Texture analysis of polymer modified bitumen images

International Journal of Advanced Manufacturing Technology, 2011

Natural manufactured materials are highly heterogeneous. The remarkable variability exhibited from one sample to another makes it impossible to find two samples with the same morphological distribution in their texture. This fact makes automatic quality measurement of the properties of naturally textured objects a desirable though challenging problem in industrial applications where costs are paramount. Cork, as a natural and heterogeneous material with fully seven quality classes, defies easy automatic quality classification. This paper describes an improved computer vision system for measuring the quality of industrial cork samples by incorporating techniques based on the biological configuration of human vision and human reasoning, such as Gabor filtering techniques and neurofuzzy computing. These techniques have provided important texture detection improvements over previous results. Neurofuzzy classification system performance has proved to be better than results obtained with other classification algorithms. The proposed neurofuzzy classifier includes in its feature space three wavelet-based texture quality features. The results obtained from the final proposed system showed an error rate of only 4.85% that is lower than previous similar systems designed for this application. The system proposed in this paper has the potential to reduce costs, time, and current conflicts in the cork industry.

Technical University of Lodz, Institute of …, 1998

Methods for digital-image texture analysis are reviewed based on available literature and research work either carried out or supervised by the authors. The review has been prepared on request of Dr Richard Lerski, Chairman of the Management Committee of the COST B11 action "Quantitation of Magnetic Resonance Image Texture".

Journal of Applied Polymer Science, 2011

This article presents a laboratory evaluation of conventional, fundamental, rheological, and morphological characteristics of styrene-butadiene-styrene (SBS) and ethylene vinyl acetate (EVA) polymer modified bitumens. Polymer modified bitumen (PMB) samples have been produced by mixing a 50/70 penetration grade unmodified (base) bitumen with SBS and EVA copolymer at different polymer contents. The fundamental viscoelastic properties of the PMBs were determined using dynamic (oscillatory) mechanical analysis and presented in the form of temperature and frequency-dependent rheological parameters. The morphology of the samples as well as the percent area distribution of polymers throughout the base bitumen have been characterized and determined by means of fluorescent light optic microscopy and Qwin Plus image analysis software, respectively. The results indicated that polymer modification improved the conventional and rheological properties of the base bitumen. It was also concluded that the temperature and frequency had a significant effect on complex modulus of PMBs. The behavior of EVA and SBS PMBs had exhibited quite difference at 50 C. Moreover, it was found out that at low polymer contents, the samples revealed the existence of dispersed polymer particles in a continuous bitumen phase, whereas at high polymer contents a continuous polymer phase has been observed. V

2015

The infrared spectroscopy is suitable for the assessment of chemical compounds based on the plots of light intensity as a function of wavelength within the infrared range of electromagnetic spectrum. It enables the fingerprinting of substances, as well as detection and quantitative analysis of admixtures and additives present in a given substance. In case of bitumen binders the FTIR method may be adopted for the assessment of presence of ageing products. It effectively identifies impurities and discloses the presence of additives, e.g. adhesion agents. The FTIR device is also used for checking the presence of polymers in bitumen and even for determining the modification degree through an appropriate quantitative assessment. The present paper outlines practical applications of FTIR to testing bitumens, which include assessment of several types of binders prepared under control conditions, i.e. with a known polymer concentration and ageing degree. For comparison purposes, some commerc...

Bonfring

In India, the methods for rheological characterization of bituminous binders are inadequate to characterize the bitumen. Hence a complete rheological study and characterization of bitumen using dynamic shear rheometer would be helpful. Also there is limited insight about the chemistry of modified bitumen. Modification of asphalt binders can serve several purposes. It can increase the overall performance of a binder by widening the range between the binder?s high and low-temperature grades. In this paper VG30 bitumen and two polymers: Styrene Butadiene Styrene (SBS) block copolymer and Ethylene Vinyl Acetate (EVA) polymers were used as modifiers. SBS copolymers derive their strength and elasticity from physical and cross linking of molecules into three dimensional networks. Increasing the SBS content results in increased polymer swelling, which in turn produces increase in asphaltenes and reduction in maltene content resulting in harder and viscous matrix whereas EVA modifies bitumen by forming tough, three dimensional, rigid network to resist deformation. The change in physical properties such as penetration, softening point, penetration index and mass loss were studied and compared before and after modification. Rheological parameters such as complex modulus and phase angle before and after modification were studied by using Dynamic Shear Rheometer. The results indicate that polymer modification leads to decrease in penetration value, and increase in softening point and rutting parameter (G*/Sinδ). Modifiers used for bitumen modification are normally polymeric materials which have different structures such as atactic, isotactic and syndiotactic. These structures give characteristic features in spectroscopy analysis. Thus, Infra Red Spectroscopy techniques was employed in identifying the structure and functional groups present in polymeric materials.

Hybrid texture identification method, 2021

The importance of the modeling mode in systems of computer visual pattern recognition is shown. The purpose of the mode is to determine the types of textures that are present on the images processed in intelligent diagnostic systems. Images processed in technical diagnostic systems contain texture regions, which can be represented by different types of textures-spectral, statistical and spectral-statistical. Texture identification methods, such as, statistical, spectral, expert, multifractal, which are used to identify and analyze texture images, have been analyzed. To determine texture regions on images that are of a combined spectral-statistical nature, a hybrid texture identification method has been developed which makes it possible to take into account the local characteristics of the texture based on multifractal indicators characterizing the non-stationarity and impulsite of the data and the sign of the spectral texture. The stages of the developed hybrid texture identification method are: preprocessing; formation of the primary features vector; formation of the secondary features vector. The formation of the primary features vector is performed for the selected rectangular fragment of the image, in which the multifractal features and the spectral texture feature are calculated. To reduce the feature space at the stage of formation of the secondary identification vector, the principal component method was used. An experimental study of the developed hybrid texture identification method textures on model images of spectral, statistical, spectralstatistical textures has been carried out. The results of the study showed that the developed method made it possible to increase the probability of correct determination of the region of the combined spectral-statistical texture. The developed identification method was tested on images from Brodatz album of textures and images of wear zones of cutting tools, which are processed in intelligent systems of technical diagnostics. The probability of correctly identifying areas of spectral-statistical texture in the images of wear zones of cutting tools averaged 0.9, which is sufficient for the needs of practice.

It is very difficult to identify an object just considering its color and shape. Texture is one of the important features which helps in identifying the objects that appear similar on the image but in reality they are different objects on the ground. It is commonly accepted that texture analysis plays a very significant role in object classification and outlining the important regions of a given gray level image. Image analysis process requires the knowledge of the characteristics of the pixels in an image. In this paper, five texture analysis methods namely; Structural, Statistical, Model based, Transform based and Soft computing have been described. Moreover, benefits and shortcoming of the different methods are also discussed.

Road Materials and Pavement Design, 2008

The morphology of bitumen-SBS blends is studied using Epi-Fluorescence microscopy. Morphology is defined as the partition of polymer-rich and bitumen-rich phases. Existing sample preparation methods for microscopy on bitumen are compared and the obtained morphologies are shown. In a second part, differences in morphology are related to differences in rheological behavior.

Sensor Review, 2012

Purpose-The purpose of this paper is to review and provide a detailed performance evaluation of a number of texture descriptors that analyse texture at micro-level such as local binary patterns (LBP) and a number of standard filtering techniques that sample the texture information using either a bank of isotropic filters or Gabor filters. Design/methodology/approach-The experimental tests were conducted on standard databases where the classification results are obtained for single and multiple texture orientations. The authors also analysed the performance of standard filtering texture analysis techniques (such as those based of LM and MR8 filter banks) when applied to the classification of texture images contained in standard Outex and Brodatz databases. Findings-The most important finding resulting from this study is that although the LBP/C and the multi-channel Gabor filtering techniques approach texture analysis from a different theoretical perspective, in this paper the authors have experimentally demonstrated that they share some common properties in regard to the way they sample the macro and micro properties of the texture. Practical implications-Texture is a fundamental property of digital images and the development of robust image descriptors plays a crucial role in the process of image segmentation and scene understanding. Originality/value-This paper contrast, from a practical and theoretical standpoint, the LBP and representative multi-channel texture analysis approaches and a substantial number of experimental results were provided to evaluate their performance when applied to standard texture databases.

Faculty of Built Environment and Engineering, 2011

Texture analysis and textural cues have been applied for image classification, segmentation and pattern recognition. Dominant texture descriptors include directionality, coarseness, line-likeness etc. In this dissertation a class of textures known as particulate textures are defined, which are predominantly coarse or blob-like. The set of features that characterise particulate textures are different from those that characterise classical textures. These features are micro-texture, macro-texture, size, shape and compaction. Classical texture analysis techniques do not adequately capture particulate texture features. This gap is identified and new methods for analysing particulate textures are proposed. The levels of complexity in particulate textures are also presented ranging from the simplest images where blob-like particles are easily isolated from their background to the more complex images where the particles and the background are not easily separable or the particles are occluded. Simple particulate images can be analysed for particle shapes and sizes. Complex particulate texture images, on the other hand, often permit only the estimation of particle dimensions. Real life applications of particulate textures are reviewed, including applications to sedimentology, granulometry and road surface texture analysis. A new framework for computation of particulate shape is proposed. A granulometric approach for particle size estimation based on edge detection is developed which can be adapted to the gray level of the images by varying its parameters. This study binds visual texture analysis and road surface macrotexture in a theoretical iii framework, thus making it possible to apply monocular imaging techniques to road surface texture analysis. Results from the application of the developed algorithm to road surface macro-texture, are compared with results based on Fourier spectra, the autocorrelation function and wavelet decomposition, indicating the superior performance of the proposed technique. The influence of image acquisition conditions such as illumination and camera angle on the results was systematically analysed. Experimental data was collected from over 5km of road in Brisbane and the estimated coarseness along the road was compared with laser profilometer measurements. Coefficient of determination R 2 exceeding 0.9 was obtained when correlating the proposed imaging technique with the state of the art Sensor Measured Texture Depth (SMTD) obtained using laser profilometers.