts of silt (0.07–0.7 wt %, dry weight). To initiate erosion and to detect a possible stabilizing effect in both settings, we increased the flow speeds in increments up to 0.30 m/s. Results showed that the sediment bed (or the underlying sand bed in the case of the layering experiment) stabilized with increasing silt composition. The increasing sediment stability was defined by a shift of the initial threshold conditions towards higher flow speeds, combined with, in the case of the mixed bed, decreasing erosion rates. Our results show that even extremely low concentrations of silt play a stabilizing role (1.4% silt (wt %) on a layered sediment bed of 10 cm thickness). In the case of a mixed sediment bed, 0.18% silt (wt %, in a sample of 10 cm depth) stabilized the bed. Both cases show that the depositional history of the sediment fractions can change the erosion characteristics of the seabed. These observations are summarized in a conceptual model that suggests that, in addition to the effect on surface roughness, silt stabilizes the sand bed by pore-space plugging and reducing the inflow in the bed, and hence increases the bed stability. Measurements of hydraulic conductivity on similar bed assemblages qualitatively supported this conclusion by showing that silt could decrease the permeability by up to 22% in the case of a layered bed and by up to 70% in the case of a mixed bed

Data assimilation of in situ soil moisture measurements in hydrological models: first annual doctoral progress report, work plan and achievements

Water scarcity and the presence of water of good quality is a serious public concern since it determines the availability of water to society. Water scarcity especially in arid climates and due to extreme droughts related to climate change drive water use technologies such as irrigation to become more efficient and sustainable. Plant root water and nutrient uptake is one of the most important processes in subsurface unsaturated flow and transport modeling, as root uptake controls actual plant evapotranspiration, water recharge and nutrient leaching to the groundwater, and exerts a major influence on predictions of global climate models. To improve irrigation strategies, water flow needs to be accurately described using advanced monitoring and modeling. Our study focuses on the assimilation of hydrological data in hydrological models that predict water flow and solute (pollutants and salts) transport and water redistribution in agricultural soils under irrigation. Field plots of a potato farmer in a sandy region in Belgium were instrumented to continuously monitor soil moisture and water potential before, during and after irrigation in dry summer periods. The aim is to optimize the irrigation process by assimilating online sensor field data into process based models. Over the past year, we demonstrated the calibration and optimization of the Hydrus 1D model for an irrigated grassland on sandy soil. Direct and inverse calibration and optimization for both heterogeneous and homogeneous conceptualizations was applied. Results show that Hydrus 1D closely simulated soil water content at five depths as compared to water content measurements from soil moisture probes, by stepwise calibration and local sensivity analysis and optimization the Ks, n and α value in the calibration and optimization analysis. The errors of the model, expressed by deviations between observed and modeled soil water content were, however, different for each individual depth. The smallest differences between the observed value and soil-water content were attained when using an automated inverse optimization method. The choice of the initial parameter value can be optimized using a stepwise approach. Our results show that statistical evaluation coefficients (R2, Ce and RMSE) are suitable benchmarks to evaluate the performance of the model in reproducing the data. The degree of water stress simulated with Hydrus 1D suggested to increase irrigation at least one time, i.e. at the beginning of the simulation period and further distribute the amount of irrigation during the growing season, instead of using a huge amount of irrigation later in the season. In the next year, we will further look for to the best method (using soft data and methods for instance PTFs, EMI, Penetrometer) to derive and predict the spatial variability of soil hydraulic properties (saturated hydraulic conductivity) of the soil and link to crop yield at the field scale. Linear and non-linear pedotransfer functions (PTFs) have been assessed to predict penetrometer resistance of soils from their water status (matric potential, ψ and degree of saturation, S) and bulk density, ρb, and some other soil properties such as sand content, Ks etc. The geophysical EMI (electromagnetic induction) technique provides a versatile and robust field instrument for determining apparent soil electrical conductivity (ECa). ECa, a quick and reliable measurement, is one of ancillary properties (secondary information) of soil, can improve the spatial and temporal estimation of soil characteristics e.g., salinity, water content, texture, prosity and bulk density at different scales and depths. According to previous literature on penetrometer measurements, we determined the effective stress and used some models to find the relationships between soil properties, especially Ks, and penetrometer resistance as one of the prediction methods for Ks. The initial results obtained in the first yearshowed that a new data set would be necessary to validate the results of this part. In the third year, quasi 3D-modelling of water flow at the field scale will be conducted. In this modeling set -up, the field will be modeled as a collection of 1D-columns representing the different field conditions (combination of soil properties, groundwater depth, root zone depth). The measured soil properties are extrapolated over the entire field by linking them to the available spatially distributed data (such as the EMI-images). The data set of predicted Ks and other soil properties for the whole field constructed in the previous steps will be used for parameterising the model. Sensitivity analysis ‘SA’ is essential to the model optimization or parametrization process. To avoid overparameterization, the use of global sensitivity analysis (SA) will be investigated. In order to include multiple objectives (irrigation management parameters, costs, …) in the parameter optimization strategy, multi-objective techniques such as AMALGAM have been introduced. We will investigate multi-objective strategies in the irrigation optimization

Assessing the potential for reopening a building stone quarry : Newbigging Sandstone Quarry, Fife

Newbigging Sandstone Quarry in Fife is one of a number of former quarries in the Burntisland- Aberdour district which exploited the pale-coloured Grange Sandstone from Lower Carboniferous rocks. The quarry supplied building stone from the late 19th century, working intermittently from 1914 until closure in 1937, and again when reopened in the 1970s to the 1990s. The stone was primarily used locally and to supply the nearby markets in the Scottish Central Belt. Historical evidence indicates that prior to sandstone extraction, the area was dominated by largescale quarrying and mining of limestone, and substantial sandstone quarrying is likely to have begun after the arrival of the main railway line in 1890. It is probable that removal of the sandstone was directly associated with limestone exploitation, and that the quarried sandstone was effectively a by-product of limestone production. Sandstone extraction was probably viable due to the existing limestone quarry infrastructure (workforce, equipment, transportation) and the high demand for building stone in Central Scotland in the late 19th century. The geology within Newbigging Sandstone Quarry is dominated by thick-bedded uniform sandstone with a wide joint spacing, well-suited for obtaining large blocks. However, a mudstone (shale) band is likely to be present within a few metres of the principal (north) face of the quarry, around which the sandstone bed thickness and quality is likely to decrease. The mudstone bed forms a plane sloping at a shallow angle to the north, so that expansion of the quarry in this direction is likely to encounter a considerable volume of poor quality stone. Additionally, an east-west trending fault is present approximately 100 metres north of the quarry face, which is also likely to be associated with poor quality (fractured) stone

Superhydrophobic surfaces: a model approach to predict contact angle and surface energy of soil particles

Wettability of soil affects a wide variety of processes including infiltration, preferential flow and surface runoff. The problem of determining contact angles and surface energy of powders, such as soil particles, remains unsolved. So far, several theories and approaches have been proposed, but formulation of surface and interfacial free energy, as regards its components, is still a very debatable issue. In the present study, the general problem of the interpretation of contact angles and surface free energy on chemically heterogeneous and rough soil particle surfaces is evaluated by a reformulation of the Cassie-Baxter equation, assuming that the particles are attached on to a plane and rigid surface. Compared with common approaches, our model considers a roughness factor that depends on the Young’s Law contact angle determined by the surface chemistry. Results of the model are discussed and compared with independent contact angle measurements using the Sessile Drop and the Wilhelmy Plate methods. Based on contact angle data, the critical surface tension of the grains were determined by the method proposed by Zisman. Experiments were made with glass beads and three soil materials ranging from sand to clay. Soil particles were coated with different loadings of dichlorodimethylsilane (DCDMS) to vary the wettability. Varying the solid surface tension using DCDMS treatments provided pure water-wetting behaviours ranging from wettable to extremely hydrophobic, with contact angles > 150°. Results showed that the critical surface energy measured on grains with the highest DCDMS loadings was similar to the surface energy measured independently on ideal DCDMS-coated smooth glass plates, except for the clay soil. Contact angles measured on plane surfaces were related to contact angles measured on rough grain surfaces using the new model based on the combined Cassie-Baxter Wenzel equation, which takes into account the particle packing density on the sample surface

An investigation into reach scale estimates of sub-pixel fluvial grain size from hyperspatial imagery.

Grain size data for gravel bed rivers is important in a wide variety of contexts; providing crucial information to guide the development of flood defences, and maintaining navi-gability, biodiversity and ecological integrity within large gravel bed rivers. Advances in remote sensing technologies have seen an increase in the acquisition of hyperspatial imagery (imagery with a spatial resolution of < 10 cm), and advances in computational power have complemented this data acquisition allowing for the application of complex image processing techniques. An improved methodology is presented for extracting reach scale grain size information. Of particular note is the ability to generate estimates of sub-pixel surface sand content, as well as sub-pixel grain size distributions. The methodology was applied to Queens Bar, NBar, Calamity Bar and Harrison Bar within the gravel reach of the Fraser River (British Columbia, Canada). Hyperspatial imagery was acquired at 3 cm resolution, along with independent surface grain size information. Surface sand estimates were calculated through a first order standard deviation textural layer; calibrations revealed an inequality based relationship be-tween texture and sand content, allowing for the production of binary maps of surface sand content with an approximate accuracy of 70%. Calibrations were calculated for 7 grain size percentiles for the gravel fraction of the grain size distribution ( > 2 mm); D5, D16, D35, D50, D65, D84 and D95 were achieved, following a wide ranging parameter investigation. A combination of first order standard deviation along with several second order Grey Level Co-occurrence Matrix textural parameters (entropy, contrast and cor-relation) calibrated to grain size using multiple linear regression. The best performing calibrations were found for smaller and intermediate percentiles; cross validated mean square error (%) at 0.61, 3.55, 9.58 and 16.25 for D5, D16, D35, and D50 respectively. Calibrations began to break down for the largest percentiles; cross validated mean square error (%) at 26.43 and 44.99 for D84 and D95. The breakdown of calibrations for larger percentiles is attributed to the ‘pixel averaging eect’; for smaller percentiles a larger population of grains were averaged into one pixel, thus variance across multiple pixels is low, whereas for the larger percentiles the grain size approaches the spatial resolution of the pixels, therefore a smaller population of grains makes up one pixel and introduces in-creased variance across multiple pixels. Overall, this new methodology presents a means for extracting sub-pixel grain size information from hyperspatial imagery, with higher ac-curacies for the smaller percentiles than previously published. This allows for the rapid acquisition of a large amount of grain size information without the need for time intensive field techniques